Reviewerly/exHarmony huggingface dataset:
A short-term probabilistic photovoltaic power prediction method based on feature selection and improved LSTM neural network

• Cloud cover and visibility are used as new feature variables for photovoltaic output prediction, and a classification method of weather types is proposed, which is based on cloud cover and visibility. • Maximal Information Coefficient is used to select the input variables of the model, which improves the performance of the model. • The simpler form of the gated structure memory network, called CIFG, is proposed to minimize the structure without significantly reducing the prediction accuracy. • A new model that combined Quantile Regression and CIFG, called QRCIFG, is used to perform photovoltaic output probabilistic forecasting. • The experimental results show that QRCIFG has the ability to obtain photovoltaic power prediction results with excellent performance on accuracy, uncertainty, and reliability. With the increase of solar photovoltaic(PV) penetration in power system, the impact of random fluctuation of PV power on the secure operation of power grid becomes more and more serious. An efficient PV forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from PV systems. This study proposes a classification method of weather types based on cloud cover and visibility. A PV power forecasting model is proposed, based on various meteorological data including cloud cover and visibility and in order to make the model show better performance, Maximal Information Coefficient(MIC) is used to select the feature variables. Coupled Input and Forget Gate(CIFG) network is proposed to minimize structure without significantly decreasing the prediction accuracy. Furthermore, a new hybrid method combining Quantile Regression(QR) and CIFG network is proposed to predict the conditional quantile of PV output. Afterward, Kernel Density Estimation(KDE) method is used to estimate PV output probabilistic density function(PDF) according to these conditional quantiles of PV output. The effectiveness and high reliability of the proposed forecasting model are demonstrated through several other forecasting methods, and a significant improvement in PV power prediction is observed.
Insulator Breakage Detection Based on Improved YOLOv5

Aerial images have complex backgrounds, small targets, and overlapping targets, resulting in low accuracy of intelligent detection of overhead line insulators. This paper proposes an improved algorithm for insulator breakage detection based on YOLOv5: The ECA-Net (Efficient Channel Attention Network) attention mechanism is integrated into its backbone feature extraction layer, and the effective distinction between background and target is achieved by increasing the weight of important channels. A bidirectional feature pyramid network is added to the feature fusion layer, and large-scale images with more original information are combined to effectively retain small target features. Incorporating a flexible detection frame selection algorithm Soft-NMS (Soft Non-Maximum Suppression) into the prediction layer to re-screen the target frame, thereby reducing the probability of mistaken deletion of overlapping targets. The effectiveness of the improved YOLOv5 algorithm is verified in the actual aerial image dataset, and the results show that the mean Average Precision (mAP) of the improved algorithm is 95.02% and the detection speed FPS (Frames Per Second) can reach 49.4 frames/s, which meets the real-time and accuracy requirements of engineering applications.
Secure IoT Healthcare Architecture with Deep Learning-Based Access Control System

The existing healthcare system based on traditional management involves the storage and processing of large quantities of medical data. The incorporation of the Internet of Things (IoT) and its gradual maturation has led to the evolution of IoT-enabled healthcare with extraordinary data processing capability and massive data storage. Due to the advancement in the Industrial Internet of Things (IIoT), the resulting system is aimed at building an intelligent healthcare system that can monitor the medical health of the patient by means of a wearable device that is monitored remotely. The data that is gathered by the wearable IoT module is stored in the cloud server which is subject to privacy leakage and attacks by unauthorized users and attackers. To address this security issue, an IoT-based deep learning-based privacy preservation and data analytics system is proposed in this work. Data is collected from the user, and the sensitive information is segregated and separated. Using a convolutional neural network (CNN), the health-related information is analyzed in the cloud, devoid of users’ privacy information. Thus, a secure access control module is introduced that works based on the user attributes for the IoT-Healthcare system. A relationship between the users’ trust and attributes is discovered using the proposed work. The precision, recall, and F1 score of the proposed CNN classifier are achieved at 95%. With the increase in the size of the training set, higher performance is attained. When data augmentation is added, the system performs better without data augmentation. Further, the accuracy of around 98% is achieved with an increased user count. Experimental analysis indicates the robustness and effectiveness of the proposed system with respect to low privacy leakage and high data integrity.
Classification of faults in grid-connected photovoltaic system based on wavelet packet transform and an equilibrium optimization algorithm-extreme learning machine

