Reviewerly/exHarmony huggingface dataset:
A Very Short-Term Photovoltaic Power Forecasting Model Using Linear Discriminant Analysis Method and Deep Learning Based on Multivariate Weather Datasets

Photovoltaic (PV)-system-generated solar energy has inconsistent and variable properties, which makes controlling electric power distribution and preserving grid stability extremely difficult. A photovoltaic (PV) system’s performance is profoundly affected by the amount of sunlight that reaches the solar cell, the season of the year, the ambient temperature, and the humidity of the air. Every renewable energy technology, sadly, has its problems. As a result, the system is unable to function at its highest or best level. To combat the unstable and intermittent performance of solar power output, it is essential to achieve a precise PV system output power. This work introduces a new approach to enhancing accuracy and extending the time range of very short-term solar energy forecasting (15 min step ahead) by using multivariate time series inputs in different seasons. First, Linear Discriminant Analysis (LDA) is used to select the relevant factors from the mixed meteorological input data. Secondly, two very short-term deep learning prediction models, CNN and LSTM, are used to predict PV power for a shuffled and reduced database of weather inputs. Finally, the predicted outputs from the two models are combined using a classification strategy. The proposed method is applied to one year of real data collected from a solar power plant located in southern Algeria to demonstrate that this technique can improve the forecasting accuracy compared to other techniques, as determined through the use of statistical analysis involving normalized root mean square error (NRMSE), mean absolute error (MAE), mean bias error (MBE), and determination coefficient. (R2).
Novel deep learning models for yoga pose estimator

Abstract Yoga pose recognition and correction are artificial intelligent techniques to provide standardized and appropriate yoga poses. Incorrect yoga poses can cause serious injuries and long-term complications. Analyzing human posture can identify and rectify abnormal positions, improving well-being at home. A posture estimator extracts yoga asana attributes from properly represented images. These extracted features are then utilized directly as inputs for various neural networks and machine learning models. These models serve the purpose of evaluating and predicting the accuracy of specific yoga poses. The objective of this research is to explore multiple methods for classifying yoga poses. The LGDeep model is introduced, which combines a novel residual convolutional neural network with three deep learning approaches: Xception, VGGNet, and SqueezeNet. Additionally, the LGDeep model incorporates feature extraction methods such as LDA and GDA. Experimental results demonstrate that the LGDeep classifier outperforms other approaches and achieves the highest classification accuracy ratio.
Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks

Artistic style transfer, a captivating application of generative artificial intelligence, involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs). By leveraging deep image representations learned by CNNs, we demonstrate how to separate and manipulate image content and style, enabling the synthesis of high-quality images that combine content and style in a harmonious manner. We describe the methodology, including content and style representations, loss computation, and optimization, and showcase experimental results highlighting the effectiveness and versatility of the approach across different styles and content.
Does financial development really spur industrialization in sub‐Saharan African countries?

Industrialization constitutes one of the strategies of structural transformation in many sub-Saharan African (SSA) countries. In this paper, we re-examine the effect of financial development on the industrialization of 35 SSA countries over the period 1996–2018. Contrary to previous work, we formulate the hypothesis that there exists a threshold of financial development beyond which financial development positively influences industrialization. Using the dynamic panel threshold regression model, and system generalized methods of moment models, we find two main results. First, financial development has a negative and significant impact on the development of industrialization. Secondly, the findings show the existence of a nonlinear relationship between financial development and industrialization development. Thus, we find that financial development has a negative effect on industrialization below the threshold while the effect is significantly positive on industrialization in countries whose financial development is above the threshold. The results are stable to the different robustness tests used. These findings highlight the need for SSA countries to develop better financial institutions if they hope to boost the manufacturing sector.
Coarse-to-fine matching via cross fusion of satellite images

