Reviewerly/exHarmony huggingface dataset:
Prediction of Non-Stationary Multi-Head Cloud Motion Vectors for Intra-Hourly Satellite-Derived Solar Power Forecasting

Solar photovoltaic (PV) power has become one of the major renewable energy sources in modern power systems. Hence, solar forecasting is indispensable for addressing the uncertainties of PV power outputs. Since PV power generation mainly depends on solar radiation conditions, satellite-derived methods have been proven effective in large-scale short-term solar forecasting. These methods utilize satellite optical images to track cloud motion and do not require supplemental ground cameras. However, cloud motion between two consecutive satellite images is highly non-stationary, as the motion includes both displacement and shape variation. Conventional motion estimation methods may miss the detailed cloud variation information, increasing the errors in solar forecasting. In this study, a multi-head cloud motion vector prediction method is proposed to address this challenge. The method models cloud variation as the probabilistic sum of pixels moving under multiple optical flow vectors. Time-varying solar intensity is also considered in the method. In order to estimate these optical flow vectors, a learning-based intelligent model with a self-attention structure is established. The proposed method of multi-step-ahead satellite-derived solar forecasting is superior to conventional methods, as the results of case studies indicate that the proposed method improves the precision of intra-hourly PV power prediction.
Comparing word frequency, semantic diversity, and semantic distinctiveness in lexical organization.

Word frequency (WF) is a strong predictor of lexical behavior. However, much research has shown that measures of contextual and semantic diversity offer a better account of lexical behaviors than WF (Adelman et al., 2006; Jones et al., 2012). In contrast to these previous studies, Chapman and Martin (see record 2022-14138-001) recently demonstrated that WF seems to account for distinct and greater levels of variance than measures of contextual and semantic diversity across a variety of datatypes. However, there are two limitations to these findings. The first is that Chapman and Martin (2022) compared variables derived from different corpora, which makes any conclusion about the theoretical advantage of one metric over another confounded, as it could be the construction of one corpus that provides the advantage and not the underlying theoretical construct. Second, they did not consider recent developments in the semantic distinctiveness model (SDM; Johns, 2021a; Johns et al., 2020; Johns & Jones, 2022). The current paper addressed the second limitation. Consistent with Chapman and Martin (2022), our results showed that the earliest versions of the SDM were less predictive of lexical data relative to WF when derived from a different corpus. However, the later versions of the SDM accounted for substantially more unique variance than WF in lexical decision and naming data. The results suggest that context-based accounts provide a better explanation of lexical organization than repetition-based accounts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
A New Selection and Class Prediction Using Type-1 Fuzzy Logic Applied to a Convolutional Neural Network

In this work, a new way of selecting the belonging of a set of images to their respective classes is presented. In this method we replace the Softmax function, which predicts based on percentages of the image and shows which is more likely based on its predictions and its highest percentages. In the proposal, fuzzy logic is used and we show that its results and predictions are correct using Type-1 fuzzy logic when applied to a convolutional neural network.
Exploring the balance between interpretability and performance with carefully designed constrainable Neural Additive Models

The interpretability of an intelligent model automatically derived from data is a property that can be acted upon with a set of structural constraints that such a model should adhere to. Often these are in contrast with the task objective and it is not straightforward how to explore the balance between model interpretability and performance. In order to allow an interested user to jointly optimise performance and interpretability, we propose a new formulation of Neural Additive Models (NAM) which can be subject to a number of constraints. Accordingly, our approach produces a new model that is called Constrainable NAM (or just CNAM in short) and it allows the specification of different regularisation terms. CNAM is differentiable and is built in such a way that it can be initialised as a solution of an efficient tree-based GAM solver (e.g., Explainable Boosting Machines). From this local optimum the model can then explore solutions with different interpretability-performance tradeoffs according to different definitions of both interpretability and performance. We empirically benchmark the model on 56 datasets against 12 models and observe that on average the proposed CNAM model ranks on the Pareto front of optimal solutions, i.e., models generated by CNAM exhibit a good balance between interpretability and performance. Moreover, we provide two illustrative examples which are aimed to show step by step how CNAM works well for solving classification tasks, but also how it can yield insights when considering regression tasks.
Adapting hybrid approaches for electronic medical record management and sharing using blockchain sharding

