Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production

The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-term forecasting of renewable energy resources using an existing ML/DL model is still debatable and needs additional research. Considering the constraints inherent in current empirical or physical-based forecasting models, the study utilizes ML/DL models to provide long-term predictions for solar power production. This study aims to examine the efficacy of several existing forecasting models. The study suggests approaches to enhance the accuracy of long-term forecasting of solar power generation for a case study power plant. It summarizes and compares the statistical model (ARIMA), ML model (SVR), DL models (LSTM, GRU, etc.), and ensemble models (RF, hybrid) with respect to long-term prediction. The performances of the univariate and multivariate models are summarized and compared based on their ability to accurately predict solar power generation for the next 1, 3, 5, and 15 days for a 100-kW solar power plant in Lubbock, TX, USA. Conclusions are drawn predicting the accuracy of various model changes with variation in the prediction time frame and input variables. In summary, the Random Forest model predicted long-term solar power generation with 50% better accuracy over the univariate statistical model and 10% better accuracy over multivariate ML/DL models.
Adaptive Marginalized Semantic Hashing for Unpaired Cross-Modal Retrieval

In recent years, Cross-Modal Hashing (CMH) has aroused much attention due to its fast query speed and efficient storage.Previous literatures have achieved promising results for Cross-Modal Retrieval (CMR) by discovering discriminative hash codes and modality-specific hash functions.Nonetheless, most existing CMR works are subjected to some restrictions: 1) It is assumed that data of different modalities are fully paired, which is impractical in real applications due to sample missing and false data alignment, and 2) binary regression targets including the label matrix and binary codes are too rigid to effectively learn semantic-preserving hash codes and hash functions.To address these problems, this paper proposes an Adaptive Marginalized Semantic Hashing (AMSH) method which not only enhances the discrimination of latent representations and hash codes by adaptive margins, but also can be used for both paired and unpaired CMR.As a two-step method, in the first step, AMSH generates semantic-aware modality-specific latent representations with adaptively marginalized labels, which enlarges the distances between different classes, and exploits the labels to preserve the inter-modal and intra-modal semantic similarities into latent representations and hash codes.In the second step, adaptive margin matrices are embedded into the hash codes, and enlarge the gaps between positive and negative bits, which improves the discrimination and robustness of hash functions.On this basis, AMSH generates similarity-preserving hash codes and robust hash functions without strict one-to-one data correspondence requirement.Experiments are conducted on several benchmark datasets to demonstrate the superiority and flexibility of AMSH over some state-of-the-art CMR methods.
Balanced neighbor exploration for semi-supervised node classification on imbalanced graph data

Graph neural networks (GNNs) are powerful models for node classification tasks. When the nodes exhibit a skew distribution, the imbalanced graph data and the message-passing way of GNNs may significantly influence the decision boundary. Existing methods, generating additional nodes and edges or assigning weights of nodes to balance the training scenario, generally have a complicated implementation and a high time complexity. To this end, we propose Balanced Neighbor Exploration (BNE), an algorithm to improve training efficiency and classification performance in a fundamentally different way without generating nodes and computing weights. Intuitively, nodes highly influenced by nodes in one class should have a higher probability of belonging to the class. Inspired by this intuition, BNE incorporates more nodes highly influenced by minority nodes into the minority set to provide a balanced training scenario. The neighbor exploration involves sum and sort operations whose time complexities are O(N) and O(Nlog⁡N), respectively, with a fast operation process. Moreover, BNE conducts the message-passing and neighbor exploration processes simultaneously, with a straightforward implementation. Experiments on real-world imbalanced graph data demonstrate that BNE vastly outperforms the state-of-the-art methods for semi-supervised node classification on imbalanced graph data.
K-sets and k-swaps algorithms for clustering sets

We present two new clustering algorithms called k-sets and k-swaps for data where each object is a set. First, we define the mean of the sets in a cluster, and the distance between a set and the mean. We then derive the k-sets algorithm from the principles of classical k-means so that it repeats the assignment and update steps until convergence. To the best of our knowledge, the proposed algorithm is the first k-means based algorithm for this kind of data. We adopt the idea also into random swap algorithm, which is a wrapper around the k-means that avoids local minima. This variant is called k-swaps. We show by experiments that this algorithm provides more accurate clustering results than k-medoids and other competitive methods.
Adaptable pathway to net zero carbon: A case study for Techno-Economic &amp; Environmental assessment of Rooftop Solar PV System

