Reviewerly/exHarmony huggingface dataset:
Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets

The widespread adoption of photovoltaic (PV) technology for renewable energy necessitates accurate segmentation of PV panels to estimate installation capacity. However, achieving highly efficient and precise segmentation methods remains a pressing challenge. Recent advancements in artificial intelligence and remote sensing techniques have shown promise in PV segmentation. Nevertheless, real-world scenarios introduce complexities such as diverse sensing platforms, sensors, panel categories, and testing regions. These factors contribute to resolution, size, and foreground-background class imbalances, impeding accurate and generalized PV panel segmentation over large areas. To address these challenges, we propose GenPV, a deep learning model that leverages data distribution analysis and PV panel characteristics to enhance segmentation accuracy and generalization. GenPV employs a multi-scale feature learning approach, utilizing an enhanced feature pyramid network to fuse data features from multiple resolutions, effectively addressing resolution imbalance. Moreover, inductive learning is employed through a multitask approach, facilitating the detection and identification of both small and large-sized PV panels to mitigate size imbalance. To address significant class imbalance in PV panel recognition tasks, we integrate the Focal loss function for effective hard sample mining. Through experimental evaluation conducted in Heilbronn, Germany, our proposed method demonstrates superior performance compared to state-of-the-art approaches in PV panel segmentation. The results exhibit progressively higher accuracy and improved generalization capability. These findings highlight the potential of our method to serve as an advanced and practical tool for PV segmentation in the renewable energy field.
How Visual Design of Severe Weather Outlooks Can Affect Communication and Decision-Making

Abstract Multiday severe weather outlooks can inform planning beyond the hour-to-day windows of warnings and watches. Outlooks can be complex to visualize, as they represent large-scale weather phenomena overlapping across several days at varying levels of uncertainty. Here, we present the results of a survey ( n = 417) that explores how visual variables affect comprehension, inferences, and intended decision-making in a hypothetical scenario with the New Zealand MetService Severe Weather Outlook. We propose that visualization of the time window, forecast area, icons, and uncertainty can influence perceptions and decision-making based on four key findings. First, composite-style outlooks that depict multiple days of weather on one map can lead to biased perceptions of the forecast. When responding to questions about a day for which participants accurately reported there was no severe weather forecast, those who viewed a composite outlook reported higher likelihoods of severe weather occurring, higher levels of concern about travel, and higher likelihoods of changing plans compared to those who viewed outlooks that showed weather for each day on a separate map, suggesting that they perceived the forecast to underrepresent the likelihood of severe weather on that day. Second, presenting uncertainty in an extrinsic way (e.g., “low”) can lead to more accurate estimates of likelihood than intrinsic formats (e.g., hue variation). Third, shaded forecast areas may lead to higher levels of confidence in the forecast than outlined forecast areas. Fourth, inclusion of weather icons can improve comprehension in some conditions. The results demonstrate how visualization can affect decision-making about severe weather and support several evidence-based considerations for effective design of long-term forecasts. Significance Statement Severe weather outlook forecasts can be hard to clearly communicate because they show multiple weather patterns across multiple days and regions with varying uncertainty. The purpose of this study is to explore how visual elements of outlook design affect the way that people understand this complex content. We had three separate groups respond to the same series of questions while viewing different modified versions of the MetService Severe Weather Outlook in Aotearoa New Zealand and compared their responses. We find that the way the outlooks’ time window, forecast area, icons, and uncertainty are visualized can influence how people understand outlooks and make inferences and decisions about severe weather. We discuss how these influences may impact communication and action and present several evidence-based considerations for effective outlook design.
Augmenting inertial motion capture with SLAM using EKF and SRUKF data fusion algorithms

This paper proposes quaternion-based extended and square-root unscented Kalman filters to estimate human body segment positions and orientations using low-cost IMUs in conjunction with a SLAM method. The Kalman filters use measurements based on SLAM output, multilink biomechanical constraints, and vertical referencing to correct errors. In addition to the sensor biases, the fusion algorithm is capable of estimating link geometries, allowing the imposition of biomechanical constraints without a priori knowledge of sensor positions. The proposed algorithms achieve up to 5.87 (cm) and 1.1 (deg) accuracy in position and attitude estimation in various scenarios of human arm movements. Compared to the EKF, the SRUKF algorithm presents a smoother and higher convergence rate but is 2.4 times more computationally demanding. After convergence, the SRUKF is up to 17% less and 36% more accurate than the EKF in position and attitude estimation, respectively.
Multi-modal spatial relational attention networks for visual question answering

