Reviewerly/exHarmony huggingface dataset:
Estimating Flyrock Distance Induced Due to Mine Blasting by Extreme Learning Machine Coupled with an Equilibrium Optimizer

Blasting is essential for breaking hard rock in opencast mines and tunneling projects. It creates an adverse impact on flyrock. Thus, it is essential to forecast flyrock to minimize the environmental effects. The objective of this study is to forecast/estimate the amount of flyrock produced during blasting by applying three creative composite intelligent models: equilibrium optimizer-coupled extreme learning machine (EO-ELM), particle swarm optimization-based extreme learning machine (PSO-ELM), and particle swarm optimization-artificial neural network (PSO-ANN). To obtain a successful conclusion, we considered 114 blasting data parameters consisting of eight inputs (hole diameter, burden, stemming length, rock density, charge-per-meter, powder factor (PF), blastability index (BI), and weathering index), and one output parameter (flyrock distance). We then compared the results of different models using seven different performance indices. Every predictive model accomplished the results comparable with the measured values of flyrock. To show the effectiveness of the developed EO-ELM, the result from each model run 10-times is compared. The average result shows that the EO-ELM model in testing (R2 = 0.97, RMSE = 32.14, MAE = 19.78, MAPE = 20.37, NSE = 0.93, VAF = 93.97, A20 = 0.57) achieved a better performance as compared to the PSO-ANN model (R2 = 0.87, RMSE = 64.44, MAE = 36.02, MAPE = 29.96, NSE = 0.72, VAF = 74.72, A20 = 0.33) and PSO-ELM model (R2 = 0.88, RMSE = 48.55, MAE = 26.97, MAPE = 26.71, NSE = 0.84, VAF = 84.84, A20 = 0.51). Further, a non-parametric test is performed to assess the performance of these three models developed. It shows that the EO-ELM performed better in the prediction of flyrock compared to PSO-ELM and PSO-ANN. We did sensitivity analysis by introducing a new parameter, WI. Input parameters, PF and BI, showed the highest sensitivity with 0.98 each.
Oil extraction and gender equality for social equity: The role of corporate social responsibility in Nigeria's coastal communities

We examine the impact of multinational oil companies' (MOCs) corporate social responsibility (CSR) on gender equality for social equity using a combined propensity score matching and logit model. The result indicates a significant relationship between CSR and gender equality for social equity in coastal communities of Nigeria's oil producing region. This implies that CSR of MOCs is a critical factor for promoting equal opportunity, equal access, equal treatment, equal sharing and division of resources. The finding suggest for improved CSR investment of MOCs on empowering the women in coastal communities in taking up alternative livelihoods from conservation and marine resources.
Short-term PV power forecasting based on time series expansion and high-order fuzzy cognitive maps

Achieving short-term accurate photovoltaic power forecasting is of great significance to improve the efficiency of grid operation, especially in power stations where historical values of meteorological parameters are lagged or not recorded. Therefore, numerous studies have been presented to predict the intricate stochastic properties of photovoltaic power sequence in recent years. However, due to the limitation of algorithm performance and the effect of noise in the time series, previously proposed methods may not extract effective features from power data. Furthermore, most studies have only focused on point prediction, which ignores the uncertain information and unavoidable forecast bias. In this study, a hybrid framework based on fuzzy information granulation algorithm, improved variational mode decomposition technique, and high-order fuzzy cognitive maps is proposed to fill these gaps. Comparison experiments were set up using 5-minute photovoltaic power data from Alice Springs, Australia. The computational results not only demonstrate that the proposed framework significantly improves forecast accuracy of short-term photovoltaic power, but also achieves effective interval prediction by fuzzy information.
Survey of Distributed Computing Frameworks for Supporting Big Data Analysis

Distributed computing frameworks are the fundamental component of distributed computing systems.They provide an essential way to support the efficient processing of big data on clusters or cloud.The size of big data increases at a pace that is faster than the increase in the big data processing capacity of clusters.Thus, distributed computing frameworks based on the MapReduce computing model are not adequate to support big data analysis tasks which often require running complex analytical algorithms on extremely big data sets in terabytes.In performing such tasks, these frameworks face three challenges: computational inefficiency due to high I/O and communication costs, non-scalability to big data due to memory limit, and limited analytical algorithms because many serial algorithms cannot be implemented in the MapReduce programming model.New distributed computing frameworks need to be developed to conquer these challenges.In this paper, we review MapReduce-type distributed computing frameworks that are currently used in handling big data and discuss their problems when conducting big data analysis.In addition, we present a non-MapReduce distributed computing framework that has the potential to overcome big data analysis challenges.
Ex-post facto analysis of cryptocurrency literature over a decade using bibliometric technique