Several countries have a critical need to enhance their electricity production to satisfy the tremendously increasing energy demand and to minimize the black outs. Due to scarcity of fossil fuels and degrading environment, rooftop Solar Photovoltaic (SPV) can be a suitable renewable alternative of green and clean energy. In this paper, due to the techno-economic, environmental and social benefits of Solar PV, the modelling and planning of a solar PV plant to fulfil the energy demand of an academic building is carried out. The analysis is done including all the financial and environmental aspects considering recent market prices and future price escalation. A Modified Greedy Search based optimal planning of the SPV to minimize grid dependence considering load variation pattern with its assessment analysis for an academic building is proposed in this research work. The various financial parameters that affect the feasibility of Solar PV plant i.e., energy payback period, grid dependency, rate of investment return, economic payback period, rate of escalation with actual cost of electricity, annual maintenance cost, depreciation and taxation are considered and evaluated. The environmental factors related to the generation from Solar PV i.e., CO2 emission reduction and carbon credit are also included in analysis. The results obtained for the proposed Solar PV plant shows that the energy payback period is 8 years and IRR with depreciation and taxation is 21% (approx.) with net CO2 mitigation of 1199 tCO2 during the useful life span (25 years) of the plant.
Cryptocurrency Risks, Fraud Cases, and Financial Performance

In this study, we examine major cryptocurrencies, present notable fraud cases, describe fraud risks, and analyze cryptocurrency financial performance. People debate whether cryptocurrency is an investment opportunity, the new Dutch Tulip Bubble, or a giant Ponzi scheme. There have been a number of high-profile fraud cases associated with cryptocurrencies, such as the FTX scandal in late 2022, thereby making fraud a real concern to current and potential future investors. Regarding financial performance, cryptocurrencies experienced a major collapse in value in the most recent period of the study, about three times worse than the major stock market indices. While in prior periods, cryptocurrencies have significantly outperformed stock market indices, recent fraud cases and the extreme volatility of cryptocurrencies indicate that investing in cryptocurrencies comes with much higher risk than traditional stock market investments. The debate over the investment potential of cryptocurrencies continues, whether they have long term value or are simply the new Dutch Tulip Bubble. The study’s findings will be useful to investors, regulators, and academic researchers regarding the cryptocurrency industry.
Irregular mask image inpainting based on progressive generative adversarial networks

Existing image inpainting algorithms have achieved good results in regular mask inpainting tasks, but there are still many limitations for irregular mask, and the inpainting results lack edge consistency and semantic correctness. To address these problems, we designed a progressive generative adversarial network in this paper. The generative network mainly contains a progressive feature generation module and an adaptive consistent attention module, and the discriminator network uses SN-PatchGAN. The network performs recursive inpainting from the edge to the centre, and the repaired features are used as the conditions for the next feature generation, which makes the constraints of the central content gradually strengthened. In order to obtain distant information from the feature maps as well as to consider the possible problem of inconsistency between feature mappings under different recursions when using attention directly in the progressive inpainting network, we designed an adaptive consistent attention module in this paper, which can adaptively combine scores obtained from different recursions to capture more feature information. The discriminator used in this paper is SN-PatchGAN, which directly calculates the hinge loss of each point on the output graph and can focus on different locations as well as different semantics. Comparing this paper's method on CelebA dataset and CMP Facade dataset with the latest work, the experimental results show that this paper's method performs well in the irregular mask image inpainting task, and the inpainting results have better performance in terms of edge consistency, semantic correctness, and overall image structure.
Forecasting Bike Sharing Demand Using Quantum Bayesian Network

In recent years, bike-sharing systems (BSS) are being widely established in urban cities to provide a sustainable mode of transport, by fulfilling the mobility requirements of public residents. The application of BSS in highly congested urban cities reduces the effect of overcrowding, pollution, and traffic congestion problems. The crucial role behind incorporating BSS depends on the prediction of bike demand across all the bike stations. The bike demand prediction involves real-time analysis for identifying the discrepancy between the bike pick-up and drop-off throughout all the bike stations in a given time period. To enhance the prediction analysis of bike demand we propose quantum computing algorithms to provide computational speedup in comparison with classical algorithms. In this paper, we illustrate the construction of Quantum Bayesian Networks (QBN), for predicting bike demand. Furthermore, we provide a solution framework for implementing QBN for two case studies: (a) bike demand prediction during weekdays, (b) bike demand prediction during weekends. We have compared the quantum and classical solutions, by using IBM-Qiskit and Netica computing platforms.
A privacy-preserving logistic regression-based diagnosis scheme for digital healthcare