A novel intelligent scheme using the wavelet packet transform (WPT) and extreme learning machine (ELM) is proposed for fault event classification in the grid-connected photovoltaic (PV) system. The WPT is applied for preprocessing the cycle of the post-fault voltage samples at the point of common coupling (PCC) measurement to get the normalized logarithmic energy entropy (NLEE). The ELM is applied to classify the different fault cases. To enhance the performance of ELM for faults classification, a hybrid optimization mechanism based on an equilibrium optimization algorithm (EOA) is proposed to optimize the selection of input feature subset and the number of ELM hidden nodes. Furthermore, to evaluate the proposed scheme's performance, a comprehensive evaluation was conducted on a 250 kW grid-connected photovoltaic system. From simulation, the classification accuracy is recorded to be 100% under the no-noise condition, while at the signal-to-noise ratios (SNR) of 30, 35, and 40 dB, the accuracies are 98.96, 99.04, and 99.36%, respectively. Moreover, the practical performance of the EOA-ELM classifier is validated using IEEE 34 bus system. The obtained results validate the effectiveness of the proposed scheme in terms of robustness against measurement noise, computation time, and detection accuracy.
Detection of White Blood Cell Cancer using Deep Learning using Cmyk-Moment Localisation for Information Retrieval

Medical diagnosis, notably concerning tumors, has been transformed by artificial intelligence as well as deep neural network. White blood cell identification, in particular, necessitates effective diagnosis and therapy. White Blood Cell Cancer (WBCC) comes in a variety of forms. Acute Leukemia Lymphocytes (ALL), Acute Myeloma Lymphocytes (AML), Chronic Leukemia Lymphocytes (CLL), and Chronic Myeloma Lymphocytes (CML) are white blood cell cancers for which detection is time-consuming procedure, vulnerable to sentient as well as equipment blunders. Despite just a comprehensive review with a competent examiner, it can be hard to render a precise conclusive determination in some cases. Conversely, Computer-Aided Diagnosis (CAD) may assist in lessening the number of inaccuracies as well as duration spent in diagnosing WBCC. Though deep learning is widely regarded as the most advanced method for detecting WBCCs, the richness of the retrieved attributes employed in developing the pixel-wise categorization algorithms has a substantial relationship with the efficiency of WBCC identification. The investigation of the various phases of alterations related with WBC concentrations and characteristics is crucial to CAD. Leveraging image handling plus deep learning technologies, a novel fusion characteristic retrieval technique has been created in this research. The suggested approach is divided into two parts: 1) The CMYK-moment localization approach is applied to define the Region of Interest (ROI) and 2) A CNN dependent characteristic blend strategy is utilized to obtain deep learning characteristics. The relevance of the retrieved characteristics is assessed via a variety of categorization techniques. The suggested component collection approach versus different attributes retrieval techniques is tested with an exogenous resource. With all the predictors, the suggested methodology exhibits good effectiveness, adaptability, including consistency, exhibiting aggregate categorization accuracies of 97.57 percent and 96.41 percent, correspondingly, utilizing the main as well as auxiliary samples. This approach has provided a novel option for enhancing CLL identification that may result towards a more accurate identification of malignancies.
Design of a cryptographically secure pseudo random number generator with grammatical evolution

This work investigates the potential for using Grammatical Evolution (GE) to generate an initial seed for the construction of a pseudo-random number generator (PRNG) and cryptographically secure (CS) PRNG. We demonstrate the suitability of GE as an entropy source and show that the initial seeds exhibit an average entropy value of 7.940560934 for 8-bit entropy, which is close to the ideal value of 8. We then construct two random number generators, GE-PRNG and GE-CSPRNG, both of which employ these initial seeds. We use Monte Carlo simulations to establish the efficacy of the GE-PRNG using an experimental setup designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system. This returned the value of pi of 3.146564000, which is precise up to six decimal digits for the actual value of pi. We propose a new approach called control_flow_incrementor to generate cryptographically secure random numbers. The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology SP800-22 and the Diehard statistical test requirements. We also present a computational performance analysis of GE-CSPRNG demonstrating its potential to be used in industrial applications.
UIF: An Objective Quality Assessment for Underwater Image Enhancement

