Reviewerly/exHarmony huggingface dataset:
A Blockchain-Centric Data Sharing Framework for Building Trust in Healthcare Insurance

Data sharing is very important in the healthcare insurance industry, given that a premium claim often needs data from multi-parties, such as patients, hospitals, banks, and insurance companies. Generally, data sharing only occurs when sufficient trust exists among the multiple parties involved with the claim. However, since fierce competition and lack of trust may exist among some of these parties, willingness of data sharing may be severely restricted in healthcare insurance. Furthermore, healthcare data are private and sensitive. Risk of privacy disclosure is also a major problem we must consider for managing shared data in healthcare insurance. As blockchain has been proposed in recent years as a potential solution to enable multi-party trust, we develop in this article a blockchain-centric data sharing framework to resolve the trust problem in the context of healthcare insurance. Specifically, we construct a trusted data sharing platform built on blockchain where each party uploads their encrypted data to the blockchain while the immutability of blockchain prevents any data tampering and the traceability of blockchain helps identify attacks to the data immediately after they happen. Moreover, we also develop zero-knowledge-proof mechanisms to ensure that data verification can be made without any disclosure of data specifics.
Blockchain-Based Dynamic Consent for Healthcare and Research

As individuals gain greater access control over their health information, dynamic consent solutions are increasingly offered to allow individuals to make informed choices about their permissions over time. Blockchain-based tools and technologies are emerging to enhance the capabilities of dynamic consent solutions to offer individuals more engagement. While blockchain-based systems cannot replace all human interactions, blockchain features can increase granularity, transparency, and trust. This chapter describes the benefits and drawbacks of dynamic, informed consent and proposes several design and feature considerations to optimize blockchain-based features.
Blockchain Implementation for Decentralized Real-World Research

The goal of this chapter is to demonstrate that a patient centric and data-driven approach are required to develop blockchain-based health applications that are useful for patients and healthcare providers. Bowhead Health, describes a case study of such an application to identify people with migraines that required a community-building educational approach. We first describe how a private, Ethereum blockchain platform within the pharmaceutical and consumer health sectors was implemented, including its objectives, key features, adaptations, and implementation steps. Next, we discuss our key learnings and the challenges faced in implementing the blockchain health data case study. In particular, we explore the importance of the community building and education approach that was used as an adjunct tool to support the mindset shifts necessary for blockchain applications to have a beneficial impact in healthcare.
Generative AI and Simulation Modeling: How Should You (Not) Use Large Language Models Like ChatGPT

Generative Artificial Intelligence (AI) tools, such as Large Language Models (LLMs) and chatbots like ChatGPT, hold promise for advancing simulation modeling in various domains. Despite their growing prominence and associated debates, there remains a gap in comprehending the potential of generative AI in this field and a lack of guidelines for its effective deployment. This paper endeavors to bridge these gaps. We discuss the applications of ChatGPT through an example of modeling COVID-19’s impact on economic growth in the United States. Although we utilize ChatGPT, our guidelines are generic and can be applied to a broader range of generative AI tools and platforms. Our work presents a systematic approach for integrating generative AI across the simulation research continuum, from problem articulation to insight derivation and documentation, independent of the specific simulation modeling method chosen. We emphasize that while these tools offer enhancements in refining ideas and expediting processes, they should complement rather than replace critical thinking inherent to research.
Design and Implementation Considerations for Blockchain for Health Records

This chapter provides the motivation for using blockchain-based systems for health records as the currently prevalent electronic health records are inadequate for providing complete care and a smooth experience. The organization of such systems is discussed for developers intending to proceed on this path. Real examples from multiple such systems indicate that the challenges faced by developers in creating and deploying successful systems are not trivial. The issues are described here in depth and the corresponding solutions are discussed.
Optimization of the 2P fifth degree convolution kernel in the spectral domain