Visual Question Answering (VQA) is a task that requires VQA model to fully understand the visual information of the image and the language information of the question, and then combine both to provide an answer. Recently, a large amount of VQA approaches focus on modeling intra- and inter-modal interactions with respect to vision and language using a deep modular co-attention network, which can achieve a good performance. Despite their benefits, they also have their limitations. First, the question representation is obtained through Glove word embeddings and Recurrent Neural Network, which may not be sufficient to capture the intricate semantics of the question features. Second, they mostly use visual appearance features extracted by Faster R-CNN to interact with language features, and they ignore important spatial relations between objects in images, resulting in incomplete use of image information. To overcome the limitations of previous methods, we propose a novel Multi-modal Spatial Relation Attention Network (MSRAN) for VQA, which can introduce spatial relationships between objects to fully utilize the image information, thus improving the performance of VQA. In order to achieve the above, we design two types of spatial relational attention modules to comprehensively explore the attention schemes: (i) Self-Attention based on Explicit Spatial Relation (SA-ESR) module that explores geometric relationships between objects explicitly; and (ii) Self-Attention based on Implicit Spatial Relation (SA-ISR) module that can capture the hidden dynamic relationships between objects by using spatial relationship. Moreover, the pre-training model BERT, which replaces Glove word embeddings and Recurrent Neural Network, is applied to MSRAN in order to obtain the better question representation. Extensive experiments on two large benchmark datasets, VQA 2.0 and GQA, demonstrate that our proposed model achieves the state-of-the-art performance.
Leveraging Artificial Intelligence for Enhancing Customer Experience and Efficiency in the Banking Industry

AI is a revolutionary force that could disrupt several sectors. This chapter addresses key questions about AI's impact on banking. It examines natural language processing, machine learning, and predictive analytics in finance. The study aims to elucidate how AI can enhance the banking experience for customers by exploring its role in personalized services, customer support, and fraud prevention. Further, it offers a comprehensive analysis of the pros and cons of utilizing AI in banking operations, illuminating the potential ethical and societal difficulties that may arise as a result. The research examines how AI can improve banking efficiency, transforming the business. The study's main objective is to inform industry stakeholders, policymakers, and researchers on the pros and downsides of AI adoption in this vital sector by addressing the significant AI in banking issues. This study sets the stage for a comprehensive exploration of AI's transformative potential in the banking landscape.
Artificial Intelligence Applications in Human Resource Management

Human resource is facing a transformation challenge with artificial intelligence(AI). AI's position is larger in various human resource (HR) functions where robotics companies are able to handle recruiting, employing, analyzing data, collecting data, reducing workplace workload, and improving workplace performance. To report this gap, the current study purposes to head to the digital revolution by means of various methods to streamline resources through usage of big data analytics, AI, and cloud computing. The prime role of this research is to address the ensuing research on significant journals, keywords, and affiliations, authors, countries, elevating the literature on AI in human resource management (HRM) using bibliometric content analysis approach. Four prominent themes emerged in the literature which give directions on the role of information systems in building the framework for AI, machine learning applications, decision making, and handling the recruitment process with neural networks. Further, the current research recommends several suggestions for the part of future direction AI in HRM.
The Managerial Ethical and Operational Challenges of Hospital Cybersecurity

The COVID-19 pandemic has presented healthcare institutions with unprecedented challenges in maintaining robust cybersecurity practices to safeguard patient data and protect critical healthcare operations. This chapter explores the ethical, organizational cultural, and leadership complexities hospitals face in cybersecurity during and after the pandemic. Ethical considerations encompass patient privacy, informed consent, and the responsible use of emerging technologies. Organizational cultural challenges involve balancing patient care priorities with cybersecurity efforts, fostering a cybersecurity-aware culture, and mitigating insider threats. Drawing insights from real-world experiences and expert opinions, this chapter sheds light on the multifaceted nature of hospital cybersecurity challenges amidst a global crisis. This chapter explores the nature of these challenges through a management consultant hospital intervention in a hospital that has experienced a significant cybersecurity breach.
A Convolutional Neural Network Approach for Crowd Counting