Due to complex and volatile lighting environment, underwater imaging can be readily impaired by light scattering, warping, and noises. To improve the visual quality, Underwater Image Enhancement (UIE) techniques have been widely studied. Recent efforts have also been contributed to evaluate and compare the UIE performances with subjective and objective methods. However, the subjective evaluation is time-consuming and uneconomic for all images, while existing objective methods have limited capabilities for the newly-developed UIE approaches based on deep learning. To fill this gap, we propose an Underwater Image Fidelity (UIF) metric for objective evaluation of enhanced underwater images. By exploiting the statistical features of these images in CIELab space, we present the naturalness, sharpness, and structure indexes. Among them, the naturalness and sharpness indexes represent the visual improvements of enhanced images; the structure index indicates the structural similarity between the underwater images before and after UIE. We combine all indexes with a saliency-based spatial pooling and thus obtain the final UIF metric. To evaluate the proposed metric, we also establish a first-of-its-kind large-scale UIE database with subjective scores, namely Underwater Image Enhancement Database (UIED). Experimental results confirm that the proposed UIF metric outperforms a variety of underwater and general-purpose image quality metrics. The database and source code are available at https://github.com/z21110008/UIF.
Dynamics of chaotic system based on image encryption through fractal-fractional operator of non-local kernel

In this paper, the newly developed fractal-fractional differential and integral operators are used to analyze the dynamics of chaotic system based on image encryption. The problem is modeled in terms of classical order nonlinear, coupled ordinary differential equations that are then generalized through fractal-fractional differential operator of Mittag-Leffler kernel. In addition to that, some theoretical analyses, such as model equilibria, existence, and uniqueness of the solutions, have been proved. Furthermore, the highly non-linear problem is solved by adopting a numerical scheme through MATLAB software. The graphical solution is portrayed through 2D and 3D portraits. Some interesting results are concluded considering the variation of fractional-order parameter and fractal dimension parameter.
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets

Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become apparent until after the models are deployed. Our work tackles this issue and enables the preemptive analysis of large-scale datasets. REvealing VIsual biaSEs (REVISE) is a tool that assists in the investigation of a visual dataset, surfacing potential biases along three dimensions: (1) object-based, (2) person-based, and (3) geography-based. Object-based biases relate to the size, context, or diversity of the depicted objects. Person-based metrics focus on analyzing the portrayal of people within the dataset. Geography-based analyses consider the representation of different geographic locations. These three dimensions are deeply intertwined in how they interact to bias a dataset, and REVISE sheds light on this; the responsibility then lies with the user to consider the cultural and historical context, and to determine which of the revealed biases may be problematic. The tool further assists the user by suggesting actionable steps that may be taken to mitigate the revealed biases. Overall, the key aim of our work is to tackle the machine learning bias problem early in the pipeline. REVISE is available at https://github.com/princetonvisualai/revise-tool .
Developing serious games for CBRN-e training in mixed reality, virtual reality, and computer-based environments

In the last decade, chemical, biological, radioactive, nuclear, and explosive (CBRN-e) attacks have become severe risks to countries, prompting connected parties to prioritize CBRN-e training. CBRN-e training is typically performed as physical exercises, and although such training is necessary and beneficial, repeating the same training program can be time-consuming and costly. In this study, newly developed versions of two previous serious games—Hospital and Biogarden— and a new mining serious game were developed for training purposes in Virtual Reality (VR), Mixed Reality (MR), and personal computer (PC) environments. The Hospital and Biogarden games’ scenarios were based on the joint activities held in 2018 in France and Belgium as part of the EU H2020 European Network Of CBRN Training CEnters (eNOTICE) project, while the mining game was created over a replica of a training mine at a university in Turkey. Sixteen CBRN-e experts from the eNOTICE project, who took part in the physical training programs in France and Belgium, evaluated the games. For evaluation and extensive feedback, presence, system usability scale, technology acceptance model questionnaires, and open-ended questions were conducted. The findings revealed that serious games have a vast potential in CBRN-e training, and the comparisons of different environments provided invaluable testbeds giving hindsight to develop a future training program.
Mobile sensors based platform of Human Physical Activities Recognition for COVID-19 spread minimization

The development of smartphones technologies has determined the abundant and prevalent computation. An activity recognition system using mobile sensors enables continuous monitoring of human behavior and assisted living. This paper proposes the mobile sensors-based Epidemic Watch System (EWS) leveraging the AI models to recognize a new set of activities for effective social distance monitoring, probability of infection estimation, and COVID-19 spread prevention. The research focuses on user activities recognition and behavior concerning risks and effectiveness in the COVID-19 pandemic. The proposed EWS consists of a smartphone application for COVID-19 related activities sensors data collection, features extraction, classifying the activities, and providing alerts for spread presentation. We collect the novel dataset of COVID-19 associated activities such as hand washing, hand sanitizing, nose-eyes touching, and handshaking using the proposed EWS smartphone application. We evaluate several classifiers such as random forests, decision trees, support vector machine, and Long Short-Term Memory for the collected dataset and attain the highest overall classification accuracy of 97.33%. We provide the Contact Tracing of the COVID-19 infected person using GPS sensor data. The EWS activities monitoring, identification, and classification system examine the infection risk of another person from COVID-19 infected person. It determines some everyday activities between COVID-19 infected person and normal person, such as sitting together, standing together, or walking together to minimize the spread of pandemic diseases.
Diversity based imbalance learning approach for software fault prediction using machine learning models

