Reviewerly/exHarmony huggingface dataset:
Nature-inspired approach: An enhanced whale optimization algorithm for global optimization

The whale optimization algorithm is based on the bubble-net attacking behavior of humpback whales and simulates encircling prey, bubble-net attacking and searching for prey to obtain the global optimal solution. However, the basic whale optimization algorithm has the disadvantage of search stagnation, easily falls into a local optimum, has slow convergence speed and has low calculation accuracy. The Lévy flight strategy is beneficial for expanding the search range and prevents the algorithm from falling into a local optimum, which enhances the global search ability. The ranking-based mutation operator can increase the selection probability and accelerate the convergence speed to enhance the local search ability. To overcome these shortcomings and avoid premature convergence, the Lévy flight strategy and the ranking-based mutation operator are added to the whale optimization algorithm. In this paper, an enhanced whale optimization algorithm is proposed, which realizes complementary advantages to balance exploration and exploitation. Eighteen benchmark test functions and five structural engineering design problems are used to verify the robustness and overall optimization performance of the enhanced whale optimization algorithm. The experimental results show that the enhanced whale optimization algorithm is an effective and feasible method that has a fast convergence speed, high calculation accuracy, strong robustness and stability.
Bipartite Graph Embedding via Mutual Information Maximization

Bipartite graph embedding has recently attracted much attention due to the fact that bipartite graphs are widely used in various application domains. Most previous methods, which adopt random walk-based or reconstruction-based objectives, are typically effective to learn local graph structures. However, the global properties of bipartite graph, including community structures of homogeneous nodes and long-range dependencies of heterogeneous nodes, are not well preserved. In this paper, we propose a bipartite graph embedding called BiGI to capture such global properties by introducing a novel local-global infomax objective. Specifically, BiGI first generates a global representation which is composed of two prototype representations. BiGI then encodes sampled edges as local representations via the proposed subgraph-level attention mechanism. Through maximizing the mutual information between local and global representations, BiGI enables nodes in bipartite graph to be globally relevant. Our model is evaluated on various benchmark datasets for the tasks of top-K recommendation and link prediction. Extensive experiments demonstrate that BiGI achieves consistent and significant improvements over state-of-the-art baselines. Detailed analyses verify the high effectiveness of modeling the global properties of bipartite graph.
An Evolutionary Multitasking-Based Feature Selection Method for High-Dimensional Classification

Feature selection (FS) is an important data preprocessing technique in data mining and machine learning, which aims to select a small subset of information features to increase the performance and reduce the dimensionality. Particle swarm optimization (PSO) has been successfully applied to FS due to being efficient and easy to implement. However, most of the existing PSO-based FS methods face the problems of trapping into local optima and computationally expensive high-dimensional data. Multifactorial optimization (MFO), as an effective evolutionary multitasking paradigm, has been widely used for solving complex problems through implicit knowledge transfer between related tasks. Inspired by MFO, this study proposes a novel PSO-based FS method to solve high-dimensional classification via information sharing between two related tasks generated from a dataset. To be specific, two related tasks about the target concept are established by evaluating the importance of features. A new crossover operator, called assortative mating, is applied to share information between these two related tasks. In addition, two mechanisms, which are variable-range strategy and subset updating mechanism, are also developed to reduce the search space and maintain the diversity of the population, respectively. The results show that the proposed FS method can achieve higher classification accuracy with a smaller feature subset in a reasonable time than the state-of-the-art FS methods on the examined high-dimensional classification problems.
Incentivizing Differentially Private Federated Learning: A Multidimensional Contract Approach

Federated learning is a promising tool in the Internet-of-Things (IoT) domain for training a machine learning model in a decentralized manner. Specifically, the data owners (e.g., IoT device consumers) keep their raw data and only share their local computation results to train the global model of the model owner (e.g., an IoT service provider). When executing the federated learning task, the data owners contribute their computation and communication resources. In this situation, the data owners have to face privacy issues where attackers may infer data property or recover the raw data based on the shared information. Considering these disadvantages, the data owners will be reluctant to use their data to participate in federated learning without a well-designed incentive mechanism. In this article, we deliberately design an incentive mechanism jointly considering the task expenditure and privacy issue of federated learning. Based on a differentially private federated learning (DPFL) framework that can prevent the privacy leakage of the data owners, we model the contribution as well as the computation, communication, and privacy costs of each data owner. The three types of costs are data owners' private information unknown to the model owner, which thus forms an information asymmetry. To maximize the utility of the model owner under such information asymmetry, we leverage a 3-D contract approach to design the incentive mechanism. The simulation results validate the effectiveness of the proposed incentive mechanism with the DPFL framework compared to other baseline mechanisms.
Fusion of Federated Learning and Industrial Internet of Things: A survey

