Reviewerly/exHarmony huggingface dataset:
Augmented Reality in Seismic Risk Management: A Contribution to the Reduction of Non-Structural Damage

To increase seismic resilience is one of the challenges the developers of new technologies face to reduce seismic risk. We set up an augmented reality (AR) exhibition with which users’ curiosity was confronted with the opportunity to have a wealth of information on damaging earthquakes that could be a multimedia add-on to the plain “single-layer exhibit”. AR is an emergent technology developed to “augment” reality through various devices; it combines the real world with virtual items, such as images and videos. Our AR exhibition aims to: (i) show the effects of earthquakes even in cases of moderate magnitude; and (ii) promote preventive actions to reduce non-structural damage. It can be customized for different seismic scenarios. In addition, it offers a holistic approach to communicate problems and solutions—with the cost and degree of ease of execution for each solution—to reduce non-structural damage at home, school, and office. Our AR exhibition can do more than just a plain text or a preconceived video: it can trigger fruitful interaction between the presenters, or even the stand-alone poster, and the public. Such interactivity offers an easy engagement to people of all ages and cultural backgrounds. AR is, indeed, extremely flexible in raising recipients’ interest; moreover, it is an appealing tool for the digital native generations. The positive feedback received led us to conclude that this is an effective way to raise awareness and individual preparedness to seismic risk.
Early detection of earthquake magnitude based on stacked ensemble model

A new machine learning model, named, EEWPEnsembleStack has been developed for predicting the magnitude of the earthquake from a few seconds of recorded ground motion after the arrival of the P phase. The testing and training dataset consists of 2360 and 591 strong-motion records from central Japan recorded by the Kyoshin Network. Eight parameters that are well correlated with the magnitude have been used for training and testing of the model. Feature ablation study using several models shows that a minimum mean absolute error of 0.42 has been obtained for the case when the model has been trained by using all parameters rather than by a single parameter. The model ablation study indicates that among all individually trained single models, the minimum error has been obtained for a Decision Tree regression model. However, the error is minimized when all machine learning models have been together utilized in the EEWPEnsembleStack model for the training purposes. The EEWPEnsembleStack model has been used to predict a 6.3 magnitude earthquake by using its 21 records from various stations that lie within 50 to 150 km epicentral distance. The predicted magnitude from the developed model using weighted magnitude prediction is obtained as 6.4, which is close to the actual magnitude. The comparison of the predicted magnitude of this earthquake from the developed model with that predicted by using popular τc and Pd methods clearly indicates the suitability of the developed machine learning model over other conventional models.
Ensemble of deep learning models with surrogate-based optimization for medical image segmentation

Deep Neural Networks (DNNs) have created a breakthrough in medical image analysis in recent years. Because clinical applications of automated medical analysis are required to be reliable, robust and accurate, it is necessary to devise effective DNNs based models for medical applications. In this paper, we propose an ensemble framework of DNNs for the problem of medical image segmentation with a note that combining multiple models can obtain better results compared to each constituent one. We introduce an effective combining strategy for individual segmentation models based on swarm intelligence, which is a family of optimization algorithms inspired by biological processes. The problem of expensive computational time of the optimizer during the objective function evaluation is relieved by using a surrogate-based method. We train a surrogate on the objective function information of some populations and then use it to predict the objective values of each candidate in the subsequent populations. Experiments run on a number of public datasets indicate that our framework achieves competitive results within reasonable computation time.
Performance of Composite PPSO on Single Objective Bound Constrained Numerical Optimization Problems of CEC 2022

Particle swarm optimization has been extensively noticed for its fast convergence speed with few parameters. However, it would be plagued by the premature convergence only affected by the global particles. In this study, Composite Proactive Particles in Swarm Optimization (Co-PPSO) is proposed. In Co-PPSO, the composite strategy framework is embedded into Proactive Particles in Swarm Optimization (PPSO), that is, three learning strategies are proposed to evaluate their differences and select the most suitable one for each particle. In addition, an elite group is constructed to make the particles jump out of the situation that they are only affected by the global best one in the particle swarm, and further improve the convergence accuracy. CEC2022 competition of single objective bound-constrained numerical optimization is employed to test the effect of 10- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> and 20- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> optimization, and four well-known PSO variants were used for comparison. The experimental results show that the Co-PPSO has certain competitiveness to improve premature convergence.
Evolutionary Automated Machine Learning for Multi-Scale Decomposition and Forecasting of Sensor Time Series