Heaping praise on cryptocurrency network due to its transparency, portability, divisibility, and resistance to inflation, the crypto market has gained huge support from investors and traders worldwide. Given its rising popularity, most of the central banks today plan to launch their own cryptocurrencies. Such phenomenal growth trend has undoubtedly impacted academic research with hundreds of publications filling the academic space. This study provides a comprehensive overview of the academic trends and thematic dimensions of cryptocurrency research. Applying bibliometrics, we track ten thematic clusters identified as: cryptocurrency as a tool for risk management; decentralization of crypto transactions using blockchain and fintech; regulatory and digital framework of cryptocurrencies; market efficiency of cryptocurrencies; pricing efficiency of cryptocurrencies; price clustering and liquidity in crypto transactions; cryptocurrency as an investment asset; portfolio diversification using cryptocurrency; trading volume, return and volatility of cryptocurrencies; and role of information in the volatility of crypt prices. Along with an analysis of each of the thematic clusters, we provide directions for future research.
Application of Deep Learning in Software Defect Prediction: Systematic Literature Review and Meta-analysis

Despite recent attention given to Software Defect Prediction (SDP), the lack of any systematic effort to assess existing empirical evidence on the application of Deep Learning (DL) in SDP indicates that it is still relatively under-researched. To synthesize literature on SDP using DL, pertaining to measurements, models, techniques, datasets, and achievements; to obtain a full understanding of current SDP-related methodologies using DL; and to compare the DL models’ performances with those of Machine Learning (ML) models in classifying software defects. We completed a thorough review of the literature in this domain. To answer the research issues, results from primary investigations were synthesized. The preliminary findings for DL vs. ML in SDP were verified by using meta-analysis (MA). We discovered 63 primary studies that passed the systematic literature review quality evaluation. However, only 19 primary studies passed the MA quality evaluation. The five most popular performance measurements employed in SDP were f-measure, recall, accuracy, precision, and Area Under the Curve (AUC). The top five DL techniques used in building SDP models were Convolutional Neural Network (CNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), Deep Belief Network (DBN), and Stacked Denoising Autoencoder (SDAE). PROMISE and NASA datasets were found to be used more frequently to train and test DL models in SDP. The MA results show that DL was favored over ML in terms of study and dataset across accuracy, f-measure, and AUC. The application of DL in SDP remains a challenge, but it has the potential to achieve better predictive performance when the performance-influencing parameters are optimized. We provide a reference point for future research which could be used to improve research quality in this domain.
Swin Unet3D: a three-dimensional medical image segmentation network combining vision transformer and convolution

Semantic segmentation of brain tumors plays a critical role in clinical treatment, especially for three-dimensional (3D) magnetic resonance imaging, which is often used in clinical practice. Automatic segmentation of the 3D structure of brain tumors can quickly help physicians understand the properties of tumors, such as the shape and size, thus improving the efficiency of preoperative planning and the odds of successful surgery. In past decades, 3D convolutional neural networks (CNNs) have dominated automatic segmentation methods for 3D medical images, and these network structures have achieved good results. However, to reduce the number of neural network parameters, practitioners ensure that the size of convolutional kernels in 3D convolutional operations generally does not exceed [Formula: see text], which also leads to CNNs showing limitations in learning long-distance dependent information. Vision Transformer (ViT) is very good at learning long-distance dependent information in images, but it suffers from the problems of many parameters. What's worse, the ViT cannot learn local dependency information in the previous layers under the condition of insufficient data. However, in the image segmentation task, being able to learn this local dependency information in the previous layers makes a big impact on the performance of the model.This paper proposes the Swin Unet3D model, which represents voxel segmentation on medical images as a sequence-to-sequence prediction. The feature extraction sub-module in the model is designed as a parallel structure of Convolution and ViT so that all layers of the model are able to adequately learn both global and local dependency information in the image.On the validation dataset of Brats2021, our proposed model achieves dice coefficients of 0.840, 0.874, and 0.911 on the ET channel, TC channel, and WT channel, respectively. On the validation dataset of Brats2018, our model achieves dice coefficients of 0.716, 0.761, and 0.874 on the corresponding channels, respectively.We propose a new segmentation model that combines the advantages of Vision Transformer and Convolution and achieves a better balance between the number of model parameters and segmentation accuracy. The code can be found at https://github.com/1152545264/SwinUnet3D .
Multi-feature space similarity supplement for few-shot class incremental learning