The first part of the paper describes a two-parameter (2P) fifth-order interpolation kernel, r. After that, from the 2P kernel, the kernel components were created. By applying the Fourier transformation to each kernel component, the spectral components of the 2P kernel were obtained. The spectral characteristic of the 2P kernel, H, was created from the spectral components. After that, the algorithm, that optimizes the parameters of the 2P kernel so as to eliminate the ripple of the spectral characteristics, is described. The optimization was performed in such a way that the spectral characteristic developed in the Taylor series, H T. With the condition for the elimination of the members of the Tylor series, which greatly affect the ripple of the spectral characteristic, the optimal kernel parameters (a opt , b opt) were determined. The second part of the paper describes an Experiment, in which the interpolation accuracy of the 2P kernel was tested. Convolution interpolation, with the 2P kernel, was performed over the signals from the Test base. The Test base is created with musical signals. By analyzing the interpolation error, which is represented by the Mean Square Error, MSE, the precision of the interpolation was determined. The results (a opt , b opt , MSE min) are presented on tables and graphs. Detailed comparative analysis showed higher interpolation precision with the proposed 2P interpolation kernel, compared to the interpolation precision with, 1P interpolation kernel. Finally, the numerical values of the optimal kernel parameters, which are determined by the optimization algorithm proposed in this paper, were experimentally verified.
Verification of generality from weight analysis of Integration Neural Network approximators

Multiple regression analysis, neural networks (NN), and other methods are used as approximate computation methods for physical phenomena. However, multiple regression analysis has the problem of overlearning due to factors that cannot be formulated, and NN has the problem of overlearning due to the diversity of its representational capabilities. In response to this problem, we have proposed an integrated neural network (INN) that integrates a simple perceptron and an NN, which mimic multiple regression analysis. In this paper, we show that INN can reduce overlearning of the simple perceptron part (multiple regression analysis) and achieve higher accuracy by compensating for errors caused by NNs through weight analysis. Furthermore, it was revealed that the correction of the NN part is effective under adaptive control according to the approximation accuracy of the simple perceptron part.
APPLICATION OF GENERATIVE FUNCTIONS TO THE PROBLEMS OF MAXIMUM CHESS ARRANGEMENTS OF N FIGURES

A generating function is a formal structure that is closely related to a numerical sequence, but allows us to manipulate the sequence as a single entity, with the goal of understanding it better. Roughly speaking, generating functions transform problems about sequences into problems about functions. They provide a systematic way to encode sequences of numbers or other combinatorial objects, allowing for elegant solutions to complex problems across diverse mathematical domains. In this article, we will approach a range of problems, involving placing
The Inter-Organizational Environment of Blockchain in Healthcare: The State of Blockchain Healthcare Consortia

Investment and attention to blockchain burgeoned since 2019 and healthcare is one active industry experimenting with this novel technology. Among the projects in the healthcare blockchain sphere, the most prevalent theme is the booming of healthcare blockchain consortia. To explore the growing partnership activities in the healthcare blockchain sphere, we conducted a content analysis of collected archival data from public sources to address two research questions: What are these healthcare blockchain consortia and what are the potential benefits and challenges to these consortia? We found that healthcare organizations activities in forming consortia are largely unstructured. Information about their members, visions, objectives, and agenda is fragmentary, elusive, and sometimes confusing; most consortia are small size with less than or equal to 5 members and most organization join one consortia. Insights from strategic alliances and the emerging blockchain governance literature suggest that these consortia might face several organizational challenges to mature, especially when they are not prepared for the novel decentralized governance of blockchain consortia.
Blockchain Architecture for the Healthcare Ecosystem

The healthcare industry presents special challenges as an ecosystem due to the nature of incentives and goals of the various participants such as patients, payers, hospitals, doctors, insurers, pharmacies, pharma companies, governmental bodies, and regulators. Despite its importance to the quality of life, it is plagued by inefficient processes, duplications, and delays. While blockchain is not a panacea for all of healthcare’s issues, it can go a long way in removing friction in data transactions, ensuring integrity of drugs and prescriptions, protecting patient data, making high-quality data available for research or case-based analysis, preventing fraud, and mitigating conflicting incentives. Blockchain’s native capabilities of consensus-driven record of data, immutability of data, smart contracts for fluidity in processing, and ensuring authorization-driven access (with immutable audit trail) are tailor-made for moving healthcare into the next generation of sophistication. We recommend an approach to blockchain architecture driven by the business requirements of the healthcare ecosystem that leverages these native blockchain capabilities. We discuss the components of flexibility, trade-off, and safety.
Quantum Machine Learning for Biomedical Data Analysis