In the paper, we discuss the applicability of automated machine learning for the effective multi-scale modeling of the industrial sensors time series. The proposed approach is based on the evolutionary generative design of the composite modeling pipelines. The iterative data decomposition algorithm is proposed in the paper to improve the quality of the sensor time series forecasting. To effectively use it in an automated way, the boosting-like mutation operators have been implemented for graphs-based genotypes. The proposed approach reduced the forecast error by 10% compared to the competitor library AutoTS. Also, the proposed modifications of the evolutionary algorithm resulted in better metrics in 78% of the cases where they were used.
Attentional Local Contrastive Learning for Face Forgery Detection

In this work, we present a novel representation learning module for face forgery detection called attentional local contrastive learning (ALCL). ALCL is designed to distinguish forged regions from pristine regions using an explicit constraint. Specifically, feature vectors extracted by the backbone are first embedded into a unit hypersphere, and for each local feature vector, ALCL constructs horizontal and vertical triple sets respectively with its adjacent vectors. ALCL minimizes the angle between vectors of the same source while maximizing that between different sources by optimizing their normalized cosine similarity. Moreover, we also propose a multiple scale residual learning (MSRL) module that takes advantage of rich residual information to complement RGB input. We demonstrate the effectiveness of the proposed method through comprehensive experiments. On multiple challenging face forgery benchmarks, our method achieves great performances under both in-domain and cross-domain settings, and also shows good robustness to compression compared to existing works.
Evaluation of the Economic Potential of Photovoltaic Power Generation in Road Spaces

Photovoltaic (PV) power generation has become an important clean energy generation source. In the context of transportation development and its very large energy demand, scholars have begun to use PV power generation technology on roads and their surrounding road spaces. Current research on PV power generation in road spaces has mostly focused on its feasibility and technical potential, but there have been few studies on its economic potential. For this reason, this paper used the Zhengding County of Hebei Province, China, to study the evaluation method of the technical and economic potential of PV power generation in road spaces and to analyze the development potential of PV power generation in road spaces. The results show that Zhengding County has a very high amount of road space available for PV power generation, with an effective PV installation area of 20.98 km2 and an annual theoretical power generation capacity of 1.5 billion kWh. If the PV road space project is fully operational in 2021, it could be profitable by 2026, and the net profit (NP) could reach $705 million in 2030. The application of photovoltaic power generation in road spaces is a very promising method of sustainable energy supply.
An Image Encryption Algorithm Based on Complex Network Scrambling and Multi-Directional Diffusion

Various security threats are encountered when keys are transmitted in public channels. In this paper, we propose an image encryption algorithm based on complex network scrambling and multi-directional diffusion. Combining the idea of public key cryptography, the RSA algorithm is used to encrypt the key related to plaintext. The algorithm consists of three stages: key generation stage, complex network scrambling stage, and multi-directional diffusion stage. Firstly, during the key generation phase, SHA-512 and the original image are used to generate plaintext-related information, which is then converted to plaintext-related key through transformation mapping. Secondly, in the complex network scrambling stage, the chaotic random matrix establishes the node relationships in the complex network, which is then used to construct an image model based on the complex network, and then combines pixel-level and block-level methods to scramble images. Finally, in the multi-directional diffusion stage, the multi-directional diffusion method is used to perform forward diffusion, middle spiral diffusion, and backward diffusion on the image in turn to obtain the final ciphertext image. The experimental results show that our encryption algorithm has a large keyspace, the encrypted image has strong randomness and robustness, and can effectively resist brute force attack, statistical attack, and differential attack.
Pruning graph neural networks by evaluating edge properties

The emergence of larger and deeper graph neural networks (GNNs) makes their training and inference increasingly expensive. Existing GNN pruning methods simultaneously prune the graph adjacency matrix and the model weights on a pretrained neural network by directly leveraging the lottery-ticket hypothesis, but the benefits of such methods are mainly via weight pruning, and methods based on saliency metrics struggle to outperform random pruning when pruning only the graph adjacency matrix. This motivates us to use different scoring standards for graph edges and network weights during GNN pruning. Thus, rather than measuring the importance of graph edges based on saliency metrics, we formulate the performance of GNNs mathematically with respect to the properties of their edges, elucidating how the performance drop can be avoided by pruning negative edges and nonbridges. This leads to our simple but effective two-step method for GNN pruning, leveraging the saliency metrics for the network pruning while sparsifying the graph with preservation of the loss performance. Experimental results show the effectiveness and efficiency of the proposed method on both small-scale graph datasets (Cora, Citeseer, and PubMed) and a large-scale dataset (Ogbn-ArXiv), where our method saves up to 98% of floating-point operations per second (FLOPs) on the small graphs and 94% of FLOPs on the large one, with no significant drop in accuracy.
Extreme learning machine models for predicting the n-octanol/water partition coefficient (Kow) data of organic compounds

