Reviewerly/exHarmony huggingface dataset:
Evidence-based guidelines for protective actions and earthquake early warning systems

Earthquake early warning (EEW) systems are becoming increasingly available or are in development throughout the world. As these systems develop, it is important to provide evidence-based recommendations for protective action so people know how to protect themselves when they receive an alert. However, many factors need to be considered when developing contextually relevant and appropriate recommendations. We have reviewed earthquake injury reports, protective action and communication theories, and behavioral research to determine what factors can guide inquiry and decision making when developing protective action guidelines. Factors that emerge from relevant literature include: (1) social, cultural, and environmental context, such as which people are present, what their social roles are, and in what type of building they are located when an earthquake happens, (2) demographic and experiential variables, such as gender and age as well as previous history with earthquakes; and (3) magnitude and intensity that influence the duration and impacts of the earthquake itself. Although we examine data from around the world, we focus largely on evidence-based recommendations for the U.S. system, ShakeAlert, because it provides a timely case study for understanding how people receive and respond to EEW messages. In addition to synthesizing relevant literature, we recommend pathways forward for this interdisciplinary research community that explores EEW and its application around the world. Consistency in collecting and reporting injury data globally may assist in aligning this fragmented literature to develop a richer understanding of how demographic, cultural, seismic, engineering, and technological issues can be addressed to reduce human suffering due to earthquakes.
Bringing Light Into the Dark: A Large-Scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework

The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult. To assess the reproducibility of previously published results, we re-implemented and evaluated 21 models in the PyKEEN software package. In this paper, we outline which results could be reproduced with their reported hyper-parameters, which could only be reproduced with alternate hyper-parameters, and which could not be reproduced at all, as well as provide insight as to why this might be the case. We then performed a large-scale benchmarking on four datasets with several thousands of experiments and 24,804 GPU hours of computation time. We present insights gained as to best practices, best configurations for each model, and where improvements could be made over previously published best configurations. Our results highlight that the combination of model architecture, training approach, loss function, and the explicit modeling of inverse relations is crucial for a model's performance and is not only determined by its architecture. We provide evidence that several architectures can obtain results competitive to the state of the art when configured carefully. We have made all code, experimental configurations, results, and analyses available at https://github.com/pykeen/pykeen and https://github.com/pykeen/benchmarking.
Intelligent designs in nanophotonics: from optimization towards inverse creation

Abstract Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years. Inverse design of functional nanostructures is at the heart of this topic, in which artificial intelligence (AI) furnishes various optimization toolboxes to speed up prototyping of photonic layouts with enhanced performance. In this review, we offer a systemic view on recent advancements in nanophotonic components designed by intelligence algorithms, manifesting a development trend from performance optimizations towards inverse creations of novel designs. To illustrate interplays between two fields, AI and photonics, we take meta-atom spectral manipulation as a case study to introduce algorithm operational principles, and subsequently review their manifold usages among a set of popular meta-elements. As arranged from levels of individual optimized piece to practical system, we discuss algorithm-assisted nanophotonic designs to examine their mutual benefits. We further comment on a set of open questions including reasonable applications of advanced algorithms, expensive data issue, and algorithm benchmarking, etc. Overall, we envision mounting photonic-targeted methodologies to substantially push forward functional artificial meta-devices to profit both fields.
A Novel Secure Blockchain Framework for Accessing Electronic Health Records Using Multiple Certificate Authority

Blockchain is a promising technology in the context of digital healthcare systems, but there are issues related to the control of accessing the electronic health records. In this paper, we propose a novel framework based on blockchain and multiple certificate authority that implement smart contracts and access health records securely. Our proposed solution provides the facilities of flexible policies to update a record or invoke the policy such that a patient has complete authority. A novel approach towards multiple certificate’s authority (CA) is introduced in the design through our proposed framework. Our proposed policies and methods overcome the shortcoming and security breaches faced by single certificate authority. Our proposed scheme provides a flexible access control mechanism for securing electronic health records as compared to the existing benchmark models. Moreover, our proposed method provides a re-enrolment facility in the case of a user lost enrolment.
Blockchain-based privacy preserving e-health system for healthcare data in cloud