Industrial Internet of Things (IIoT) lays a new paradigm for the concept of Industry 4.0 and paves an insight for new industrial era. Nowadays smart machines and smart factories use machine learning/deep learning based models for incurring intelligence. However, storing and communicating the data to the cloud and end device leads to issues in preserving privacy. In order to address this issue, Federated Learning (FL) technology is implemented in IIoT by the researchers nowadays to provide safe, accurate, robust and unbiased models. Integrating FL in IIoT ensures that no local sensitive data is exchanged, as the distribution of learning models over the edge devices has become more common with FL. Therefore, only the encrypted notifications and parameters are communicated to the central server. In this paper, we provide a thorough overview on integrating FL with IIoT in terms of privacy, resource and data management. The survey starts by articulating IIoT characteristics and fundamentals of distributed machine learning and FL. The motivation behind integrating IIoT and FL for achieving data privacy preservation and on-device learning are summarized. Then we discuss the potential of using machine learning (ML), deep learning (DL) and blockchain techniques for FL in secure IIoT. Further we analyze and summarize several ways to handle the heterogeneous and huge data. Comprehensive background on data and resource management are then presented, followed by applications of IIoT with FL in automotive, robotics, agriculture, energy, and healthcare industries. Finally, we shed light on challenges, some possible solutions and potential directions for future research.
What to teach? Strategies for developing digital competency in preservice teacher training

Developing digital competencies is a critical component of preservice teacher training. However, this is a complex process which includes a range of different strategies. Moreover, little is known about how they come together as an integrated approach to prepare future teachers for educational technology use. Therefore, the aim of this study is to examine relationships among strategies to develop digital competence in preservice teacher training. Association rules analysis was conducted on a questionnaire dataset of 931 preservice teachers' experiences in their training, representing the six strategies. Results showed four distinct clusters of associated strategies, revealing a new integrated approach for the development of digital competence in teacher training. Specifically, multi-directional and dynamic relationships in strategies are needed to improve preservice teachers’ experiences developing digital competencies. Future directions for research are discussed.
Economic development thresholds for a green economy in sub-Saharan Africa

This study investigates how increasing economic development affects the green economy in terms of CO 2 emissions, using data from 44 countries in the sub-Saharan Africa for the period 2000–2012. The Generalized Method of Moments is used for the empirical analysis. The following main findings are established. First, relative to CO 2 emissions, enhancing economic growth and population growth engenders a U-shaped pattern whereas increasing inclusive human development shows a Kuznets curve. Second, increasing gross domestic product growth beyond 25% of annual growth is unfavorable for a green economy. Third, a population growth rate of above 3.089% (i.e. annual %) has a positive effect of CO 2 emissions. Fourth, an inequality-adjusted human development index of above 0.4969 is beneficial for a green economy because it is associated with a reduction in CO 2 emissions. The established critical masses have policy relevance because they are situated within the policy ranges of adopted economic development dynamics.
Matrix-Based Evolutionary Computation

Computational intelligence (CI), including artificial neural network, fuzzy logic, and evolutionary computation (EC), has rapidly developed nowadays. Especially, EC is a kind of algorithm for knowledge creation and problem solving, playing a significant role in CI and artificial intelligence (AI). However, traditional EC algorithms have faced great challenge of heavy computational burden and long running time in large-scale (e.g., with many variables) problems. How to efficiently extend EC algorithms to solve complex problems has become one of the most significant research topics in CI and AI communities. To this aim, this paper proposes a matrix-based EC (MEC) framework to extend traditional EC algorithms for efficiently solving large-scale or super large-scale optimization problems. The proposed framework is an entirely new perspective on EC algorithm, from the solution representation to the evolutionary operators. In this framework, the whole population (containing a set of individuals) is defined as a matrix, where a row stands for an individual and a column stands for a dimension (decision variable). This way, the parallel computing functionalities of matrix can be directly and easily carried out on the high performance computing resources to accelerate the computational speed of evolutionary operators. This paper gives two typical examples of MEC algorithms, named matrix-based genetic algorithm and matrix-based particle swarm optimization. Their matrix-based solution representations are presented and the evolutionary operators based on the matrix are described. Moreover, the time complexity is analyzed and the experiments are conducted to show that these MEC algorithms are efficient in reducing the computational time on large scale of decision variables. The MEC is a promising way to extend EC to complex optimization problems in big data environment, leading to a new research direction in CI and AI.
Machine Learning for Cloud Security: A Systematic Review