In the past few years, it is noticed that management and sharing medical records is a key step towards increasing healthcare provider connectivity and making the healthcare system more efficient.The scalability and sustainability issues confer to mismanagement of patient is record and also raised several issues in privacy and security.The study aims to suggest more efficient alternatives for Electronic Healthcare System.Scalability and privacy are the major limitations that existing systems contain so the goal of this study is to define alternatives about how parameters like scalability, usability and data protection could be achieved in an efficient manner for healthcare system.In the healthcare industry, providing accurate, thorough, and upto-date information on patients is critical.Another feature that allows researchers to consider efficient EHR systems is rapid access to patient records for boosting efficiency and coordination.Blockchain sharding technique is utilized along with hyper-ledger protocols and Proof-of-Authority to carry out our model implementation.
Data literacy training needs of researchers at South African universities

Purpose The aim of this study was to determine data literacy (DL) training needs of researchers at South African public universities. The outcome of this study would assist librarians and researchers in developing a DL training programme which addressed identified needs. Design/methodology/approach A survey research method was used to gather data from researchers at these universities by convenience. Online questionnaires were distributed to public universities through library directors for further distribution to researchers. Findings The results indicate low levels of DL training at the respondent South African public universities with most researchers indicating that they had not received any formal training on DL. A few researchers indicated that they would welcome DL training. Research limitations/implications This study was exploratory in nature and data was received from eight universities, which is not representative of all the 26 public universities in South Africa. Nonetheless, the low DL confirmed by the majority in the realised sample is indicative of the need to further investigate the subject. Practical implications Librarians and research support personnel should collaborate on the development of DL training courses, workshops and materials used by researchers at institutions of higher learning to enhance DLs on campus. Originality/value This study may be novel in South Africa in investigating the DL training needs of researchers at several universities and contributes to the growing body of literature on research data management
Fully chaotic medical image encryption scheme based on dynamic DNA and block rotation

Abstract As an important application of image encryption, digital medical image encryption plays an important role in the field of medical health and privacy protection. This paper put forwards a fully chaotic and strongly plaintext associated image encryption framework based on an improved chaotic system, block rotation and DNA computing. The algorithm generates multiple chaotic sequences by using different one-dimensional seed chaotic maps under the sine transform framework for subsequent block rotation, DNA dynamic encoding and decoding, generating key images for DNA XOR calculation. Simulation experiments and comparative analysis have shown that this algorithm can achieve fantastic encryption performance, resist various attacks, and have higher security levels and good generalization performance.
Enhancing haze removal and super-resolution in real-world images: a cycle generative adversarial network-based approach for synthesizing paired hazy and clear images

Haze significantly impacts various fields, such as autonomous driving, smart cities, and security monitoring. Deep learning has been proven effective in removing haze from images. However, obtaining pixel-aligned hazy and clear paired images in the real world can be challenging. Therefore, synthesized hazed images are often used for training deep networks. These images are typically generated based on parameters such as depth information and atmospheric scattering coefficient. However, this approach may cause the loss of important haze details, leading to color distortion or incomplete dehazed images. To address this problem, this paper proposes a method for synthesizing hazed images using a cycle generative adversarial network (CycleGAN). The CycleGAN is trained with unpaired hazy and clear images to learn the features of the hazy images. Then, the real haze features are added to clear images using the trained CycleGAN, resulting in well-pixel-aligned synthesized hazy and clear paired images that can be used for dehaze training. The results demonstrate that the dataset synthesized using this method efficiently solves the problem associated with traditional synthesized datasets. Furthermore, the dehazed images are restored using a super-resolution algorithm, enabling the obtainment of high-resolution clear images. This method has broadened the applications of deep learning in haze removal, particularly highlighting its potential in the fields of autonomous driving and smart cities.
Multimodal sentiment analysis for social media contents during public emergencies