In recent years, with the popularity of smart wearable devices, online diagnosis is becoming a promising medical technology and therefore promotes the progress of digital healthcare. Online diagnostic services relieve computing and storage requirements of wearable devices with the help of the cloud, while facilitating remote collaboration and data sharing, providing instant access to major diagnostics that patients can obtain the diagnosis within seconds, thereby saving a lot of time and economic costs. However, the frequent occurrence of security incidents based on wireless transmission of wearable devices further exacerbates the security risks of patient health data, and therefore the security of online diagnosis based on wearable devices should be taken seriously. This paper proposes privacy-preserving logistic regression based online disease diagnosis (LR-DDH), where the privacy of the medical data can be preserved with the use of homomorphic authenticated encryption. Theoretical analysis and experimental results demonstrate that the scheme LR-DDH proposed in this paper achieves efficient computation and communication under the premise of security.
Feature selection in high dimensional data: A specific preordonnances-based memetic algorithm

In supervised learning scenarios, feature selection has been largely investigated in the literature because only a few features carry valuable information. This study introduces an algorithm for heterogeneous variable selection in the discrimination problem. The proposed algorithm, Specific Memetic Algorithm Preordonnances-based (SMAP), uses association techniques based on preordonnances theory and is a hybrid filter-wrapper algorithm to make full use of the benefits of each: The filter phase measures the relevance of features by their agreement with the target variable and discards those that disagree ; this leads to a reduction of the search space, while the wrapper phase measures the usefulness of subsets of features to identify the best one using a memetic algorithm based on preordonnances theory. The association is quantified by a coefficient measuring the concordance between two variables, even if one is numeric and the other is categorical (mixed). We propose a generalization of this coefficient measuring the concordance between several (more than two) heterogeneous variables. In this study, a new feature discrimination power measure combining the two coefficients is introduced to intensify and diversify the search taking a minimum amount of time. SMAP is empirically analyzed by comparing its performance to that of recently referred state-of-art approaches on seven datasets and on simulated ones using three different classifiers. The experimental results show the superiority of SMAP over comparative methods.
An edge-located uniform pattern recovery mechanism using statistical feature-based optimal center pixel selection strategy for local binary pattern

The Local Binary Pattern (LBP) is a commonly used method for texture classification that performs well in terms of feature discrimination. However, (1) LBP can misclassify some important edge-located textures as non-uniform patterns with only one bin in the feature histogram, thus losing their discrimination capability. (2) When center pixel is contaminated by noise, a uniform pattern may be transformed into a non-uniform pattern, which can seriously affect the obtained LBP, thus degrading the classification results. To overcome these drawbacks, an edge-located uniform pattern recovery mechanism using statistical feature-based optimal center pixel selection strategy (SFB-OCPS) is proposed in this paper. To extract the correct edge pixels, we divide the whole texture image into 16 = 4 × 4 sub-images and propose an edge pixel selection strategy (EPSS) based on adaptive quantization with local threshold on each sub-image. Then 3 candidate center pixels constructed by statistical features of the local sampling neighborhood are generated for each edge-located center pixel. After the steps above, the SFB-OCPS strategy is introduced into the LBP-based algorithms. It is possible to recover some important edge-located non-uniform patterns to uniform patterns with an optimal center pixel selection, thus improving feature discrimination capability of the LBP-based algorithms. It should be emphasized that any LBP variants can introduce the proposed SFB-OCPS strategy to achieve the recovery of the edge-located uniform patterns. To validate the effectiveness of the proposed SFB-OCPS strategy, we introduce the SFB-OCPS strategy into the original LBP and 5 representative LBP-based algorithms. Experiments are conducted on 6 representative texture databases. Classification comparison reveals that the introduction of SFB-OCPS strategy can significantly improve the texture classification performance of LBP-based algorithms. Additionally, the noise-robustness of the proposed SFB-OCPS strategy is also verified through a series of experiments.
Cybersecurity enhancement to detect credit card frauds in health care using new machine learning strategies

