Reviewerly/exHarmony huggingface dataset:
Tuning Multi-Layer Perceptron by Hybridized Arithmetic Optimization Algorithm for Healthcare 4.0

Healthcare 4.0 has been enabled by the recent advances in several different fields, including the artificial intelligence (AI) and Internet of things (IoT), among others. It aims to support the doctors in making decisions as fast as possible by providing early prediction of the illness, that could save lives of the patients. Typical workflow includes IoT sensors that collect the data of the patients, while the AI is employed to analyze this data by utilizing a variety of machine learning approaches to predict and classify the illness. This manuscript aims to suggest a healthcare framework that consists of a neural network that is optimized by a hybridized arithmetic optimization algorithm. In the proposed framework, metaheuristics is used to simultaneously perform the network training and tune the hyper-parameters of the multi-layer perceptron neural network. The performance of this framework has been evaluated on three healthcare benchmark datasets, with the task to classify the fatal conditions including heart diseases, breast cancer and diabetes. The obtained results were compared to five other cutting-edge metaheuristics that were employed in the same framework with the same task. The proposed hybridized arithmetic optimization algorithm achieved superior level of performance, indicating that this structure could assist the medical workers in early diagnostics.
Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting

Electric power systems have experienced the rapid insertion of distributed renewable generating sources and, as a result, are facing planning and operational challenges as new grid connections are made. The complexity of this management and the degree of uncertainty increase significantly and need to be better estimated. Considering the high volatility of photovoltaic generation and its impacts on agents in the electricity sector, this work proposes a multivariate strategy based on design of experiments (DOE), principal component analysis (PCA), artificial neural networks (ANN) that combines the resulting outputs using Mixture DOE (MDOE) for photovoltaic generation prediction a day ahead. The approach separates the data into seasons of the year and considers multiple climatic variables for each period. Here, the dimensionality reduction of climate variables is performed through PCA. Through DOE, the possibilities of combining prediction parameters, such as those of ANN, were reduced, without compromising the statistical reliability of the results. Thus, 17 generation plants distributed in the Brazilian territory were tested. The one-day-ahead PV generation forecast has been considered for each generation plant in each season of the year, reaching mean percentage errors of 10.45% for summer, 9.29% for autumn, 9.11% for winter and 6.75% for spring. The versatility of the proposed approach allows the choice of parameters in a systematic way and reduces the computational cost, since there is a reduction in dimensionality and in the number of experimental simulations.
Up-Sampling Method for Low-Resolution LiDAR Point Cloud to Enhance 3D Object Detection in an Autonomous Driving Environment

Automobile datasets for 3D object detection are typically obtained using expensive high-resolution rotating LiDAR with 64 or more channels (Chs). However, the research budget may be limited such that only a low-resolution LiDAR of 32-Ch or lower can be used. The lower the resolution of the point cloud, the lower the detection accuracy. This study proposes a simple and effective method to up-sample low-resolution point cloud input that enhances the 3D object detection output by reconstructing objects in the sparse point cloud data to produce more dense data. First, the 3D point cloud dataset is converted into a 2D range image with four channels: x, y, z, and intensity. The interpolation on the empty space is calculated based on both the pixel distance and range values of six neighbor points to conserve the shapes of the original object during the reconstruction process. This method solves the over-smoothing problem faced by the conventional interpolation methods, and improves the operational speed and object detection performance when compared to the recent deep-learning-based super-resolution methods. Furthermore, the effectiveness of the up-sampling method on the 3D detection was validated by applying it to baseline 32-Ch point cloud data, which were then selected as the input to a point-pillar detection model. The 3D object detection result on the KITTI dataset demonstrates that the proposed method could increase the mAP (mean average precision) of pedestrians, cyclists, and cars by 9.2%p, 6.3%p, and 5.9%p, respectively, when compared to the baseline of the low-resolution 32-Ch LiDAR input. In future works, various dataset environments apart from autonomous driving will be analyzed.
Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