Cloud-assisted electronic health (e-health) system has played an important role in the management of healthcare data, which can provide efficient and convenient storage services for the patients to store their electronic health records (EHRs). However, there are the security challenges in the existing e-health system, where the malicious doctors may collude with cloud storage service provider (CSP) to tamper with the patients’ EHRs, or directly reveal the content of EHRs to other adversaries for profit. In this paper, we propose a blockchain-based privacy preserving e-health system to ensure the security and confidentiality of the patients’ EHRs. In the system, we introduce the pairing-based cryptography to generate the tamper-proof records of the EHRs and allow the patients to integrate them into the transactions deployed on the blockchain, which can make the patients’ EHRs verifiable and protect them from illegal modification. Moreover, we will design secure payment protocols by performing blockchain-based smart contracts, which can enable the patients and hospital to reliably pay for the diagnostic and storage service, respectively. Security analysis and the performance evaluation demonstrate that our proposed scheme is secure and efficient with a limited computation overhead.
The Effects of Cryptocurrency Trading Websites on Airlines’ Advertisement Campaigns

In future years, airline companies will be leaning more and more towards cryptocurrencies to implement their digital marketing strategies as leaders seek to gain an understanding of the factors affecting airlines’ visibility parameters. Cryptocurrency investment websites are currently experiencing rising demand, making them an appropriate site for paid advertisements. The above factors suggest the need for airlines to harvest cryptocurrency investment and platform users in their favour. To this end, it can be beneficial for airlines’ web promotions to link certain web analytics metrics to cryptocurrency trading site metrics. For research purposes, web analytics data were monitored and gathered for 2 consecutive years from 10 globally leading cryptocurrency trading companies and 10 airline websites. A three-stage model was adopted by the authors. In the first stage, statistical analysis was implemented using cryptocurrency and airline metrics, followed by fuzzy cognitive mapping and agent-based modelling stages. The findings of the study indicate that engagement with cryptocurrency trading websites has a positive impact on airline websites’ global ranking and visibility parameters. The outcomes of this research provide noteworthy digital marketing strategies which can be addressed by airline companies to increase their website visitors and optimise visibility parameters with the assistance of cryptocurrency trading websites.
EAPT: Efficient Attention Pyramid Transformer for Image Processing

Recent transformer-based models, especially patch-based methods, have shown huge potentiality in vision tasks. However, the split fixed-size patches divide the input features into the same size patches, which ignores the fact that vision elements are often various and thus may destroy the semantic information. Also, the vanilla patch-based transformer cannot guarantee the information communication between patches, which will prevent the extraction of attention information with a global view. To circumvent those problems, we propose an Efficient Attention Pyramid Transformer (EAPT). Specifically, we first propose the Deformable Attention, which learns an offset for each position in patches. Thus, even with split fixed-size patches, our method can still obtain non-fixed attention information that can cover various vision elements. Then, we design the Encode-Decode Communication module (En-DeC module), which can obtain communication information among all patches to get more complete global attention information. Finally, we propose a position encoding specifically for vision transformers, which can be used for patches of any dimension and any length. Extensive experiments on the vision tasks of image classification, object detection, and semantic segmentation demonstrate the effectiveness of our proposed model. Furthermore, we also conduct rigorous ablation studies to evaluate the key components of the proposed structure.
QuickSilver: Efficient and Affordable Zero-Knowledge Proofs for Circuits and Polynomials over Any Field

Zero-knowledge (ZK) proofs with an optimal memory footprint have attracted a lot of attention, because such protocols can easily prove very large computation with a small memory requirement. Such ZK protocol only needs O(M) memory for both parties, where M is the memory required to verify the statement in the clear. In this paper, we propose several new constant-round ZK protocols in this setting, which improve the concrete efficiency and, at the same time, enable sublinear amortized communication for circuits with some notion of relaxed uniformity. In the circuit-based model, where the computation is represented as a circuit over a field, our ZK protocol achieves a communication complexity of 1 field element per non-linear gate for any field size while keeping the computation very cheap. We implemented our protocol, which shows extremely high efficiency and affordability. Compared to the previous best-known implementation, we achieve 6x--7x improvement in computation and 3x--7x improvement in communication. When running on intro-level AWS instances, our protocol only needs one US dollar to prove one trillion AND gates (or 2.5 US dollars for one trillion multiplication gates over a 61-bit field). In the setting where part of the computation can be represented as a set of polynomials with a "degree-separated" format, we can achieve communication sublinear to the polynomial size: the communication only depends on the total number of distinct variables in all the polynomials and the highest degree of all polynomials, independent of the number of multiplications to compute all polynomials. Using the improved ZK protocol, we can prove matrix multiplication with communication proportional to the input size, rather than the number of multiplications. Proving the multiplication of two 1024 x 1024 matrices, our implementation, with one thread and 1 GB of memory, only needs 10 seconds and communicates 25 MB.
FedMask

