Reviewerly/exHarmony huggingface dataset:
Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods

Obtaining precise solar radiation data is the first stage in determining the availability of solar energy. It is also regarded as one of the major inputs for a variety of solar applications. Due to the scarcity of solar radiation measurement data for many locations throughout the world, many solar radiation models are utilized to predict global solar radiation. Indeed, the most widely used AI technique is artificial neural networks (ANNs). Hitherto, while ANNs have been utilized in various studies to estimate global solar radiation (GSR), limited attention has been given to the architecture of ANN. Thus, this study aimed to: first, optimize the design of one of the faster and most used machine-learning (ML) algorithms, the ANN, to forecast GSR more accurately while saving computation power; second, optimize the number of neurons in the hidden layer to obtain the most significant ANN model for accurate GSR estimation, since it is still lacking; in addition to investigating the impact of varying the number of neurons in the hidden layer on the proficiency of the ANN-based model to predict GSR with high accuracy; and, finally, conduct a comparative study between the ANN and empirical techniques for estimating GSR. The results showed that the best ANN model and the empirical model provided an excellent estimation for the GSR, with a Coefficient of Determination R2 greater than 0.98%. Additionally, ANN architectures with a smaller number of neurons in the single hidden layer (1–3 neurons) provided the best performance, with R2 &gt; 0.98%. Furthermore, the performance of the developed ANN models remained approximately stable and excellent when the number of hidden layer’s neurons was less than ten neurons (R2 &gt; 0.97%), as their performance was very close to each other. However, the ANN models experienced performance instability when the number of hidden layer’s neurons exceeded nine neurons. Furthermore, the performance comparison between the best ANN-based model and the empirical one revealed that both models performed well (R2 &gt; 0.98%). Moreover, while the relative error for the best ANN model slightly exceeded the range, ±10% in November and December, it remained within the range for the empirical model even in the winter months. Additionally, the obtained results of the best ANN model in this work were compared with the recent related work. While it had a good RMSE value of 0.8361 MJ/m2 day−1 within the ranges of previous work, its correlation coefficient (r) was the best one. Therefore, the developed models in this study can be utilized for accurate GSR forecasting. The accurate and efficient estimation of global solar radiation using both models can be valuable in designing and performance evaluation for different solar applications.
A survey on blockchain envisioned attribute based access control for internet of things: Overview, comparative analysis, and open research challenges

The Internet of Things (IoT) network is rapidly expanding due to sudden technological advancements, which enable objects to become intelligent and contribute to the network. Before permitting a newly added IoT device to communicate with the network, it is essential to provide access and authenticate the device’s legitimacy by ensuring that it has not been tampered. Recently, blockchain technology has been integrated into Attribute Based Access Control (ABAC) protocols to supply a more robust security mechanism for access control in IoT. The IoT and blockchain-based ABAC serve as the foundation of this survey, offering a comprehensive introduction to both topics. Additionally, several security concerns and vulnerabilities associated with Blockchain Envisioned ABAC (BE-ABAC) are presented. A Comparison with related surveys has also been made. Besides, we also present a comparative analysis based on evaluation based on Distance from Average Solution (EDAS) to rank the best schemes among the suggested BE-ABAC schemes. In conclusion, we discuss some open research challenges in an IoT network that uses blockchain to manage access control.
Image Deraining Transformer with Sparsity and Frequency Guidance

In recent years, Transformer has witnessed significant progress in the single image deraining field. However, most existing methods do not consider the latent sparse representation and distinguished frequency information. To this end, this paper proposes an effective Image Deraining Transformer with Sparsity and Frequency Guidance, called SFG-IDT. To achieve such guidance, the proposed method consists two key designs: sparsity-compensated multi-head attention (SCMA) and frequency-enhanced multi-scale operator (FEMO). Specifically, the SCMA enhances the concentration of attention while explicitly retaining non-local connectivity with Locality Sensitive Hashing (LSH), to facilitate rain removal better and help image restoration. Simultaneously, the FEMO integrates the frequency information into the multi-scale convolution operators with Fast Fourier Transform (FFT) to obtain a more accurate representation for achieving high-quality derained results. Extensive experimental results show that our developed SFG-IDT outperforms the state-of-the-art approach (Restormer) by 0.27 dB on average, but saves 50.3% parameters and 46.7% computational cost.
Electronic excited states from a variance-based contracted quantum eigensolver