The emerging and novel field of utilizing quantum physics in the world of machine learning, with applications customized for biomedical data, is explored in detail in this chapter. The discussion begins with an introduction to quantum physics and machine learning, before going on to explain how the two fields can work together to revolutionize biomedical data processing. Quantum-enhanced algorithms and their ability to process massive and intricate biomedical datasets are discussed in depth. In applications such as protein folding prediction, genomic data processing, and real-time diagnostics, the inherent parallelism and superposition capabilities of quantum machine learning are on full display. Finally, the technical and ethical difficulties of combining quantum machine learning with biomedical data are assessed in depth. The authors also provide an outlook on the promising interdisciplinary subject of quantum-powered machine learning and its potential to radically alter the field of biomedical research and healthcare solutions.
Enhancing Industrial Robotics Performance and Security With AI and Blockchain Technologies

Industrial robotics are becoming more widely used, but their performance and security must be urgently enhanced to satisfy the needs of contemporary industrial contexts. This chapter focuses on how AI and blockchain technology might improve industrial robotic systems' performance while guaranteeing strong security precautions. The capabilities of industrial robots are greatly enhanced by AI technologies. Robots may improve their performance, gain new abilities, and adapt to changing circumstances by utilising cutting-edge machine learning techniques. Robots may learn from their experiences thanks to the incorporation of AI, which improves their operational effectiveness, precision, and decision-making abilities. AI enables robots to optimise their performance, spot anomalies, and proactively resolve potential difficulties, resulting in increased production and less downtime. This is done through real-time data analysis and predictive analytics. Incorporating blockchain technology also provides an industrial robotics system with a safe and open framework.
AI in Market Research

This systematic review examines the role of artificial intelligence (AI) in market research and its impact on customer insights. While existing research has extensively explored AI's influence on marketing, education, and customer experience, a noticeable gap remains in understanding AI's role in reshaping the market research industry. This study aims to fill that gap by focusing on the unexplored territory of AI-driven market research and customer insights. The review follows a structured methodology, analyzing relevant research articles published between 2000 and 2023 using the TCCM framework. The findings demonstrate AI's significant impact on customer experiences, market segmentation, and personalized services across various industries. By embracing AI's capabilities, businesses can thrive in the data-driven and competitive landscape, forging lasting connections with their target audiences and driving success in the dynamic world of business.
Building a Secure and Transparent Online Conversation Platform

This chapter explores the integration of artificial intelligence (AI) and blockchain technology to create a secure and transparent online conversation platform. Advancements in AI have given rise to virtual assistants capable of human-like interactions and versatile task execution. By harnessing blockchain technology, these virtual assistants enhance security and response times, making them invaluable in applications ranging from healthcare to gaming. The chapter presents a project that enhances virtual assistant capabilities through blockchain integration, featuring a self-learning module and a Solana-based blockchain network with smart contracts. Implementation employs Vanilla JavaScript, Express.js, and Node.js, offering end-to-end customer support and adapting to modern lifestyles. The fusion of AI and blockchain showcases the potential for a robust and secure online conversation platform.
Blockchain and Artificial Intelligence for Cyber Security in the Era of Internet of Things and Industrial Internet of Things Applications

As the internet of things (IoT) and industrial internet of things (IIoT) continue to expand, the need for robust cyber security solutions becomes increasingly critical. The convergence of blockchain technology and artificial intelligence (AI) offers promising opportunities to address the security challenges posed by IoT and IIoT applications. This chapter provides an overview of the potential synergies between blockchain and AI in the context of cyber security for IoT and IIoT. Blockchain technology, with its decentralized and immutable nature, can provide enhanced security and trust in IoT and IIoT networks. It offers features such as data integrity, transparency, and tamper resistance, making it well-suited for securing critical data and transactions. Additionally, blockchain can facilitate secure device identity management, access control, and secure communication among IoT and IIoT devices.
Blockchain Technology

Intelligent internet of vehicles is an emerging technology and has an impact on all smart vehicles on the road due to rapid development in satellite communication integrated with internet technology and enables the integration of smart vehicles with system components like inbuilt vehicle sensors and internet, surrounding vehicles, and smart devices. This integration creates a safe and better platform for vehicles to travel on the road and at the same time be connected to the surrounding environment and vehicles for better performance. Integrating blockchain technology with vehicular communication and smart vehicles is the latest emerging concept. In this chapter, the authors present the survey and latest advancement in block chain technology for intelligent transport systems. This chapter's main highlight is on the application scenario of the internet of vehicles using block chain after a decent review of the literature survey. Also, the authors focus on key challenges and explore further research paths in the intelligent internet of vehicles using block chain technology.
Explaining the Challenges of Accountability in Machine Learning Systems Beyond Technical Obstacles