Recent advancements in deep neural networks (DNN) enabled various mobile deep learning applications. However, it is technically challenging to locally train a DNN model due to limited data on devices like mobile phones. Federated learning (FL) is a distributed machine learning paradigm which allows for model training on decentralized data residing on devices without breaching data privacy. Hence, FL becomes a natural choice for deploying on-device deep learning applications. However, the data residing across devices is intrinsically statistically heterogeneous (i.e., non-IID data distribution) and mobile devices usually have limited communication bandwidth to transfer local updates. Such statistical heterogeneity and communication bandwidth limit are two major bottlenecks that hinder applying FL in practice. In addition, considering mobile devices usually have limited computational resources, improving computation efficiency of training and running DNNs is critical to developing on-device deep learning applications. In this paper, we present FedMask - a communication and computation efficient FL framework. By applying FedMask, each device can learn a personalized and structured sparse DNN, which can run efficiently on devices. To achieve this, each device learns a sparse binary mask (i.e., 1 bit per network parameter) while keeping the parameters of each local model unchanged; only these binary masks will be communicated between the server and the devices. Instead of learning a shared global model in classic FL, each device obtains a personalized and structured sparse model that is composed by applying the learned binary mask to the fixed parameters of the local model. Our experiments show that compared with status quo approaches, FedMask improves the inference accuracy by 28.47% and reduces the communication cost and the computation cost by 34.48X and 2.44X. FedMask also achieves 1.56X inference speedup and reduces the energy consumption by 1.78X.
HWOA: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation

Traditional methods to address color image segmentation work efficiently for bi-level thresholding. However, for multi-level thresholding, traditional methods suffer from time complexity that increases exponentially with the increasing number of threshold levels. To overcome this problem, in this paper, a new approach is proposed to tackle multi-threshold color image segmentation by employing the Otsu method as an objective function. This approach is based on a hybrid of the whale optimization algorithm (WOA) with a novel method called the local minima avoidance method (LMAM), abbreviated as HWOA. LMAM avoids local minima by updating the whale either within the search space of the problem or between two whales selected randomly from the population-based on a certain probability. HWOA is validated on ten color images taken from the Berkeley University Dataset by measuring the objective values, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), features similarity index (FSIM), and CPU time, and compared with a number of the well-known robust meta-heuristic algorithms: the sine–cosine algorithm (SCA), WOA, modified salp swarm algorithm (MSSA), improved marine predators algorithm (IMPA), modified Cuckoo Search (CS) using McCulloch's algorithm (CSMC), and equilibrium optimizer (EO). The experimental results show that HWOA is superior to all the other algorithms in terms of PSNR, FSIM, and objective values, and is competitive in terms of SSIM.
Image encryption based on logistic chaotic systems and deep autoencoder

In this paper, we propose a novel image encryption method based on logistic chaotic systems and deep autoencoder. In the encryption phase, first, the plaintext image is randomly scrambled by a logistic chaotic system. Then, the random scrambled image is encoded by a deep autoencoder to generate the ciphertext image. In order to obtain the ciphertext image with uniform distribution, we incorporated the uniform distribution constraint into the training of the deep autoencoder. The resulting ciphertext image contains high randomness, which is critical for an excellent image encryption algorithm. Histogram analysis, information entropy analysis, key space analysis, key sensitivity analysis, correlation analysis, and ablation experiments show that the proposed encryption algorithm can effectively resist attacks and has excellent encryption performance while providing high security.
A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems

The integration of photovoltaic energy into a grid demands accurate power output forecasting. In this research, an hour ahead prediction of power output is performed on an annual basis over real data period (2016–2019) for three different PV systems based on polycrystalline, monocrystalline, and thin-film technologies. The solar radiation, ambient temperature, module temperature and wind speed are the considered input parameters, while the power output of each PV system is the output parameter. A hybrid deep learning (DL) method (SSA-RNN-LSTM) is proposed for an hour ahead prediction of output power for each PV system. The proposed technique is compared with GA-RNN-LSTM, PSO-RNN-LSTM and RNN-LSTM. The considered forecasting accuracy measurement parameters are RMSE, MSE, MAE and coefficient of determination (R2). The findings elaborate that SSA-RNN-LSTM has shown better forecasting accuracy with the lowest (RMSE and MSE), highest (R2) and highest convergence speed compared to other methods. The proposed model has shown testing (RMSE and MAE) of (19.14% and 21.57%), (15.4% and 10.81%) and (22.9% and 25.2%) lower than RNN-LSTM for polycrystalline, monocrystalline and thin-film PV systems respectively. Furthermore, the proposed model is found more robust in predicting the power output for three different PV systems over four years data period.
KNNOR: An oversampling technique for imbalanced datasets

Predictive performance of Machine Learning (ML) models rely on the quality of data used for training the models. However, if the training data is not balanced among different classes, the performance of ML models deteriorate heavily. Several techniques have been proposed in the literature to add some semblance of balance to the data sets by adding artificial data points. Synthetic Minority Oversampling Technique(SMOTE) and Adaptive Synthetic Sampling(ADASYN) are some of the commonly used techniques to deal with class imbalance. However, these approaches are prone to ‘within class imbalance’ and ‘small disjunct problem’. To overcome these problems, this article proposes an advanced algorithm by studying the compactness and location of the minority class relative to other classes. The proposed technique called K-Nearest Neighbor OveRsampling approach (KNNOR) performs a three step process to identify the critical and safe areas for augmentation and generate synthetic data points of the minority class. The relative density of the entire population is considered while generating artificial points. This enables the proposed KNNOR approach to oversample the minority class more reliably and at the same time stay resilient against noise. The proposed method is compared with the ten top performing contemporary oversamplers by testing the accuracy of classifiers trained on augmented data provided by each oversampler. The experimental results on several common imbalanced datasets show that our method ranks first more consistently than the other state-of-art oversamplers. The proposed method is easy to use and has been made open source as a python library.
Blockchain in the AECO industry: Current status, key topics, and future research agenda

Blockchain has attracted great attention in the architecture, engineering, construction, and operation (AECO) industry. To capture its research trends, this study conducted a comprehensive review of blockchain development in the industry using a mixed approach of bibliometrics and content analyses. Ninety-four papers were filtered from the Scopus and Web of Science databases for this review. Bibliometric analysis revealed annual publication trends, journal distributions, country distributions, high-yielding authors, author cooperation networks, and keyword co-occurrence networks, whereas content analysis identified five key topics of contract management, supply chain management, information management, stakeholder management, and the integration of blockchain with other technologies. Research gaps and future research directions are articulated and explained within the research framework. This paper contributes to the body of knowledge by synthesizing the current state and development areas of blockchain research, which would promote the strategic adoption and use of blockchain to a greater extent in the AECO industry.
Appropriate service robots in exchange and communal relationships

This paper explores which type of service robot (functional vs. social) is evaluated more favorably depending on a firm’s communal or exchange relationship orientation and the underlying processes driving the appropriate match effect between type of service robot and relationship orientation. The results of two scenario-based experiments and one video-based study with respondents who actually experienced service robots reveal that higher customer satisfaction with the service robot emerges when functional (social) service robots are aligned with a firm’s exchange (communal) relationship orientation. Further, the match between social service robot and communal relationship orientation promotes customer satisfaction with the service robot primarily through perceptions of the warmth of the robot. In contrast, higher customer satisfaction with a functional service robot in an exchange relationship orientation results from increased perception of the competence of the robot. This study provides theoretical and practical implications about how to implement service robots in service encounters.
Is cuteness irresistible? The impact of cuteness on customers’ intentions to use AI applications

Artificial intelligence technology has been increasingly applied in the hospitality and tourism industry, and it is a matter of concern how to increase customer adoption of AI applications in service encounters. This study examines how cuteness of AI applications interacts with the type of service task (emotional or knowledge-based) in their influence on customers' willingness to use the application. Hypotheses were tested using four experiments in different interactive contexts. It was found that customers were more willing to adopt AI applications with high perceived cuteness to perform emotional tasks, and social distance played a mediating role; conversely, customers preferred AI applications with low perceived cuteness to perform knowledge-based tasks, and here performance expectancy mediated the relationship between perceived cuteness and customers’ willingness to use the application. The findings advance the theories of customer adoption by revealing and differentiating the “cuteness effect” within different service types.