Electronic excited states of molecules are central to many physical and chemical processes, and yet they are typically more difficult to compute than ground states. In this paper we leverage the advantages of quantum computers to develop an algorithm for the highly accurate calculation of excited states. We solve a contracted Schr\"odinger equation (CSE)---a contraction (projection) of the Schr\"odinger equation onto the space of two electrons---whose solutions correspond identically to the ground and excited states of the Schr\"odinger equation. While recent quantum algorithms for solving the CSE, known as contracted quantum eigensolvers (CQEs), have focused on ground states, we develop a CQE based on the variance that is designed to optimize rapidly to a ground or excited state. We apply the algorithm to compute the ground and excited states of ${\mathrm{H}}_{2}, {\mathrm{H}}_{4}$, and BH.
Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach

Chatbots are AI-powered programs designed to replicate human conversation. They are capable of performing a wide range of tasks, including answering questions, offering directions, controlling smart home thermostats, and playing music, among other functions. ChatGPT is a popular AI-based chatbot that generates meaningful responses to queries, aiding people in learning. While some individuals support ChatGPT, others view it as a disruptive tool in the field of education. Discussions about this tool can be found across different social media platforms. Analyzing the sentiment of such social media data, which comprises people’s opinions, is crucial for assessing public sentiment regarding the success and shortcomings of such tools. This study performs a sentiment analysis and topic modeling on ChatGPT-based tweets. ChatGPT-based tweets are the author’s extracted tweets from Twitter using ChatGPT hashtags, where users share their reviews and opinions about ChatGPT, providing a reference to the thoughts expressed by users in their tweets. The Latent Dirichlet Allocation (LDA) approach is employed to identify the most frequently discussed topics in relation to ChatGPT tweets. For the sentiment analysis, a deep transformer-based Bidirectional Encoder Representations from Transformers (BERT) model with three dense layers of neural networks is proposed. Additionally, machine and deep learning models with fine-tuned parameters are utilized for a comparative analysis. Experimental results demonstrate the superior performance of the proposed BERT model, achieving an accuracy of 96.49%.
Towards a Sustainable Future: A Systematic Review of Mobile Learning and Studies in Higher Education

In recent years, mobile learning has emerged as a promising educational paradigm, revolutionizing the landscape of higher education. As the world confronts escalating environmental challenges and calls for sustainable solutions, it becomes essential to explore the potential of mobile learning to contribute to a more sustainable future. This review encompasses a comprehensive analysis of the existing literature, focusing on empirical studies, theoretical frameworks, and case studies conducted between 2002 and 2021. A substantial corpus of 981 articles were selected for in-depth examination by employing rigorous inclusion and exclusion criteria. The findings reveal that mobile learning has witnessed significant growth and diversification within higher education, with pervasive adoption across various disciplines and student populations. The identified studies present many innovative mobile learning strategies, encompassing mobile applications, gamified learning platforms, augmented reality experiences, and location-based learning. In conclusion, this systematic review underscores the substantial potential of mobile learning in higher education toward a sustainable future. By harnessing technological innovations, promoting eco-friendly practices, and fostering digital inclusivity, institutions can embark on a transformative journey that enhances educational outcomes and contributes to environmental preservation and global sustainability efforts. Future research should focus on exploring new avenues for sustainable mobile learning and conducting longitudinal studies to assess the long-term impact of mobile learning initiatives on educational outcomes and environmental conservation.
Snow Mask Guided Adaptive Residual Network for Image Snow Removal

Image restoration under severe weather is a challenging task. Most of the past works focused on removing rain and haze phenomena in images. However, snow is also an extremely common atmospheric phenomenon that will seriously affect the performance of high-level computer vision tasks, such as object detection and semantic segmentation. Recently, some methods have been proposed for snow removing, and most methods deal with snow images directly as the optimization object. However, the distribution of snow location and shape is complex. Therefore, failure to detect snowflakes/snow streak effectively will affect snow removing and limit the model performance. To solve these issues, we propose a Snow Mask Guided Adaptive Residual Network (SMGARN). Specifically, SMGARN consists of three parts, Mask-Net, Guidance-Fusion Network (GF-Net), and Reconstruct-Net. Firstly, we build a Mask-Net with Self-pixel Attention (SA) and Cross-pixel Attention (CA) to capture the features of snowflakes and accurately localized the location of the snow, thus predicting an accurate snow mask. Secondly, the predicted snow mask is sent into the specially designed GF-Net to adaptively guide the model to remove snow. Finally, an efficient Reconstruct-Net is used to remove the veiling effect and correct the image to reconstruct the final snow-free image. Furthermore, we propose a more refined dataset of real snow images, SnowWorld24, to provide faster evaluation of snow-free images. Extensive experiments show that our SMGARN numerically outperforms all existing snow removal methods, and the reconstructed images are clearer in visual contrast. All codes are available at https://github.com/MIVRC/SMGARN.
Student experiences of project-based learning in agile project management education