The popularity and usage of Cloud computing is increasing rapidly.Several companies are investing in this field either for their own use or to provide it as a service for others.One of the results of Cloud development is the emergence of various security problems for both industry and consumer.One of the ways to secure Cloud is by using Machine Learning (ML).ML techniques have been used in various ways to prevent or detect attacks and security gaps on the Cloud.In this paper, we provide a Systematic Literature Review (SLR) of ML and Cloud security methodologies and techniques.We analyzed 63 relevant studies and the results of the SLR are categorized into three main research areas: (i) the different types of Cloud security threats, (ii) ML techniques used, and (iii) the performance outcomes.We have defined 11 Cloud security areas.Moreover, distributed denial-of-service (DDoS) and data privacy are the most common Cloud security areas, with a 16% level of use and 14%respectively.On the other hand, we found 30 ML techniques used, some used hybrid and others as standalone.The most popular ML used is SVM in both hybrid and standalone models.Furthermore, 60% of the papers compared their models with other models to prove the efficiency of their proposed model.Moreover, 13 different evaluation metrics were enumerated.The most applied metric is true positive rate and least used is training time.Lastly, from 20 datasets found, KDD and KDD CUP'99 are the most used among relevant studies.
Blockchain technology in the energy sector: From basic research to real world applications

The decentralized blockchain technology has been increasingly recognized as a game changer for all centralized things, including traditional centralized energy. Meanwhile, the energy sector has been undergoing transformation from a traditional centralized energy supply system to a distributed energy resource. This work is aimed to explore what the decentralized blockchain technology means to the changing energy sector from the perspectives of basic research and real world applications. After introducing the background information of the blockchain technology, this work investigates the number of publications, subject categories, research areas, cooperation network, research hotspots by combining the bibliometric technique and visual analysis. And then, real-world application cases corresponding to research hotspots are offered. The key findings are as follows: (i) the basic research on the blockchain technology in the energy sector is growing rapidly over time, which means that blockchain energy is a rising research field. (ii) China, a developing country, leads the basic research of blockchain in the energy sector in terms of the total number of publications, institutions and highly cited papers, and cooperative relationship. (iii) keyword hotspot analysis and keyword trend analysis indicate the future research directions of blockchain technology in the energy sector: the decentralized energy market, micro grid, smart grid, energy internet, smart contract, peer-to-peer, renewable energy and electric vehicle. (iv) The real-world application cases are mainly concentrated in the developed countries, especially in the United States, the European Union and Australia. There are relatively few typical application cases in developing countries, which is sharply different from the results of basic research. This work could serve to stimulate meaningful discussion from basic research to real-world applications of blockchain technology in the energy sector.
Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm

Proof of work (PoW), the most popular consensus mechanism for blockchain, requires ridiculously large amounts of energy but without any useful outcome beyond determining accounting rights among miners. To tackle the drawback of PoW, we propose a novel energy-recycling consensus algorithm, namely proof of federated learning (PoFL), where the energy originally wasted to solve difficult but meaningless puzzles in PoW is reinvested to federated learning. Federated learning and pooled-mining, a trend of PoW, have a natural fit in terms of organization structure. However, the separation between the data usufruct and ownership in blockchain lead to data privacy leakage in model training and verification, deviating from the original intention of federal learning. To address the challenge, a reverse game-based data trading mechanism and a privacy-preserving model verification mechanism are proposed. The former can guard against training data leakage while the latter verifies the accuracy of a trained model with privacy preservation of the task requester's test data as well as the pool's submitted model. To the best of our knowledge, our article is the first work to employ federal learning as the proof of work for blockchain. Extensive simulations based on synthetic and real-world data demonstrate the effectiveness and efficiency of our proposed mechanisms.
A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine

Accurate photovoltaic power prediction is important to ensure the safety, stability, and economic operation of the power system after high photovoltaic demand. However, due to the intermittent and stochastic volatility characteristics of photovoltaic power, it is difficult to build satisfactory photovoltaic power prediction models. In this study, a similar day-based ultrashort-term multi-step ahead prediction model of photovoltaic power is proposed, which combines enhanced colliding bodies optimization (ECBO), variational mode decomposition (VMD), and a deep extreme learning machine (DELM). First, a new training data selection method based on grey correlation analysis and a Pearson correlation coefficient is proposed to find days that are similar to the predicted day. Second, the variational mode decomposition, based on enhanced colliding bodies optimization, is used to decompose the original photovoltaic power data into an ensemble of components with different amplitudes and frequencies, in which a new fitness function is designed based on a weighted-permutation entropy algorithm. Finally, the deep extreme learning machine network is employed to complete the prediction of each component, and the enhanced colliding bodies optimization algorithm is utilized again to obtain the optimal neuron numbers in hidden layers of the deep extreme learning machine; the ultimate prediction results of the photovoltaic power is obtained by reconstructing the prediction value of each component. The proposed model is tested with data from a real-world photovoltaic power station located in Xinjiang, China. The experimental results show that: (a) among all the competing models, the proposed model can achieve the highest multi-step prediction accuracy; (b) a qualitative breakthrough on the prediction accuracy improvement has been made by the proposed model in the 1-step and 2-step ahead prediction; (c) only a few parameters need to be tuned manually in the whole prediction process; the proposed model has good intelligence capabilities and can be easily applied and popularized in the practical projects.
Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, published over the past decade, that discuss ML techniques for mineral exploration, exploitation, and mine reclamation. Research trends, ML models, and evaluation methods primarily discussed in the 109 papers were systematically analyzed. The results demonstrated that ML studies have been actively conducted in the mining industry since 2018, mostly for mineral exploration. Among the ML models, support vector machine was utilized the most, followed by deep learning models. The ML models were evaluated mostly in terms of their root mean square error and coefficient of determination.
Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications

In recent years, self-supervised methods for monocular depth estimation has rapidly become an significant branch of depth estimation task, especially for autonomous driving applications. Despite the high overall precision achieved, current methods still suffer from a) imprecise object-level depth inference and b) uncertain scale factor. The former problem would cause texture copy or provide inaccurate object boundary, and the latter would require current methods to have an additional sensor like LiDAR to provide depth ground-truth or stereo camera as additional training inputs, which makes them difficult to implement. In this work, we propose to address these two problems together by introducing DNet. Our contributions are twofold: a) a novel dense connected prediction (DCP) layer is proposed to provide better object-level depth estimation and b) specifically for autonomous driving scenarios, dense geometrical constrains (DGC) is introduced so that precise scale factor can be recovered without additional cost for autonomous vehicles. Extensive experiments have been conducted and, both DCP layer and DGC module are proved to be effectively solving the aforementioned problems respectively. Thanks to DCP layer, object boundary can now be better distinguished in the depth map and the depth is more continues on object level. It is also demonstrated that the performance of using DGC to perform scale recovery is comparable to that using ground-truth information, when the camera height is given and the ground point takes up more than 1.03% of the pixels. Code is available at https://github.com/TJ-IPLab/DNet.
An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm

Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubtedly an innovation as the investment in the real and physical infrastructure is much greater than the cloud technology investment. The present work addresses the issue of power consumption done by cloud infrastructure. As there is a need for algorithms and techniques that can reduce energy consumption and schedule resource for the effectiveness of servers. Load balancing is also a significant part of cloud technology that enables the balanced distribution of load among multiple servers to fulfill users’ growing demand. The present work used various optimization algorithms such as particle swarm optimization (PSO), cat swarm optimization (CSO), BAT, cuckoo search algorithm (CSA) optimization algorithm and the whale optimization algorithm (WOA) for balancing the load, energy efficiency, and better resource scheduling to make an efficient cloud environment. In the case of seven servers and eight server’s settings, the results revealed that whale optimization algorithm outperformed other algorithms in terms of response time, energy consumption, execution time and throughput.