Crowd counting is a crucial computer vision task with numerous practical applications. Convolutional neural networks (CNNs) are a common foundation for crowd counting. This approach makes use of deep learning to automatically learn attributes from photos that can be used to gauge the crowd size. In order to forecast the density map of a given crowd image, a network is often trained as part of the CNN-based crowd-counting method. The crowd density at each spot in the image is continuously estimated by the density map. The density map is integrated over the full image to produce the final crowd count. The CNN architecture has undergone a number of tweaks and improvements to further demonstrate the method's robustness. In general, CNN-based crowd-counting has yielded positive results and is a viable method for automatic crowd counting in a variety of settings. When employing CNNs to count crowds, there are a number of obstacles that must be overcome. It might be difficult to determine the precise number of individuals present due to crowds' variable densities and sizes. The task might also be made more difficult by occlusions and perspective distortions. Several CNN architectures designed specifically for crowd counting have been developed to address these issues. These architectures frequently include supplementary modules, such as density estimation and multi-scale feature extraction, to boost accuracy.
Predicting User Likes in Online Shopping Using Artificial Intelligence and Big Data Analytics

There are now an absurdly large number of available products as a result of the expansion of e-commerce, making it challenging for customers to find and choose goods would enjoy. To assist customers in finding items that suit their interests, online shops have turned to recommender systems. Traditional recommender systems do, however, have constraints that AI and Big Data Analytics can get beyond. This study uses AI and Big Data Analytics to forecast customer preferences for online buying.
Review on Vision Based Human Motion Detection Using Deep Learning

The challenging problems addressed by vision-based human motion recognition and its wide range of potential applications have captured the interest of numerous researchers. These applications encompass simple gesture recognition to intricate analysis of behavior in surveillance systems. Since there was a need for practical, automatic violence detection techniques that looked at the visual information obtained from security cameras placed throughout various locations, the violence detection sector became quite popular in the domain of human motion detection systems. As a result, there is a significant advancement in the methods for representing and recognizing human motion. The focus of this study is to discuss the recent advancements made by researchers in these fields. It offers a comprehensive analysis of the methodologies employed and evaluates the credibility of the various submissions. Additionally, the article offers guidelines on approaching future directions for this application.
A Comprehensive Survey on Naive Bayes Algorithm: Advantages, Limitations and Applications

A well-known and frequently used machine learning method, the Naive Bayes algorithm has attracted a lot of interest in a number of fields. The Naive Bayes algorithm, along with its benefits, drawbacks, and practical applications, will be covering in this survey study. The Naive Bayes algorithm's fundamentals are first studied in the survey, with special attention paid to how heavily it relies on probability theory and the notion of feature independence. The Naive Bayes method is further examined, as well as its applicability to both continuous and discrete data. The study then looks into the benefits of the Naive Bayes method, emphasizing its tolerance to irrelevant characteristics, great computing efficiency, and capacity for handling high- dimensional data. The algorithm's interpretability and ability to work with different application domains are also highlighted. The survey article looks at various restrictions, such as the feature independence assumption and algorithm's sensitivity to missing data and its incapacity to recognize intricate correlations between variables. The survey article offers a variety of real-world situations where the Naive Bayes algorithm has been effectively used in order to give a thorough viewpoint. Text categorization, spam filtering, sentiment analysis, recommender systems, medical diagnosis, and financial fraud detection are some of these uses. This survey article provides academics, practitioners, and enthusiasts with an invaluable resource for comprehending the Naive Bayes algorithm's capabilities and concerns by compiling the algorithm's benefits, drawbacks, and applications. It serves as a starting point for further investigation and the creation of enhanced versions and modifications of the Naive Bayes algorithm.
Enabling Secure and Efficient Traffic Data Sharing in VANETs Through a Scalable Blockchain Framework

In this study, a novel architecture for managing road traffic events in Vehicular Ad hoc Networks (VANETs) is presented. The system makes use of a permissioned blockchain and micro-transactions to reduce the amount of communication and storage overhead required. In order to evaluate the usefulness of micro-transactions and compare the results of these simulations with the relevant literature, the former is carried out. The integrity and traceability of road traffic records are safeguarded by the approach that has been presented, and its potentially useful performance in a variety of contexts has been proved. Moreover, to further enhance the system, a machine learning algorithm is integrated into the blockchain framework, enabling intelligent data analysis and decision-making capabilities. The integration of machine learning with blockchain technology opens up new avenues for addressing security and efficiency challenges in traffic-related data sharing in VANETs.
Detecting Tags for Real-Time Objects in Images Using the Yolo-V3 Algorithm

Real-time object recognition can be a vast, erratic, and complex field of computer vision. The goal is to develop an object detection and counting system using deep learning and image processing algorithms. The whole thing is to develop a software system that requires a single video or image stream. It includes the following parts: The background has no moving objects, and the stage has moving objects. This system determines the difference between moving objects and the number of elements transmitted within the video scene. The experiment was conducted to obtain the following characteristics: Easy to use, to demonstrate that the system can verify object detection and count under specific conditions. Effective, to show that the system operates with high accuracy.
ChatGPT For Marketing Communications: Friend or Foe?