Project-based learning (PBL) is applied in various disciplines across the world and its benefits are well known and researched. For the past two decades, undergraduate information systems students at South African universities have been required to develop an artefact as part of a module. This is in the form of a project. PBL was formally applied at a South African university to determine the benefits to the students as well as how to improve the module for future students. The results indicate that all 13 criteria of PBL were met and that the students benefitted from a formal approach. They acquired various skills due to PBL that can be directly applied to the workplace. The results also indicate that PBL and the iterative nature of agile as an approach to develop the artefact complement each other. Although PBL assisted the students in learning, the specific skill set required was not determined and measured. This will form part of future research.
Explore Faster Localization Learning For Scene Text Detection

Generally, pre-training and long-time training computation are necessary for obtaining a good-performance text detector based on deep networks. In this paper, we present a new scene text detection network (called FANet) with a Fast convergence speed and Accurate text localization. The proposed FANet is an end-to-end text detector based on transformer feature learning and normalized Fourier descriptor modeling, where the Fourier Descriptor Proposal Network and Iterative Text Decoding Network are designed to efficiently and accurately identify text proposals. Additionally, a Dense Matching Strategy and a well-designed loss function are also proposed for optimizing the network performance. Extensive experiments are carried out to demonstrate that the proposed FANet can achieve the SOTA performance with fewer training epochs and no pretraining. When we introduce additional data for pre-training, the proposed FANet can achieve SOTA performance on MSRA-TD500, CTW1500, and TotalText. The ablation experiments also verify the effectiveness of our contributions. Code is available at https://github.com/callsys/FANet.
On Programming Variability with Large Language Model-based Assistant

Programming variability is central to the design and implementation of software systems that can adapt to a variety of contexts and requirements, providing increased flexibility and customization. Managing the complexity that arises from having multiple features, variations, and possible configurations is known to be highly challenging for software developers. In this paper, we explore how large language model (LLM)-based assistants can support the programming of variability.We report on new approaches made possible with LLM-based assistants, like: features and variations can be implemented as prompts; augmentation of variability out of LLM-based domain knowledge; seamless implementation of variability in different kinds of artefacts, programming languages, and frameworks, at different binding times (compile-time or run-time). We are sharing our data (prompts, sessions, generated code, etc.) to support the assessment of the effectiveness and robustness of LLMs for variability-related tasks.
Emergence of a resonance in machine learning

The benefits of noise to applications of nonlinear dynamical systems through mechanisms such as stochastic and coherence resonances have been well documented. Recent years have witnessed a growth of research in exploiting machine learning to predict nonlinear dynamical systems. It has been known that noise can act as a regularizer to improve the training performance of machine learning. Utilizing reservoir computing as a paradigm, we find that injecting noise to the training data can induce a resonance phenomenon with significant benefits to both short-term prediction of the state variables and long-term prediction of the attractor. The optimal noise level leading to the best performance in terms of the prediction accuracy, stability, and horizon can be identified by treating the noise amplitude as one of the hyperparameters for optimization. The resonance phenomenon is demonstrated using two prototypical high-dimensional chaotic systems.
Neutral atom quantum computing hardware: performance and end-user perspective

Abstract We present an industrial end-user perspective on the current state of quantum computing hardware for one specific technological approach, the neutral atom platform. Our aim is to assist developers in understanding the impact of the specific properties of these devices on the effectiveness of algorithm execution. Based on discussions with different vendors and recent literature, we discuss the performance data of the neutral atom platform. Specifically, we focus on the physical qubit architecture, which affects state preparation, qubit-to-qubit connectivity, gate fidelities, native gate instruction set, and individual qubit stability. These factors determine both the quantum-part execution time and the end-to-end wall clock time relevant for end-users, but also the ability to perform fault-tolerant quantum computation in the future. We end with an overview of which applications have been shown to be well suited for the peculiar properties of neutral atom-based quantum computers.
Role of trust in customer attitude and behaviour formation towards social service robots

This research proposes a model that identifies the antecedents of customers’ attitudes and behaviours towards the utilization of artificially intelligent (AI) social service robots in hospitality and tourism service delivery. The model highlights the importance of trust and its determinants on customers’ attitudes and behaviours towards social service robots. The proposed model and the hypotheses are tested utilizing data collected from the users of two distinctly different hospitality and tourism services. Data were analysed adopting a PLS-SEM approach. Results indicate that the level of acceptance of the use of social robots in service delivery is determined by a multistage process, in which trust perceptions play critical roles. Heuristic (i.e., hedonic motivation) and the individual (i.e., innovativeness) factors positively influence trust in social robots during service delivery. Findings, however, suggest significant differences in different service contexts: anthropomorphism has no effect on trust construct and effort expectancy does not influence positive emotion in hedonic service contexts while these relationships are significant in functional service contexts. Practical and theoretical implications of the findings are discussed.
Expressive Speech-Driven Facial Animation with Controllable Emotions