The Software fault prediction (SFP) target is to distinguish between faulty and non-faulty modules. The prediction model’s performance is vulnerable to the class imbalance issue in SFP. The existing oversampling approaches generate relatively identical synthetic data, which results in over-generalization and less diverse data. Moreover, many undesirable noisy modules are introduced while generating synthetic data. In this study, we propose the Weighted Average Centroid based Imbalance Learning Approach (WACIL), an effective synthetic over-sampling technique to mitigate the imbalance issue. The WACIL first finds borderline instances, then generates pseudo-data of them through a weighted average centroid concept and filters out inappropriate noise data through a filtration process. We conducted experiments on 24 PROMISE and NASA projects and compared them with some of the existing sampling approaches using K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Deep Neural Network (DNN) as classification models. WACIL achieves superior results in terms of Fall Out Rate (FOR), F-measure and Area Under Curve (AUC) and obtains comparable results in terms of Recall and G-mean compared to the competitive approaches. The statistical analysis indicates that WACIL’s ability to outperform the other over-sampling techniques is significant under the statistical Wilcoxon signed rank test and matched pairs rank biserial correlation coefficient effect size. Hence, WACIL is advisable as a competent choice to deal with the imbalance issue in SFP.
Multi-Party Certification on Blockchain and Its Impact in the Market for Lemons

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A Systematic Review on FabLab Environments and Creativity: Implications for Design

Fabrication laboratories (FabLabs) and makerspaces are used to transform ideas into tangible products. Used in a design–learning context, they can enhance cognitive and creative skills. Creativity is the pivotal ability to produce innovative outcomes in makerspaces, and several studies have attempted to understand the role of makerspaces and creativity in specific fields. However, a comprehensive study offering a holistic view of the contributions of the makerspaces as built environments that foster creativity is lacking. Therefore, we conducted a systematic literature review on FabLabs, makerspaces, and creativity to address this research gap. While the review was performed using five major databases, only peer reviewed journal articles were considered. The findings revealed that makerspaces help to develop person, product, physical, and social environments, as well as process aspects of creativity. Moreover, makerspaces induce problem solving, collaborative, and communication skills; they also offer appealing environments and technologies for developing creative solutions to real-life problems. We identified and analysed five major themes dealing with technical skills, technological and environmental elements, STEM learning, and skill development, and elaborated upon their importance for enhancing creativity in FabLab and makerspace environments.
Modified A-Star (A*) Approach to Plan the Motion of a Quadrotor UAV in Three-Dimensional Obstacle-Cluttered Environment

Motion-planning algorithms play a vital role in attaining various levels of autonomy for any ground or flying agent. Three-dimensional (3D) motion-planning is interesting, but rather complex, especially for flying agents such as autonomous unmanned aerial vehicles (UAVs), due to the increased dimensionality of space and consideration of dynamical constraints for a feasible trajectory. Usually, in 3D path search problems, it is hard to avoid extra expanded nodes due to increased dimensionality with various available search options. Therefore, this paper discusses and implements a modified heuristic-based A* formalism that uses a truncation mechanism in order to eradicate the mentioned problem. In this formalism, the complete motion planning is divided into shortest path search problem and smooth trajectory generation. The shortest path search problem is subdivided into an initial naïve guess of the path and the truncation of the extra nodes. To find a naïve shortest path, a conventional two-dimensional (2D) A* algorithm is augmented for 3D space with six-sided search. This approach led to the inclusion of extra expanded nodes and, therefore, it is not the shortest one. Hence, a truncation algorithm is developed to further process this path in order to truncate the extra expanded nodes and then to shorten the path length. This new approach significantly reduces the path length and renders only those nodes that are obstacle-free. The latter is ensured using a collision detection algorithm during the truncation process. Subsequently, the nodes of this shortened path are used to generate a dynamically feasible and optimal trajectory for the quadrotor UAV. Optimal trajectory generation requires that the plant dynamics must be differentially flat. Therefore, the corresponding proof is presented to ensure generation of the optimal trajectory. This optimal trajectory minimizes the control effort and ensures longer endurance. Moreover, quadrotor model and controllers are derived as preliminaries, which are subsequently used to track the desired trajectory generated by the trajectory planner. Finally, results of numerical simulations which ultimately validate the theoretical developments are presented.