The partition coefficient of n-octanol/water (Kow) is generally considered a valuable characteristic in many natural sciences fields. Predictive quantitative structure-property and relationship (QSPR) models for Kow are encouraged since the experimental data are not always available. Extreme learning machine (ELM) method with the advantages of fast learning speed and good generalization ability has been applied to various domains. This work introduced COSMO descriptors (Sσ-profile) of molecules as descriptors and the ELM algorithm to construct estimation models for Kow of organics. Four ELM models were developed and their outcomes were compared with the MLR models produced by the same descriptors. Results revealed that the proposed ELM models were reliable and applicable to predicting the logKow values of compounds, especially the ELM-4 model with the best correlation coefficient R2 (0.949) and root-mean-square error RMSE (0.358). Therefore, the proposed approaches are effective and feasible, and can be extensively generalized to forecast other important properties of chemicals.
METHOD OF REVEALING CREATIVE PERSONS IN SCIENTIFIC TEAM

An approach is developed to quantify the creativity of scientific and pedagogical staff using the Russian Science Citation Index in terms of their professional performance, including scientometric indicators. The problems of conducting surveys and testing large audiences of respondents are analyzed. Variants of possible criteria and indicators used to assess employees’ professional activities, including identifying their creative contribution to the collective indicator, are considered. The concept of intellectual products of educational and scientific institutions is defined, since their creation, development and publication are one of the requirements for employees. The article shows the interrelation of the respondents’ personal intellectual abilities, including creative ones, and their contribution to the institution scientific potential. The authors substantiate that it is advisable for faculty members to use such categories as educational, methodological, scientific activities, advanced training. The interrelation of distributing the employees’ contribution to the institution scientific potential with the Gauss law is considered. The authors propose to apply the Pareto principle, according to which “20% of efforts give 80% of the result, and the remaining 80% of efforts is only 20% of the result” to assess the creativity level. About a quarter of the employees are proven to produce about three-quarters of the entire intellectual potential of the university or a scientific institution. A threshold equal to 0.44 is justified, according to which the Pareto principle is implemented when evaluating each employee’s contribution. The developed analytical apparatus and illustrative material revealing the essence of the developed method are presented. Conclusions are drawn and directions for future research are outlined.
Dwell Selection with ML-based Intent Prediction Using Only Gaze Data

We developed a dwell selection system with ML-based prediction of a user's intent to select. Because a user perceives visual information through the eyes, precise prediction of a user's intent will be essential to the establishment of gaze-based interaction. Our system first detects a dwell to roughly screen the user's intent to select and then predicts the intent by using an ML-based prediction model. We created the intent prediction model from the results of an experiment with five different gaze-only tasks representing everyday situations. The intent prediction model resulted in an overall area under the curve (AUC) of the receiver operator characteristic curve of 0.903. Moreover, it could perform independently of the user (AUC=0.898) and the eye-tracker (AUC=0.880). In a performance evaluation experiment with real interactive situations, our dwell selection method had both higher qualitative and quantitative performance than previously proposed dwell selection methods.
Geryon

Vision-based drone-view object detection suffers from severe performance degradation under adverse conditions (e.g., foggy weather, poor illumination). To remedy this, leveraging complementary mmWave radar has become a trend. However, existing fusion approaches seldom apply to drones due to i) the aggravated sparsity and noise of point clouds from low-cost commodity radars, and ii) explosive sensing data and intensive computations leading to high latency. To address these issues, we design Geryon, an edge assisted object detection system on drones, which utilizes a suit of approaches to fully exploit the complementary advantages of camera and mmWave radar on three levels: (i) a novel multi-frame compositing approach utilizes camera to assist radar to address the aggravated sparsity and noise of radar point clouds; (ii) a saliency area extraction and encoding approach utilizes radar to assist camera to reduce the bandwidth consumption and offloading latency; (iii) a parallel transmission and inference approach with a lightweight box enhancement scheme further reduces the offloading latency while ensuring the edge-side accuracy-latency trade-off by the parallelism and better camera-radar fusion. We implement and evaluate Geryon with four datasets we collect under foggy/rainy/snowy weather and poor illumination conditions, demonstrating its great advantages over other state-of-the-art approaches in terms of both accuracy and latency.
Density-optimized intersection-free mapping and matrix multiplication for join-project operations