The registration of multimodal satellite images is essential for a prerequisite for accruing complementary observational data. Nevertheless, the differential imaging nuances amongst non-linear radiometric multimodal images precipitate a complexity in keypoint detection, rendering it a great challenge. This complexity exacerbates the difficulty encountered in matching multimodal satellite images. In this paper, a dual-branch cross fusion network (DF-Net) is proposed for the purpose of satellite image registration. DF-Net relies on the self-attention granted to a pair of images, thereby providing cross-modal fusion feature descriptions. Initially, reference and sensed images are deployed as inputs for the dual-branch network, which in turn engenders feature descriptions of both high and low resolution, respectively. Sequentially, the matching of individual feature descriptions is anchored on the low-resolution feature map, paving the way for the establishment of coarse matching correspondences. Subsequently, the outcomes of these coarse correspondences are transposed onto the feature map with a higher resolution, thereby generating fine matching results for each coarse correspondence. An exhaustive set of qualitative and quantitative assessments have been administered on three satellite image datasets encompassing a diverse range of scenarios. The average Repeatability (Rep.), Mean Matching Accuracy (MMA), and Root-Mean-Square Error (RMSE) of the DF-Net applied to three large-scale satellite images were recorded to be 0.71, 0.65, and 2.34, respectively. These findings buttress the proficiency of the proposed strategy in facilitating cross-modal matching and bear testimony to the sterling performance of the method proposed.
AI-Based Cybersecurity Threat Detection and Prevention

The chapter presents an overview of AI-based cybersecurity threat detection and prevention. It highlights the importance of AI in tackling the ever-increasing threat landscape and explores various techniques and algorithms used in cybersecurity. AI's ability to process real-time data, identify patterns, and provide accurate threat intelligence is emphasized. The chapter covers machine learning, deep learning, and natural language processing, providing practical examples of their application in cybersecurity. Challenges such as data quality and bias are discussed, along with potential solutions. AI-based cybersecurity solutions like intrusion detection systems and threat intelligence platforms are presented. The chapter concludes with a discussion on the future of AI-based cybersecurity, including emerging technologies like quantum computing and blockchain, and the need for ongoing research and development to address evolving threats. Overall, it offers a comprehensive overview of AI's role in cybersecurity, highlighting benefits, challenges, and future directions.
Histology and Embryology 4.0

Histology and embryology have evolved over time and are now in their 4.0 stages. These fields have progressed from basic microscopes and staining techniques to advanced imaging and computational approaches. Histology 4.0 combines digital imaging technology, AI, and big data analytics to improve tissue analysis accuracy, reduce costs, and develop personalized treatment plans. Embryology 4.0 uses advanced imaging, molecular biology, and bioinformatics to understand developmental biology and identify therapeutic targets for regenerative medicine. Both fields have potential applications in various areas of medicine and are expected to further advance with continued development of new technologies and approaches.
A semi-agnostic ansatz with variable structure for variational quantum algorithms

Abstract Quantum machine learning—and specifically Variational Quantum Algorithms (VQAs)—offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.
A method for stitching remote sensing images with Delaunay triangle feature constraints

The process of synthesizing multiple images into a seamless panoramic image is referred to as remote sensing image stitching. Existing studies focus less on the influence of topography on the appearance and texture of images and the perturbation of image spectra by topographic changes. This paper presents a remote sensing image stitching method that considers the impact of topography and geomorphology. First, the feature matching was optimized using the Euclidean distance similarity of texture features and the nearest neighbor distance ratio of feature points in remote sensing images as constraints. Then, the Delaunay triangle mesh of feature points in the image overlapping region was constructed, the geometric features of Delaunay triangles were used to optimize the triangle matching and reduce the matching redundancy, and the affine transformation matrix was solved based on the comprehensive consideration of the geometric features of Delaunay triangles and the texture features of the remote sensing images. Finally, the weighted fusion algorithm was applied to stitch and fuse the images. Three image datasets were selected for the experiments, one in which there were large terrain undulations in the imaging regions, one in which the main body of the imaging regions was water, and one in which the overall terrain of the imaging regions had relatively gentle slopes but obscuring features were also present. The results showed that the average correct rates of method feature matching were 89.78%, 94.99%, and 96.17%, which were the best for each algorithm, and the average feature matching times were 4.13 s, 8.27 s and 7.19 s. These times are much lower than those obtained with the APAP and AANAP algorithms and basically the same as those achieved with the SPHP and SURF algorithms. In terms of visual effect, the AG, DTenengrad, and DLaplacian indices of the proposed method were all significantly improved compared to the APAP, SPHP, AANAP, and SURF algorithms, the advantages were most obvious when dealing with datasets with large topographic relief in the imaging regions, with the maximum improvement of the AG, DTenengrad, and DLaplacian indices were 85.61%, 95.68%, and 93.12%, respectively. Therefore, it is possible to conclude that the proposed method is more suitable for remote sensing image stitching and fusion of different topographic and geomorphological conditions.
AI to Train AI: Using ChatGPT to Improve the Accuracy of a Therapeutic Dialogue System