Recommendation services become an essential and hot research topic for researchers nowadays.Social data such as Reviews play an important role in the recommendation of the products.Improvement was achieved by deep learning approaches for capturing user and product information from a short text.However, such previously used approaches do not fairly and efficiently incorporate users' preferences and product characteristics.The proposed novel Hybrid Deep Collaborative Filtering (HDCF) model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations.To overcome the cold start problem, the new overall rating is generated by aggregating the Deep Multivariate Rating DMR (Votes, Likes, Stars, and Sentiment scores of reviews) from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision, either product is truly popular or not.The proposed novel HDCF model consists of four major modules such as User Product Attention, Deep Collaborative Filtering, Neural Sentiment Classifier, and Deep Multivariate Rating (UPA-DCF + NSC + DMR) to solve the addressed problems.Experimental results demonstrate that our novel model is outperforming state-of-the-art IMDb, Yelp2013, and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy, confidence, and trust of recommendation services.
A relation-aware heterogeneous graph convolutional network for relationship prediction

Most real-world networks are heterogeneous and consist of different types of nodes and edges. Relationships (edges) between nodes of different types carry different semantics, hence relationship prediction in heterogeneous networks aims to predict the existence of a relation between nodes of different types. In this paper, we propose a relation-aware Heterogeneous Graph Convolutional Network architecture for temporal heterogeneous network relationship prediction. In particular, we first present a continuous-time temporal heterogeneous neighbour generation algorithm to capture the continuous-time interactions in the context of a specific node type. The algorithm collects temporal heterogeneous neighbours with K-hops of a node at a recent temporal distance. Moreover, it adds a decaying exponential to ensure that the fewer hops and the closer temporal distances, the more nodes are sampled. Thus, it can capture the evolutionary pattern of interactions in the recent past and collect strongly correlated heterogeneous neighbours for each node. Then the proposed relation-aware Heterogeneous Graph Convolutional Network is used to learn the most relevant relationship to the target relationship to model the heterogeneous graph neural network for predicting target relationships. Our experimental results on three real-world temporal heterogeneous networks indicate significant advances in prediction accuracy and efficiency compared to the state-of-art approaches.
AI-based learning content generation and learning pathway augmentation to increase learner engagement

Retaining learner engagement is a major challenge in online learning environments, which is even more intensified with learning spaces increasingly built by combining resources from multiple independent sources. Narrative-centric learning experience has been found to improve learner engagement by several researchers. Towards this end, we propose an AI-based approach that generates auxiliary learning content called narrative fragments which are interspersed into the learning pathways to create interactive learning narratives. The proposed approach consists of the automatic generation of two types of narrative fragments– overviews of the learning pathway segments and reflection quizzes or formative assessments from learning resources in any format including open educational resources. The pipeline for the generation of the narrative fragments consists of various components based on different semantic models and a natural language generation (NLG) component based on a pre-trained language model GPT-2 (Generative Pre-trained Transformer 2). Automation enables the generation of narrative fragments on the fly whenever there are changes in the learning pathway due to the need for reiteration of concepts, pre-requisite knowledge acquisition, etc., enabling adaptability in the learning pathways. The proposed approach is domain agnostic which makes it easily adaptable to different domains. The NLG model is evaluated using ROUGE scores against several baselines. Automatically generated narrative fragments are evaluated by human evaluators. We obtained encouraging results in both cases.
Multi-sample learning particle swarm optimization with adaptive crossover operation

Particle swarm optimization (PSO) is a well-known optimization method used for solving various optimization problems. However, PSO suffers from premature convergence and is ineffective in balancing exploration and exploitation when solving complex optimization problems. To overcome these drawbacks of PSO, a multi-sample learning particle swarm optimization with adaptive crossover operation (MLPSO) is proposed. In MLPSO, two novel strategies, multi-sample selecting strategy (MSS) and adaptive sample crossover strategy (ASC), are used to select proper learning samples for the whole population. Firstly, in MSS, two sample pools, namely elite pool and improver pool, are used to save elites and improvers. Elites are the particles with preferable fitness, while improvers denote the particles whose fitness have been improved largely in recent consecutive generations. In each generation, two particles are randomly selected from the two sample pools respectively to breed a learning sample through crossover operation for the whole population. Therefore, the generated learning sample by MSS strategy contains more diversity information. Secondly, in ASC, various crossover operations are conducted for breeding a learning sample according to the evolutionary states. Therefore, the ASC strategy proposed in this paper can realize a better trade-off between exploration and exploitation. Finally, the performance of MLPSO is evaluated using CEC2013, CEC2017 test suites and three engineering optimization problems. Experimental results show that MLPSO outperforms compared seven competitive PSO variants and 19 meta-heuristics algorithms in most functions.
DM-DQN: Dueling Munchausen deep Q network for robot path planning