A Join-Project operation is a join operation followed by a duplicate eliminating projection operation. It is used in a large variety of applications, including entity matching, set analytics, and graph analytics. Previous work proposes a hybrid design that exploits the classical solution (i.e., join and deduplication), and MM (matrix multiplication) to process the sparse and the dense portions of the input data, respectively. However, we observe three problems in the state-of-the-art solution: 1) The outputs of the sparse and dense portions overlap, requiring an extra deduplication step; 2) Its table-to-matrix transformation makes an over-simplified assumption of the attribute values; and 3) There is a mismatch between the employed MM in BLAS packages and the characteristics of the Join-Project operation. In this paper, we propose DIM 3 , an optimized algorithm for the Join-Project operation. To address 1), we propose an intersection-free partition method to completely remove the final deduplication step. For 2), we develop an optimized design for mapping attribute values to natural numbers. For 3), we propose DenseEC and SparseBMM algorithms to exploit the structure of Join-Project for better efficiency. Moreover, we extend DIM 3 to consider partial result caching and support Join- op queries, including Join-Aggregate and MJP (Multi-way Joins with Projection). Experimental results using both real-world and synthetic data sets show that DIM 3 outperforms previous Join-Project solutions by a factor of 2.3X-18X. Compared to RDBMSs, DIM 3 achieves orders of magnitude speedups.
Quantum Blockchain for Smart Society

Smart cities are a futuristic urban development concept that uses ICT to enable citizens, governments, and organizations to collect and share real-time data. Q-BoC technology can provide a new level of convenience and security for communication and transactions among all of a smart city's many stakeholders. Information technology, including quantum blockchain, has been integrated to govern physical, social, and business infrastructures in today's rapidly developing smart cities. Innovative technologies and concepts such as the IoT, 5G, artificial intelligence, and quantum blockchain have become necessary for an intelligent and advanced society. In recent years, both academics and industry have demonstrated a strong interest in the revolution of smart cities. Smart cities can deliver a variety of smart functions, such as intelligent transportation, Industry 5.0, governance, Healthcare 5.0, and smart banking, to improve people's quality of life. The chapter explores the application, challenges, and opportunities for making a smart society.
Designing the next generation of map assessment systems: Open questions and opportunities to automatically assess a student’s knowledge as a map

Instructional strategies employing complex problem-solving examine how learners represent the problem space and argue for decisions. Unlike problems with few solutions (e.g., multiple choices), instructors struggle to evaluate solutions to ill-structured problems quickly and consistently across students. This prevents instructors to provide timely feedback to learners, who may continue to engage in erroneous thinking. Despite the availability of several software packages to assess the learners’ works as maps, several barriers still prevent the wide-scale adoption of such assessment systems by instructors. We examine three open questions and identify opportunities from Artificial Intelligence to develop the next generation of assessment systems.
Equitable Design and Use of Digital Surveillance Technologies During COVID-19: Norms and Concerns

Given the unprecedented scale of digital surveillance in the COVID-19 pandemic, designing and implementing digital technologies in ways that are equitable is critical now and in future epidemics and pandemics. Yet to date there has been very limited consideration about what is necessary to promote their equitable design and implementation. In this study, literature relating to the use of digital surveillance technologies during epidemics and pandemics was collected and thematically analyzed for ethical norms and concerns related to equity and social justice. Eleven norms are reported, including procedural fairness and inclusive approaches to design and implementation, designing to rectify or avoid exacerbating inequities, and fair access. Identified concerns relate to digital divides, stigma and discrimination, disparate risk of harm, and unfair design processes. We conclude by considering what dimensions of social justice the norms promote and whether identified concerns can be addressed by building the identified norms into technology design and implementation practice.
3D trajectory planning based on the Rapidly-exploring Random Tree–Connect and artificial potential fields method for unmanned aerial vehicles

This research proposes a multifaceted approach of three-dimensional trajectory planning based on the combination of Rapidly-exploring Random Tree–Connect algorithm and artificial potential field method to improve the path search ability and dynamic obstacles avoidance capability of unmanned aerial vehicles. Firstly, an improved method of the target gravity is developed by controlling the sampling range to reduce invalid sampling and speed up the convergence speed of the algorithm so as to lessen the restriction of low efficiency and random sampling of the Rapidly-exploring Random Tree–Connect algorithm. Moreover, the regulation factor is introduced into the artificial potential field method to deal with the problem of target unreachable in the trajectory planning. Then the improved Rapidly-exploring Random Tree–Connect algorithm is implemented to plan the global path in a complex environment. This step is carried out via selecting the local target point on the global path found in the global plan, dividing the complex environment into simple environment and utilizing the artificial potential field method to achieve the effect of avoiding unknown dynamic obstacles in the simple environment. Finally, cubic B-spline is employed to smoothing of the planned trajectory. The simulation results demonstrate that the combination of two improved algorithms improves the path search ability and dynamic barrier avoidance capability of the unmanned aerial vehicles.