In this work, we present the use of one artificial intelligence (AI) application (ChatGPT) to train another AI-based application. As the latter one, we show a dialogue system named Terabot, which was used in the therapy of psychiatric patients. Our study was motivated by the fact that for such a domain-specific system, it was difficult to acquire large real-life data samples to increase the training database: this would require recruiting more patients, which is both time-consuming and costly. To address this gap, we have employed a neural large language model: ChatGPT version 3.5, to generate data solely for training our dialogue system. During initial experiments, we identified intents that were most often misrecognized. Next, we fed ChatGPT with a series of prompts, which triggered the language model to generate numerous additional training entries, e.g., alternatives to the phrases that had been collected during initial experiments with healthy users. This way, we have enlarged the training dataset by 112%. In our case study, for testing, we used 2802 speech recordings originating from 32 psychiatric patients. As an evaluation metric, we used the accuracy of intent recognition. The speech samples were converted into text using automatic speech recognition (ASR). The analysis showed that the patients’ speech challenged the ASR module significantly, resulting in deteriorated speech recognition and, consequently, low accuracy of intent recognition. However, thanks to the augmentation of the training data with ChatGPT-generated data, the intent recognition accuracy increased by 13% relatively, reaching 86% in total. We also emulated the case of an error-free ASR and showed the impact of ASR misrecognitions on the intent recognition accuracy. Our study showcased the potential of using generative language models to develop other AI-based tools, such as dialogue systems.
Improvement of Road Instance Segmentation Algorithm Based on the Modified Mask R-CNN

Although the Mask region-based convolutional neural network (R-CNN) model possessed a dominant position for complex and variable road scene segmentation, some problems still existed, including insufficient feature expressive ability and low segmentation accuracy. To address these problems, a novel road scene segmentation algorithm based on the modified Mask R-CNN was proposed. The multi-scale backbone network, Res2Net, was utilized to replace the ResNet network, and aimed to improve the feature extraction capability. The soft non-maximum suppression algorithm with attenuation function (soft-NMS) was adopted to improve detection efficiency in the case of a higher overlap rate. The comparison analyses of partition accuracy for various models were performed on the adopted Cityscapes dataset. The results demonstrated that the modified Mask R-CNN effectively increased the segmentation accuracy, especially for small and highly overlapping objects. The adopted Res2Net and soft-NMS can effectively enhance the feature extraction and improve segmentation performance. The average accuracy of the modified Mask R-CNN model reached up to 0.321, and was 0.054 higher than Mask R-CNN. This work provides important guidance to design a more efficient road scene instance segmentation algorithm for further promoting the actual application in automatic driving systems.
Approximations of System W Between c-Inference, System Z, and Lexicographic Inference

Inductive inference operators have been introduced to formalize the process of completing a conditional belief base to a full inference relation. In this paper, we investigate the approximation of inductive inference operator system W with combinations of system Z (or equivalently rational closure) and c-inference, both of which are known to be extended by system W. We introduce general functions for generating inductive inference operators, the combination of two inductive inference operators by union, and the completion of an inductive inference operator by an arbitrary set of axioms. We construct the least inductive inference operator extending system Z and c-inference which, however, does not satisfy system P. We also construct the least inductive inference operator extending system Z and c-inference that also satisfies system P and show that it is strictly extended by system W. Furthermore, we develop approximations that extend system W and introduce an inductive inference operator that strictly extends system W and that is strictly extended by lexicographic inference. This leads to a map of inference relations between rational closure and c-inference on the one side and lexicographic inference on the other side.
Case Study of Low-Code VR Platform for Training and Evaluating Employee’s Service Skills

For service industry, quality of customer service skills and customer satisfaction are very important topic. However, it is difficult to learn and master this kind of service skill without on-the-job-training (OJT). Thus, we propose a service VR simulator in which the user can train man-to-man service by using VR technologies. Through interviews and preliminary experiments with various service companies, we found that a customer service training VR system is effective for training service skills. However, the work content of each industry is very different, and the contents to be trained are also diverse, so the development cost and difficulty of operation and customization were raised as problems. Therefore, we proposed and implemented a VR platform that enables low-code development with little need for programming. In addition, we developed several business training VR systems using the proposed platform and conducted interviews.
Virtual fashion experiences in virtual reality fashion show spaces