The release of the ChatGPT chatbot in November 2022 received significant public attention. ChatGPT is an Artificial Intelligence (AI) powered chatbot that allows users to simulate human-like conversations with AI. GPT stands for Generative Pre-trained Transformer, a language processing model developed by the American artificial intelligence company OpenAI. These innovations and technologies are changing business interests, revolutionizing marketing communications strategies, and enhancing the consumer experience. ChatGPT is a powerful tool for marketers, but we need to understand the risks and place realistic expectations for the moment. The author uses data collection techniques using literature studies and observations on ChatGPT about their potential and impact on marketing communications. Authors analyze brand information, data search, reference services, cataloging, content creation, and ethical considerations such as privacy and bias. Result of this study, ChatGPT can provide and support creative content creation or copywriting, improve customer service, automate repetitive tasks, and support data analysis. However, humans are irreplaceable for examining outputs and creating marketing messages consistent with a company's strategy and brand vision. With good marketing strategies, ChatGPT can effectively enhance and support marketing processes.
Bayesian Optimization for QAOA

The Quantum Approximate Optimization Algorithm (QAOA) adopts a hybrid quantum-classical approach to find approximate solutions to variational optimization problems. In fact, it relies on a classical subroutine to optimize the parameters of a quantum circuit. In this work we present a Bayesian optimization procedure to fulfil this optimization task, and we investigate its performance in comparison with other global optimizers. We show that our approach allows for a significant reduction in the number of calls to the quantum circuit, which is typically the most expensive part of the QAOA. We demonstrate that our method works well also in the regime of slow circuit repetition rates, and that few measurements of the quantum ansatz would already suffice to achieve a good estimate of the energy. In addition, we study the performance of our method in the presence of noise at gate level, and we find that for low circuit depths it is robust against noise. Our results suggest that the method proposed here is a promising framework to leverage the hybrid nature of QAOA on the noisy intermediate-scale quantum devices.
Cognitive Relationship-Based Approach for Urdu Sarcasm and Sentiment Classification

Humans have a natural tendency to express their emotions, but they are also skilled at using sarcasm to shape their feelings. In cognitive computing and natural language processing research, sentiment analysis and sarcasm detection are typically treated as separate tasks, with each text analyzed in isolation. However, this approach overlooks the connection between sentiment and sarcasm. We believe that sentiment and sarcasm are closely related and should be analyzed together to achieve a better understanding of context and natural language. In this paper, we propose a new framework that leverages the Cognitive Relationship (CR) between sarcasm and sentiment to improve the accuracy of classification. By taking into account the relationship between these two factors, we can achieve better results in sentiment analysis and sarcasm detection. We have also created a new and nearly balanced dataset for sentiment and sarcasm classification in standard Urdu that contains 7,000 tweets, which make up over 210K tokens. To gain a better understanding of the data, we conducted exploratory data analysis on words, hashtags, and emojis. The proposed methodology conducted a variety of classical machine learning classifiers and tested them with different variations of the dataset. After a thorough analysis of the results and errors, we found that the CR-based approach for sarcasm and sentiment classification performed better than the traditional stand-alone (SA) approach. Among the classifiers, Linear Regression and eXtreme Gradient Boosting proved to be the most effective. The sentiment classification based on CR has demonstrated a 9.3% enhancement compared to the stand-alone (SA) method while maintaining an overall improvement of approximately 22% compared to the baseline distribution. In the same way, the sarcasm classification based on CR has shown a 9.1% improvement over the SA approach and approximately 23.6% improvement over the baseline distribution.
DSP-Net: A Dynamic Spectral–Spatial Joint Perception Network for Hyperspectral Target Tracking

In order to effectively utilize spectral and object spatial information to improve tracking performance, we design an Hyperspectral Video (HSV) tracker, namely DSP-Net, to integrate the various prior information. The gradient difference between spectral vectors is explored to develop a clustering technique. The approach generates a binary mask containing target spectral information and appearance clues. A feature cache is introduced to store historical information. Additionally, the channel shift operation is used on the timing to capture the trajectory clues of the target. With the help of the non-local mechanism, the trajectory clues, appearance clues and spectral information of the target are finally integrated, using the designed spectral-spatial joint perception module to enhance the expression of the target. Experimental results show that DSP-Net outperforms state-of-the-art HSV trackers on existing dataset.