The ability to make a note regarding machine learning systems decisions for people is becoming increasingly sought after, particularly in situations where decisions have significant repercussions for those affected and where capability in terms of maintaining is required. To increase comprehension based on referred to as “black box” mechanism, explaining ability is frequently cited as a technical obstacle in the design of ML systems and decision procedures. The quantities that ML systems aim to optimize must be specified by their users. This leads to the revealing of policy trade-offs that may have previously been hidden or implicit. Important decisions, as well as judgments, help what may need to be explicitly discussed in public debate as ML's use in policy expands.
A Web Semantic-Based Text Analysis Approach for Enhancing Named Entity Recognition Using PU-Learning and Negative Sampling

The NER task is largely developed based on well-annotated data. However, in many scenarios, the entities may not be fully annotated, leading to serious performance degradation. To address this issue, the authors propose a robust NER approach that combines a novel PU-learning algorithm and negative sampling. Unlike many existing studies, the proposed method adopts a two-step procedure for handling unlabeled entities, thereby enhancing its capability to mitigate the impact of such entities. Moreover, this algorithm demonstrates high versatility and can be integrated into any token-level NER model with ease. The effectiveness of the proposed method is verified on several classic NER models and datasets, demonstrating its strong ability to handle unlabeled entities. Finally, the authors achieve competitive performances on synthetic and real-world datasets.
E-Commerce Review Sentiment Analysis and Purchase Intention Prediction Based on Deep Learning Technology

This study proposes a deep learning-based analytical model to conduct an in-depth study of the relationship between consumer trust, perceived benefits, and purchase intention. This model combines natural language processing and sentiment analysis, using the BERT-LSTNet-Softmax model to extract textual features in reviews and perform temporal predictions of consumer sentiment and purchase intention. Experimental results show that this model achieves excellent performance in the e-commerce field and provides a powerful tool for in-depth understanding of consumer purchasing decisions. This research promotes the application of deep learning technology in the field of e-commerce, helps to improve the accuracy of consumer purchase intentions, and provides more support for the development of the e-commerce market and consumer decision-making.
AI and Blockchain for Industrial Robotics

The fusion of artificial intelligence (AI) and blockchain technology holds the promise of a profound transformation in the realm of industrial robotics. This amalgamation has the potential to usher in a new era characterized by heightened automation, transparency, and trust within production and supply chain processes. Nevertheless, as is often the case with technological advancements of this magnitude, it is imperative to recognize and address several ethical, legal, and social concerns that accompany this paradigm shift.
Cross-Checking-Based Trademark Image Retrieval for Hot Company Detection

A trademark is an essential symbol of a company, consisting of a semantically rich image under ordinary circumstances. The popularity of a company can be measured by the frequency of its trademark being used. Therefore, efficiently retrieving trademark images would directly contribute to the detection of popular companies. However, most mainstream retrieval methods are not especially pertinent to trademark image retrieval. To solve this problem, a combination of the ResNet50 network and Autoencoder with local sensitive hashing (LSH) is used to conduct full cross-checking, which significantly improves the effectiveness of trademark image retrieval. Meanwhile, image super-resolution-based sparse coding is also proposed to achieve high-precision trademark image retrieval and its effect is particularly significant for challenging trademark images. Finally, the authors conduct extensive experiments on a high-quality database to demonstrate the substantial effectiveness of the proposed methods.
Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits

In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.
Machine Learning and Blockchain Integration in Industrial Robotics

Machine learning and blockchain have the potential to completely change the landscape of industrial robots. Learning and adaptation give robots an edge in functionality and independence. Blockchain, on the other hand, provides a decentralized and secure platform for information exchange and transaction verification. In this chapter, the authors look at the potential benefits and drawbacks of integrating machine learning and blockchain technology for use in manufacturing robots. The study begins with a brief introduction to machine learning, blockchain, and their respective industrial robot applications. Some of the possibilities and advantages that will be covered in the next sections include better data security, more transparent and auditable decision making, and the use of decentralized control systems. The importance of collaboration between academic institutions, businesses, and government agencies is emphasized in order to speed up the process of mainstreaming machine learning and blockchain integration in industrial robots.