Abstract Purpose Nowadays, public opinions during public emergencies involve not only textual contents but also contain images. However, the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis, lacking the combination of multimodal contents. In this paper, we propose to combine texts and images generated in the social media to perform sentiment analysis. Design/methodology/approach We propose a Deep Multimodal Fusion Model (DMFM), which combines textual and visual sentiment analysis. We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis. BiLSTM is employed to generate encoded textual embeddings. To fully excavate visual information from images, a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy. A multimodal fusion method is implemented to fuse textual and visual embeddings completely, producing predicted labels. Findings We performed extensive experiments on Weibo and Twitter public emergency datasets, to evaluate the performance of our proposed model. Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models. The introduction of images can boost the performance of sentiment analysis during public emergencies. Research limitations In the future, we will test our model in a wider dataset. We will also consider a better way to learn the multimodal fusion information. Practical implications We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies. Originality/value We consider the images posted by online users during public emergencies on social platforms. The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.
The effect of groups’ reference, usefulness perception, and products quality on intention to buy and online shopping decision

The primary purpose of this study is to investigate how group reference, perceived usefulness, and product quality influence online shoppers' desire to purchase and final purchase decisions. Multiple methods of quantitative analysis and processing of data are used to evaluate and validate the study's hypotheses. Using a Structural Equation Model, the study's theories were tested and analyzed. Customers of digital market websites in Jordan participated in this study by responding to an online poll. Data was compiled from 220 study questionnaires using a systematic sampling strategy. Data analysis with the Structural Equation Model revealed significant favorable effects of group reference, perceived usefulness, and product quality on both purchase intent and final online purchase decisions. The authors chose to center their study around the perspectives, comments, attitudes, and impressions of groups of individuals. Perceived usefulness can be gauged along the aspects of experience, enjoyment, and subjective norm. The words customer expectations, real product specifications, and the quality of the service received all shed light on the product's quality. The originality of this study rests in the model that was created to explain the relationship between group reference, perceived usefulness of products, and product quality as they relate to purchase intent and digital market website use in Jordan.
Differential Privacy for Protecting Private Patterns in Data Streams

Complex event processing (CEP) is a powerful and increasingly more important tool to analyse data streams for Internet of Things (IoT) applications. These data streams often contain private information that requires proper protection. However, privacy protection in CEP systems is still in its infancy, and most existing privacy-preserving mechanisms (PPMs) are adopted from those designed for data streams. Such approaches undermine the quality of the entire data stream and limit the performance of IoT applications. In this paper, we attempt to break the limitation and establish a new foundation for PPMs of CEP by proposing a novel pattern-level differential privacy (DP) guarantee. We introduce two PPMs that guarantee pattern-level DP. They operate only on data that correlate with private patterns rather than on the entire data stream, leading to higher data quality. One of the PPMs provides adaptive privacy protection and brings more granularity and generalization. We evaluate the performance of the proposed PPMs with two experiments on a real-world dataset and on a synthetic dataset. The results of the experiments indicate that our proposed privacy guarantee and its PPMs can deliver better data quality under equally strong privacy guarantees, compared to multiple well-known PPMs designed for data streams.
Blockchain Technology in Corporate Governance: Advantages and Limitations

This paper is a literature review paper.We discussed the influence and results of using blockchain in corporate governance.In this paper, we considered both the advantages and disadvantages of using blockchain technology.Our research areas included distributed ledgers, smart contracts, corporate governance, DAO "hack" case (2016), blockchain distributed platform, the decentralized autonomous organization (DAO), customer loyalty plan using blockchain, the proof-ofwork system using blockchain, and the blockchain technology in international business.We selected a sample of around 20 articles on blockchain to study the benefit of using blockchain in corporate governance.Based on the convenience and effectiveness of using blockchain, we made the hypothesis that using blockchain in corporate governance would be able to have more transparent, real-time, reliable, cost-effective, verifiable, and accessible transaction records.We hypothesize that using blockchain in corporate governance has more benefits than using the database.Blockchain technology also has some disadvantages such as shareholder protection weakness, fraud transactions, anonymous voting, etc. Generally speaking, based on our analysis of selected articles and literature reviews, we made the following conclusion that blockchain technology will have more benefits in corporate governance compared to traditional systems and other database management systems.
Review of Anti-Counterfeit Solutions in Blockchain