With the continuous addition of new classes of data, incremental learning is widely followed and studied, especially for few-shot scenarios. Few-shot class incremental learning (FSCIL) aims to continually learn new knowledge with limited data, without forgetting old knowledge. Owing to limited new class samples and special incremental processes, FSCIL faces the problems of old knowledge catastrophic forgetting and new class adaption. We focus on a multi-feature space similarity supplement (MFS3) to alleviate the two dilemmas. We first train different feature spaces for different sessions, and use an inter-feature space similarity supplement (IFS3) to focus on boundary-sensitive sample points to improve the expression ability of every single session. To handle old knowledge catastrophic forgetting, we use the base feature space as the major and take multiple new feature spaces as supplements. For better adaption of new classes, we further design an outer-feature space similarity supplement (OFS3). OFS3 can utilize the supplement of base feature space with new feature space to rejudge the sample points, whose similarity results of base feature space are changed due to the addition of new classes. In general, IFS3 facilitates a single session, while OFS3 contributes a multi-feature space similarity supplement between different sessions in FSCIL. Sufficient experiments on three benchmark datasets, including CIFAR100, miniImageNet, and CUB200 demonstrate the advantage of our method. With the multi-feature space similarity supplement strategy, our method outperforms the state-of-the-art approaches by a large margin.
Artificial Intelligence for Natural Disaster Management

Artificial intelligence (AI) can leverage massive amount of diverse types of data, such as geospatial data, social media data, and wireless network sensor data, to enhance our understanding of natural disasters, their forecasting and detection, and humanitarian assistance in natural disaster management (NDM). Due to this potential, different communities have been dedicating enormous efforts to the development and/or adoption of AI technologies for NDM. This article provides an overview of these efforts and discusses major challenges and opportunities in this topic.
Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution

Deep learning-based single image super-resolution (SISR) approaches have drawn much attention and achieved remarkable success on modern advanced GPUs. However, most state-of-the-art methods require a huge number of parameters, memories, and computational resources, which usually show inferior inference times when applying them to current mobile device CPUs/NPUs. In this paper, we propose a simple plain convolution network with a fast nearest convolution module (NCNet), which is NPU-friendly and can perform a reliable super-resolution in real-time. The proposed nearest convolution has the same performance as the nearest upsampling but is much faster and more suitable for Android NNAPI. Our model can be easily deployed on mobile devices with 8-bit quantization and is fully compatible with all major mobile AI accelerators. Moreover, we conduct comprehensive experiments on different tensor operations on a mobile device to illustrate the efficiency of our network architecture. Our NCNet is trained and validated on the DIV2K $$3\times $$ dataset, and the comparison with other efficient SR methods demonstrated that the NCNet can achieve high fidelity SR results while using fewer inference times. Our codes and pretrained models are publicly available at https://github.com/Algolzw/NCNet .
Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processing

One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source codes are now available in order to create a large-scale, classical machine-learning and quantum machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of open-source functions that point to potential exploits. We created an efficient and scalable vulnerability detection method based on a deep neural network model– Long Short-Term Memory (LSTM), and quantum machine learning model– Long Short-Term Memory (QLSTM), that can learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Previous studies lack analyzing features of the source code that causes models to recognize flaws in real-life examples. Therefore, We keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as Glove and fastText. The embedded vectors are subsequently fed into the classical and quantum convolutional neural networks to classify the possible vulnerabilities. To measure the performance, we used evaluation metrics such as F1 score, precision, recall, accuracy, and total execution time. We made a comparison between the results derived from the classical LSTM and quantum LSTM using basic feature representation as well as semantic and syntactic representation. We found that the QLSTM with semantic and syntactic features detects significantly accurate vulnerability and runs faster than its classical counterpart.
Impact of quantum noise on the training of quantum Generative Adversarial Networks

Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different error mitigation and error correction techniques have been proposed. In this paper, we conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of quantum noise, focusing on a simplified use case in high-energy physics. In particular, we explore the effects of readout and two-qubit gate errors on the qGAN training process. Simulating a noisy quantum device classically with IBM's Qiskit framework, we examine the threshold of error rates up to which a reliable training is possible. In addition, we investigate the importance of various hyperparameters for the training process in the presence of different error rates, and we explore the impact of readout error mitigation on the results.
Active Weighted Aging Ensemble for drifted data stream classification

One of the significant problems in data stream classification is the concept drift phenomenon, which consists of the change in probabilistic characteristics of the classification task. Such changes in posterior probability destabilize the classification model performance, seriously degrading its quality. It is necessary to design appropriate strategies to counteract this phenomenon, allowing the classifier to adapt to the changing probabilistic characteristics. It is tough to propose such an approach with limited access to data labels. A human bias of high quality is usually costly, so to minimize the expenses related to this process, it is also necessary to propose learning strategies based on semi-supervised learning. Such strategies employ active learning methods indicating which of the incoming objects are valuable to be labeled for improving the classifier's performance. This paper proposes Active Weighted Aging Ensemble algorithm, a novel chunk-based method for non-stationary data stream classification. It employs a classifier ensemble approach and utilizes the changing ensemble lineup to react to concept drift appropriately. It also proposed a new active learning method, considering a limited budget that may be applied to any data stream classifier. AWAE has been evaluated through computer experiments using real and synthetic data streams, confirming the proposed algorithm's high quality over state-of-the-art methods. The research conducted on benchmark data streams confirmed the effectiveness of the proposed solution and highlighted its strengths in comparison with state-of-the-art methods. The estimated computational complexity is acceptable and comparable to the benchmark algorithms.
Speciesism and Preference of Human–Artificial Intelligence Interaction: A Study on Medical Artificial Intelligence

Artificial intelligence (AI) has revolutionized the medical industry in the decade. It is critical to integrate human–computer interaction into daily clinic service and further increase the public acceptance of medical AI. Based on self-categorization theory, our research draws on speciesism as a vital cognitive factor to examine how patients’ speciesism affects their acceptance of medical AI in different roles. The study adopted a positivist research paradigm by examining 249 samples of data collected during COVID-19 in China. The results indicate that patients with higher speciesism tend to have lower acceptance of medical AI in an independent role but higher acceptance in an assistive role. Furthermore, we verified the mediating effect of human–computer trust and the positive moderating role of human uniqueness perception. This article expands the practicality of speciesism from human–animal relationships into human–AI relationships and contributes to human–computer interaction from the perspective of medical AI acceptance.
AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit

In this paper we introduce AFFDEX 2.0 – a toolkit for analyzing facial expressions in the wild, that is, it is intended for users aiming to; a) estimate the 3D head pose, b) detect facial Action Units (AUs), c) recognize basic emotions and 2 new emotional states (sentimentality and confusion), and d) detect high-level expressive metrics like blink and attention. AFFDEX 2.0 models are mainly based on Deep Learning, and are trained using a large-scale naturalistic dataset consisting of thousands of participants from different demographic groups. AFFDEX 2.0 is an enhanced version of our previous toolkit [36], that is capable of tracking faces at challenging conditions, detecting more accurately facial expressions, and recognizing new emotional states (sentimentality and confusion). AFFDEX 2.0 outperforms the state-of-the-art methods in AU detection and emotion recognition. AFFDEX 2.0 can process multiple faces in real time, and is working across the Windows and Linux platforms.
Quantum Communication Systems: Vision, Protocols, Applications, and Challenges

The growth of modern technological sectors have risen to such a spectacular level that the blessings of technology have spread to every corner of the world, even to remote corners. At present, technological development finds its basis in the theoretical foundation of classical physics in every field of scientific research, such as wireless communication, visible light communication, machine learning, and computing. The performance of the conventional communication systems is becoming almost saturated due to the usage of bits. The usage of quantum bits in communication technology has already surpassed the limits of existing technologies and revealed to us a new path in developing technological sectors. Implementation of quantum technology over existing system infrastructure not only provides better performance but also keeps the system secure and reliable. This technology is very promising for future communication systems. This review article describes the fundamentals of quantum communication, vision, design goals, information processing, and protocols. Besides, quantum communication system model is also proposed here. This research included and explained the prospective applications of quantum technology over existing technological systems, along with the potential challenges of obtaining the goal.