As the usage of credit cards has become more common in health care applications of everyday life, banks have found it very difficult to detect credit card fraud (CCF) systematically. The fraudulent activities should be identified and detected using new techniques. As a result, machine learning (ML) can help detect CCF in transactions while reducing the strain on financial institutions. This research aims to improve cybersecurity measures by detecting fraudulent transactions in datasets. The new classifier strategies cluster and classifier-based decision tree (CCDT), cluster and classifier-based logistic regression (CCLR), and cluster and classifier-based random forest (CCRF) are modeled in this research. The proposed strategies are applied to detect fraudulent health care activities. This research performed the preprocessing through the feature extraction, sampling, and transformation stages, and the proposed classifiers are simulated, and the results are analyzed. The significant results expected range of the proposed classifiers over the other methods are accuracy—(99.95%, 99.97%), precision—(99.96%, 99.98%), sensitivity—(99.9%, 100%), specificity—(99.8%, 100%). The parameters $$\mu $$ , location, the binary variable, cluster size, and decision tree sampling observations affect the classifiers' performance. CCRF and CCLR obtain the expected significant results than other existing methods.
Determining the Key Drivers for the Acceptance and Usage of AR and VR in Cultural Heritage Monuments

An increasing number of visitor attractions and monuments are designing and implementing augmented reality (AR) and virtual reality (VR) technologies and services to enhance tourists’ experiences and make their visit more attractive and enjoyable. It is essential to acquire an in-depth understanding of AR/VR applications and their influence on consumers’ experience. This paper set to investigate the key factors influencing tourists to adopt and use AR and VR applications by exploring the consumers’ perceptions within the cultural heritage settings, focusing on the use of technological tools during the on-site experience (core service experience). This study applied the Ntheoretical basis of the Unified Theory of Acceptance and Use of Technology (UTAUT2) and suggested a research model of twelve hypotheses. The latter was empirically investigated within the Chinese context in two cultural heritage monuments. The findings indicated that AR/VR technology plays an important role in influencing the tourism experience. Performance expectancy, price value, habit, personal innovativeness, and involvement significantly positively affect behavioral intention. The most important drivers are price value and facilitating conditions. This study’s contribution is twofold: (i) theoretical, by elaborating an extended framework for digital technologies implemented in a challenging field of visitors’ experience, and (ii) practical, by formulating a set of suggestions for industry practitioners as well as designers and developers of AR/VR applications regarding the effective design and implementation of AR/VR technologies in cultural heritage monuments.
VR-Based Learning Media of Earthquake-Resistant Construction for Civil Engineering Students

The shaking of the surface of the Earth is what is known as an earthquake; its effects can span a wide area and cause such damage as to result in the total collapse of buildings. It is essential to improve the construction industry to protect buildings from disaster. However, construction development is costly. Therefore, this article focuses mainly on creating an earthquake-resistant construction model using Virtual Reality (VR), which offers its users new ways to improve knowledge transfer and communication. There were three stages in generating this model: pre-development, development, and post-development. These stages include a needs assessment, planning, initial development, validation, analysis and evaluation, and field testing. In the post-development stage, the model was then tested by civil engineering students, and a statistical analysis was used to evaluate the implementation of VR. The VR was developed to assist civil engineering students while fostering their interest in information technology. The results indicated that the VR-based application had a favorable and significant effect on learning. In addition, the mean score of 17.3 showed an improvement in average score for the VR-based application compared to traditional education. Integration of VR into civil engineering education can statistically improve learning outcomes, particularly regarding the construction of earthquake-resistant buildings.
PRAFT and RPBFT: A class of blockchain consensus algorithm and their applications in electric vehicles charging scenarios for V2G networks

With the improvement of people's awareness of environmental protection, electric vehicles (EVs) are becoming more and more popular, and the issue of vehicle to grid (V2G) energy trading is also put on the agenda. To protect the security and privacy of EVs when they trade energy with the grid, many scholars have introduced the emerging blockchain technology. However, there are few studies on the blockchain consensus algorithm for the EVs charging scenario, while the consensus is exactly the core technology in blockchain for reaching agreement in distributed systems, which to some extent determines the efficiency of V2G. Therefore, aiming at the above scenario, this paper proposes two low-complexity consensus algorithms, namely (PBFT-enabled RAFT) PRAFT and (RAFT-enable PBFT) RPBFT, which are combined the typical blockchain consensus PBFT and RAFT, and can be respectively applied to two EVs charging scenarios. In our V2G model, charging piles (CPs) and charging stations (CSs) will participate in the blockchain consensus as nodes. Through theoretical analysis and simulation, and compared with other methods, these two consensus algorithms have high scalability, low communication complexity, low storage overhead, high throughput, and low latency. Meanwhile they can also avoid the risk of Byzantine leader in RAFT. Finally, we demonstrate the two consensus algorithms in a real charging scenario, which show their transaction latency and energy consumption can well adapt to the EVs charging scenario in V2G networks.