It is in high demand to generate facial animation with high realism, but it remains a challenging task. Existing approaches of speech-driven facial animation can produce satisfactory mouth movement and lip synchronization, but show weakness in dramatic emotional expressions and flexibility in emotion control. This paper presents a novel deep learning-based approach for expressive facial animation generation from speech that can exhibit wide-spectrum facial expressions with controllable emotion type and intensity. We propose an emotion controller module to learn the relationship between the emotion variations (e.g., types and intensity) and the corresponding facial expression parameters. It enables emotion-controllable facial animation, where the target expression can be continuously adjusted as desired. The qualitative and quantitative evaluations show that the animation generated by our method is rich in facial emotional expressiveness while retaining accurate lip movement, outperforming other state-of-the-art methods.
Technological vulnerability and knowledge of cyber-incidents: threats to innovativeness in local governments?

ABSTRACTGovernments are greatly concerned about cybersecurity, yet most public managers perceive their organization as technologically vulnerable and have little knowledge of past cyber-incidents. Both vulnerability perceptions and knowledge of past exposure are known to affect innovativeness but evidence is mixed and context-dependent. Using a three-year nationally representative pooled dataset of 2,500 managers in 500 US cities, we find that technologically vulnerable departments are less innovative. Knowing about past cyber-incidents positively affects innovativeness but 'ignorant' managers more likely support innovation when technological vulnerability is high. Results call for more research on ignorance management and the implications of cybersecurity management in government agencies.KEYWORDS: Cybersecurityknowledge managementinnovativenesse-governmenttechnological vulnerability Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. Ignorance and ignorant in this research are not used as a pejorative terms. Both terms capture public managers' lack of awareness that can be due to organization structure and behavioural factors as well as individual cognitive ones.2. The 2016 survey data did not include the measures of interest and was not included.3. All three survey were reviewed by an Institutional Review Board and classified as exempt studies.4. The sample was designed in 2010. Data from 2005 Census were the most recently available.5. 2018 data were not available. We used 2017 estimates.6. Centering variables has no effect on significance levels. It only affects the interpretation of coefficients.7. We illustrate how we tested the coefficient equality for technological vulnerability. First, we create two new variables Technology Vulnerability * Year2014 and Technology Vulnerability * Year2018. Then, we re-run the model and tested the equality of coefficients of the two new variables using the LinearHypothesis function from the car package in R. Each coefficient represents the differential effect across the years. If both differences are equal to zero, then all coefficients are equal.
VulDetect: A novel technique for detecting software vulnerabilities using Language Models

Recently, deep learning techniques have garnered substantial attention for their ability to identify vulnerable code patterns accurately. However, current state-of-the-art deep learning models, such as Convolutional Neural Networks (CNN), and Long Short-Term Memories (LSTMs) require substantial computational resources. This results in a level of overhead that makes their implementation unfeasible for deployment in realtime settings. This study presents a novel transformer-based vulnerability detection framework, referred to as VulDetect, which is achieved through the fine-tuning of a pretrained large language model, (GPT) on various benchmark datasets of vulnerable code. Our empirical findings indicate that our framework is capable of identifying vulnerable software code with an accuracy of up to 92.65%. Our proposed technique outperforms SyseVR and VuIDeBERT, two state-of-the-art vulnerability detection techniques.
Skeletonmae: Spatial-Temporal Masked Autoencoders for Self-Supervised Skeleton Action Recognition

Self-supervised skeleton-based action recognition has attracted more attention in recent years. By utilizing the unlabeled data, more generalizable features can be learned to alleviate the overfitting problem and reduce the demand for massive labeled training data. Inspired by the MAE [1], we propose a spatial-temporal masked autoencoder framework for self-supervised 3D skeleton-based action recognition (SkeletonMAE). Following MAE's masking and reconstruction pipeline, we utilize a skeleton-based encoder-decoder transformer architecture to reconstruct the masked skeleton sequences. A novel masking strategy, named Spatial-Temporal Masking, is introduced in terms of both joint-level and frame-level for the skeleton sequence. This pre-training strategy makes the encoder output generalizable skeleton features with spatial and temporal dependencies. Given the unmasked skeleton sequence, the encoder is fine-tuned for the action recognition task. Extensive experiments show that our SkeletonMAE achieves remarkable performance and outperforms the state-of-the-art methods on both NTU RGB+D 60 and NTU RGB+D 120 datasets.