Abstract In order to achieve collision-free path planning in complex environment, Munchausen deep Q-learning network (M-DQN) is applied to mobile robot to learn the best decision. On the basis of Soft-DQN, M-DQN adds the scaled log-policy to the immediate reward. The method allows agent to do more exploration. However, the M-DQN algorithm has the problem of slow convergence. A new and improved M-DQN algorithm (DM-DQN) is proposed in the paper to address the problem. First, its network structure was improved on the basis of M-DQN by decomposing the network structure into a value function and an advantage function, thus decoupling action selection and action evaluation and speeding up its convergence, giving it better generalization performance and enabling it to learn the best decision faster. Second, to address the problem of the robot’s trajectory being too close to the edge of the obstacle, a method of using an artificial potential field to set a reward function is proposed to drive the robot’s trajectory away from the vicinity of the obstacle. The result of simulation experiment shows that the method learns more efficiently and converges faster than DQN, Dueling DQN and M-DQN in both static and dynamic environments, and is able to plan collision-free paths away from obstacles.
Reinforcement learning from expert demonstrations with application to redundant robot control

Current methods of reinforcement learning from expert demonstrations require humans to give all possible demonstrations in the learning phase, which is very difficult for continuous or high-dimensional spaces. In this paper, we proposed biased exploration reinforcement learning to avoid the exploration of unnecessary states and actions of the expert demonstrations. We present a convergence analysis of the novel method. This method is applied to learn the control of a redundant robot manipulator with 7-degree-of-freedom. The experimental results demonstrate that the proposed method accelerates the learning phase. The obtained policy can successfully achieve the pretended task.
Blockchain Technologies: Smart Contracts for Consumer Electronics Data Sharing and Secure Payment

Blockchain may be an optimal solution when a detailed and transparent record of assets is necessary. It is imperative to manage and safeguard digital interactions or maintain a decentralized and shared system of records in applications, such as those used for electricity production, transmission, distribution, and consumption and those used for data sharing and secure payments. Such applications can benefit from blockchain technology to resolve these problems. In the proposed blockchain-based consumer electronics data sharing and safe payment framework, an innovative IoT meter detects monthly consumption and transmits the data to a decentralized application that is stored in the blockchain. This decentralized platform will generate the bill and provide incentives for legitimate consumers. Finally, the end-to-end latency and throughput were used to evaluate the performance of the proposed approach.
Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review

For years, extensive research has been in the binarization of continuous metaheuristics for solving binary-domain combinatorial problems. This paper is a continuation of a previous review and seeks to draw a comprehensive picture of the various ways to binarize this type of metaheuristics; the study uses a standard systematic review consisting of the analysis of 512 publications from 2017 to January 2022 (5 years). The work will provide a theoretical foundation for novice researchers tackling combinatorial optimization using metaheuristic algorithms and for expert researchers analyzing the binarization mechanism’s impact on the metaheuristic algorithms’ performance. Structuring this information allows for improving the results of metaheuristics and broadening the spectrum of binary problems to be solved. We can conclude from this study that there is no single general technique capable of efficient binarization; instead, there are multiple forms with different performances.
Blockchain Federated Learning for In-Home Health Monitoring

This research combines two emerging technologies, the IoT and blockchain, and investigates their potential and use in the healthcare sector. In healthcare, IoT technology can be utilized for purposes such as remotely monitoring patients’ health. This paper details ongoing research towards individualized health monitoring using wearable gadgets. The goal of improving healthcare facilities and improvement of the quality of life of citizens naturally brings up Internet of Things (IoT) technologies for consideration. Health observation is exceptionally critical in terms of avoidance, especially since the early determination of illnesses can minimize trouble and treatment costs. The cornerstones of intelligent, integrated, and individualized healthcare are continuous monitoring of physical signs and evaluation of medical data. To build a more reliable and robust IoMT model, the study will monitor the application of blockchain technology in federated learning (FL). A viable way to address the heterogeneity problem in federated learning is to design the system, data, and model tiers to lessen heterogeneity and produce a high-quality, tailored model for each endpoint. Blockchain-based federated learning allows for smarter simulations, lower latency, and lower power consumption while maintaining privacy at the same time. This solution provides another immediate benefit: in addition to having a shared model upgrade, the updated model on phones will now be used automatically, giving personalized knowledge about the phone is used.
Performance evaluation of Queueing assisted IoMT-Fog-Blockchain framework for healthcare organizations