Blockchain technology offers a data format that has built-in security. It is built on cryptography, decentralization, and consensus concepts to ensure transaction confidence. Decentralization is enabled by blockchain technology, which allows members to participate in a dispersed network. No single user can alter the transaction, as all transactions are transparent and visible to all other users on the network. However, there are some significant security differences between blockchain and other systems. Nowadays, there are so many potential attacks found on the blockchain. The contribution of this chapter would be to match the effective defence taken by blockchain technology or a patch for related vulnerabilities offered by researchers to reduce the impact of these attacks. To keep the network secure and resilient, an overview of these security solutions for various vulnerabilities and attacks helps to reduce the attack surface in the blockchain
ASIAM-HGNN: Automatic Selection and Interpretable Aggregation of Meta-Path Instances for Heterogeneous Graph Neural Network

In heterogeneous information network (HIN)-based applications, the existing methods usually use Heterogeneous Graph Neural Networks (HGNN) to handle some complex tasks. However, these methods still have some shortcomings: 1) they manually pre-select some meta-paths and thus some important ones are missing, while the missing ones still contains the information and features of the node in the entire graph structure; and 2) they have no high interpretability since they do not consider the logical sequences in an HIN. In order to deal with them, we propose ASIAM-HGNN: a heterogeneous graph neural network combined with the automatic selection and interpretable aggregation of meta-path instances. Our model can automatically filter important meta paths for each node, while preserving the logical sequence between nodes, so as to solve the problems existing in other models. A group of experiments are conducted on real-world datasets, and the results demonstrate that the models learned by our method have a better performance in most of task scenarios.
Research on Artificial Intelligence Promoting the Reform of Weak Schools Education and Teaching

Artificial intelligence provides new opportunities for the reform of weak schools.Based on the fact that weak schools have their own limitations in the reform path of weak schools, this paper starts from the important new era technical factor of artificial intelligence, and uses its value orientation of "precision", "empowerment" and "reshaping" in the reform of weak schools to propose a path for artificial intelligence to promote the reform of weak schools: creating an intelligent environment, homeschool collaborative education, a new paradigm of classroom teaching, intelligent training for teachers, a new pattern of student learning, and multimodal education evaluation.
Discussion on the Application of Artificial Intelligence Technology in the Development of Electronic Engineering

As an advanced technology in China, artificial intelligence technology has a very high application value.Its application in the field of electronic engineering has improved the level of production technology, increased productivity, and promoted the development of the field of electronic engineering in the direction of intelligence and automation.This paper briefly outlines the artificial intelligence technology and its connection with electronic engineering, and discusses the application of artificial intelligence technology in the development of electronic engineering field, hoping to provide reference for the use of artificial intelligence and the development of electronic engineering field.
Being where, with whom, and when it happens: spatial, interpersonal, and temporal presence while viewing live streaming of collegiate sports in virtual reality

Introduction: Although virtual reality (VR) is most popularly known for its applications to gaming, other entertainment applications are increasingly being explored including in the sports media industry, but little research has so far examined the experiences induced by VR viewing of a live sporting event. Materials and methods: Participants ( n = 93) were university students who were approached in the context of a field study from a nearby community eatery area on the university campus to watch brief segments of a 360° live stream of the home games of their university volleyball and basketball teams both while wearing and not wearing an inexpensive smart-phone based head-mounted display (HMD). Immediately afterward, participants then reported on their relative experience of spatial, interpersonal, and temporal presence, as well as their satisfaction-preference with each of the two viewing modalities, in response to brief face-valid screening questions. Results: The majority of participants experienced greater presence while wearing the VR headset, and approximately one in every two reported preferring to watch the games in VR. Participants’ experience of spatial presence independently correlated with preferring to watch the games in VR. Discussion: Media vendors should offer VR viewing of sports including via inexpensive, smart-phone mediated VR as an additional, cost-effective alternative means of heightening fans’ experience of virtual presence at the games when fans are unable to go to the games in person.