Virtual reality (VR) provides a new fashion space and fashion experience. This study focuses on immersive VR and fashion shows to empirically explore the VR fashion space and fashion experience. Insights specific to fashion have not been presented in as much depth in the literature; thus, the current findings are particularly valuable and insightful.This study employed three immersive VR (IVR) fashion show stimuli and in-depth interviews according to a semi-structured questionnaire. Collected data were analyzed based on the concept of VR space and VR experience derived through literature research.The VR fashion space was divided into three types and VR experiences of cognitive presence, sensible immersion, emotional immersion, and aesthetic interaction were derived accordingly. First, the physical representation of a fashion show induced a cognitive and emotional sense of presence, in which users felt as though they had moved to the same time and place as those at the fashion show. Second, participants experienced cognitive confusion owing to the differences with a priori experiences in the fashion show space (i.e., reality and imagination coexist). Third, participants transcended the limitations of physical reality while in the fashion show space of pataphysics (which was realized with human imagination), and they moved beyond the stage of confusion that is experienced while facing realistic objects to connect to creative inspiration.The difference in the properties of VR space may be associated with distinct VR fashion experiences. The findings suggest that (1) a priori elements such as sociocultural contexts and personal experiences differ in the experiential dimension of virtual space, (2) the VR fashion show space induces a psychological experience between brand and consumer, and (3) creative inspiration and exploratory play can be greatly induced in a user if the immersive fashion space is further from the original source.
No graph state is preparable in quantum networks with bipartite sources and no classical communication

Abstract In research concerning quantum networks, it is often assumed that the parties can classically communicate with each other. However, classical communication might introduce a substantial delay to the network, especially if it is large. As the latency of a network is one of its most important characteristics, it is interesting to consider quantum networks in which parties cannot communicate classically and ask what limitations this assumption imposes on the possibility of preparing multipartite states in such networks. We show that graph states of an arbitrary prime local dimension known for their numerous applications in quantum information cannot be generated in a quantum network in which parties are connected via sources of bipartite quantum states and the classical communication is replaced by some pre-shared classical correlations. We then generalise our result to arbitrary quantum states that are sufficiently close to graph states.
AI-Enabled Sustainable Development: An Intelligent Interactive Quotes Chatbot System Utilizing IoT and ML

A chatbot is computer software that uses artificial intelligence to communicate with people in their native tongues. These chatbots often speak by text or audio, and they are adept at mimicking human speech to interact with people in a way that is human-like. One of the most effective uses of natural language processing is a chatbot. The main purpose of chat bots is to offer customer service. It assists in serving a sizable target audience simultaneously and around-the-clock. May broadcast newsletters, auto-sequences, and plan meetings. It can also gather leads from comments. Create dialog-based forms and save all of the data in spreadsheets. A chatbot can be used to purchase food, propose products, provide customer service, provide weather information, help with personal finances, plan meetings, find and track flights, send money, and many other things.
The emotional infrastructure of a cybercrime collective: Evidence from Dark0de

Complex cybercrime markets face collective action problems. As they involve disparate networks of individuals, they cannot use in person persuasion or coercion to ensure cooperation. They face a tension between being open to new members and opportunities, and regulating participation. We propose that collective emotional regulation plays a crucial part in managing members’ behaviours within illicit marketspaces. We take one critical case, Dark0de, which was a leading English language cybercrime market. Drawing on a publicly available dataset of internal discussions, we use Qualitative Thematic Content Analysis and Conversational Analysis to investigate how through mutual emotion regulation, this cybercrime collective managed collective action dilemmas deriving from the context of its activity, containing conflict among members and fostering cooperation along with competition. We conclude that emotional micro-dynamics are key to maintaining cybercriminal marketplaces as relatively stable communities, circumscribing individuals’ actions and aligning them with emergent normative orders, enabling those communities to remain operable in adverse environments. Dark0de can be seen as a representative case for a category of digital environments where the community develops its own emotional ethnopsychology which uses displays of semi-ironic abuse and attack along with cooperation on emerging projects.