As healthcare service is a major concern world-wide therefore cloud computing-driven Internet of Medical Things(IoMT) are widely accepted technology for establishing smart healthcare frameworks in most organizations. Competition among healthcare organizations is increasing day by day to provide the utmost healthcare services for patients. Most of the IoMT-enabled healthcare services are facing technical challenges such as scalability, trust, privacy, security, and interoperability issues. Timely delivery of healthcare service for critical patients is a very crucial aspect for most healthcare organizations. To address all these challenges faced by healthcare organizations the researchers have proposed a novel Queueing assisted IoMT-Fog-Blockchain framework for the healthcare system. In this framework, the temporary data processing, storage, and filtration are done at the nearest fog node. This will reduce the overload, minimize the bandwidth requirement, reduce the traffic density and reduce the healthcare service time required by the cloud servers. A novel Queueing theory approach is proposed for better access control, providing in-time patient care service, and increasing the performance of the framework.
A Span-based Multi-Modal Attention Network for joint entity-relation extraction

Joint extraction of entities and their relations not only depends on entity semantics but also highly correlates with contextual information and entity types. Therefore, an effective joint modelling method designed for handling information from different modalities can lead to a superior performance of the joint entity and relation extraction. Previous span-based models tended to focus on the internal semantics of a span but failed to effectively capture the interactions between the span and other modal information (such as tokens or labels). In this study, a Span-based Multi-Modal Attention Network (SMAN) is proposed for joint entity and relation extraction. The network introduces a cloze mechanism to simultaneously extract the context and span position information, and jointly models the span and label in the relation extraction stage. To determine the fine-grained associations between different modalities, a Modal-Enhanced Attention (MEA) module with two modes is designed and adopted in the modelling process. Experimental results reveal that the proposed model consistently outperforms the state-of-the-art for both entity recognition and relation extraction on the SciERC and ADE datasets, and beats other competing approaches by more than 1.42% F1 score for relation extraction on the CoNLL04 dataset. Extensive additional experiments further verify the effectiveness of the proposed model.
Advanced RetinexNet: A fully convolutional network for low-light image enhancement

Capturing images in weak illumination environments seriously degrades image quality, such as low visibility, low contrast, artifacts, and noise. Solving a series of degradation of low-light images can effectively improve the visual quality of the image and enhance the performance of high-level visual tasks. Based on the Retinex theory, we propose a novel fully convolutional network to simultaneously solve these degradations and effectively improve the visual quality of the image. The entire network contains two sub-networks, the Decom-Net and the Enhance-Net, the image is decomposed into illumination and reflectance through Decom-Net, while noise in the reflectance is suppressed through our proposed frequency-domain noise suppression loss, and the Enhance-Net is proposed to improve the contrast of the illumination map. Our method can properly improve the contrast and suppress noise at the same time. Extensive experiments demonstrate that our method outperforms the existing state-of-the-art methods by a large margin both quantitatively and visually. In addition, our results showed that the performance of the high-level visual task (i.e., face detection) can be effectively improved by using the enhanced results obtained by our method in low-light conditions.
Combining natural language processing and multidimensional classifiers to predict and correct CMMS metadata

Computerized maintenance management systems (CMMSs) contain valuable data on the maintenance operations in an organization. A large part of these data consists of unstructured, written texts contained in failure notifications which are generated each time an unexpected failure occurs, enriched with structured metadata consisting of a number of labels that allow to categorize the failures, such as the type of failure, its cause or the corrective action that was taken. In this paper, we show that natural language processing techniques can be used to predict the structured metadata based on the unstructured text and even identify mislabeled notifications or ambiguous labels. Specific attention is given to the complexity that arises from the highly technical nature of the texts combined with a telegraphic writing style and heavy use of sentence fragments and abbreviations. Moreover, it is shown that exploiting dependencies between different components of the metadata, and regarding the prediction problem as a multidimensional classification problem, can improve the reliability of the predicted labels. We illustrate and test our label prediction pipeline on the CMMS data of a large pharmaceutical company.