Analysis of Social Public Management Guarantee Mode Based on Big Data Algorithm

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

With the improvement of the material standard of living, the demand of the people for spiritual culture continues to increase. In terms of tourism, people have gradually shifted from simple tourism needs to integrated tourism needs. Tourism has become an effective way for people to expand their horizons and enrich their spiritual world. Tourism is one of the first industries to apply network technology. After long‐term exploration and innovation, tourism e‐commerce has developed rapidly. Coupled with the advent of the era of big data (EBD), the concept of customized tourism has gradually entered people’s lives. This paper mainly introduces the research on the impact of e‐commerce tourism on custom tourism in the EBD and intends to provide some ideas and directions for the good development of custom tourism. This paper proposes a research strategy on the impact of tourism e‐commerce on customized tourism in the EBD, including related theoretical research methods, random forest algorithms, support vector machine classification algorithms, and Bayesian estimation algorithms, which are used to customize tourism e‐commerce in the EBD, and research experiment on the impact of tourism. The experimental results show that 79.84% of customers are willing to purchase related products again after experiencing travel customization services using big data technology. By using the various characteristics of tourism big data for data mining and classification, it provides users with personalized travel search services. At the same time, big data technology can provide basic technical support for customized tourism development, which shows that it can also provide customized services for users.

The article defines the dimensions of the structural and functional space of social management practices in the social work management system. The management of social work is presented as a practical management activity, as a system of social work management at the macro, meso, and micro levels, as a process of planning, motivation, implementation, and control of management decisions. Social management practices in the management system of social work act as ways of implementing social management actions that are formed as a result of the interactions of social management subjects in the field of social service provision. The dimensions of the formation, reproduction and implementation of social management practices, which create a complete structural and functional space in the management system of social work, which are implemented in cognitive-interpretive, value-motivational, procedural-organizational, communicative and activity-organizational dimensions, are highlighted and analyzed. The analysis of various sociological approaches to the interpretation of "social practice" made it possible to distinguish social-management practices as cognitive-interpretive, value-motivational, procedural-organizational, communicative, and activity-organizational, which act as an integral structural-functional space of social-management practices in the management of social work. The study singled out three main models of social management practices in the management system of social work at the regional and local (territorial community) levels of management: administrative model; model of organizational development of the region/community; model of partnership interaction of social management subjects. It was analyzed that social interaction reflects the relationship between individuals and social groups, between social groups and organizations of different levels of its functioning as a system, and therefore lies at the basis of the management system, which is carried out through the social management practices of the governing subjects, according to socially acceptable values - normative and status-role models of behavior in society in a certain space and time.

COVID-19: Challenges to GIS with Big Data

In order to extract the features of English distance network education pattern, traditional pattern recognition methods of English distance network education result in low accuracy and large standard deviation of education pattern recognition. This paper proposes a pattern recognition method of English distance network education based on big data algorithm. Using big data technology, the digital English distance teaching resource database is established to avoid the duplication of acquired resources, the random forest algorithm of pattern big data is introduced, the single classifier of decision tree is used to distinguish characteristic data, the number of decision trees in the forest is adjusted, and the self construction process of random forest algorithm is optimized. In the process of pattern recognition, feature level state fusion and support vector machine are used to complete pattern recognition of English distance online education. The experimental results show that compared with the traditional algorithm, the standard deviation of the proposed algorithm is smaller, which can effectively improve the recognition accuracy.

The proliferation of multimedia devices over the Internet of Things (IoT) generates an unprecedented amount of data. Consequently, the world has stepped into the era of big data. Recently, on the rise of distributed computing technologies, video big data analytics in the cloud has attracted the attention of researchers and practitioners. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide a complete survey of recent research work on video big data analytics in the cloud, including the management and analysis of a large amount of video data, the challenges, opportunities, and promising research directions. To serve this purpose, we present this study, which conducts a broad overview of the state-of-the-art literature on video big data analytics in the cloud. It also aims to bridge the gap among large-scale video analytics challenges, big data solutions, and cloud computing. In this study, we clarify the basic nomenclatures that govern the video analytics domain and the characteristics of video big data while establishing its relationship with cloud computing. We propose a service-oriented layered reference architecture for intelligent video big data analytics in the cloud. Then, a comprehensive and keen review has been conducted to examine cutting-edge research trends in video big data analytics. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. To the best of our knowledge, this is the first study that presents the generalized view of the video big data analytics in the cloud. This paper provides the research studies and technologies advancing video analyses in the era of big data and cloud computing.

PurposeRecent research has drawn attention to the tension between the Central and local governments in China regarding their roles in environmental protection. This paper aims to explore this tension and examine the extent to which local/provincial government’s environmental oversight has influenced the quality of corporate environmental disclosure in China.Design/methodology/approachA sample of 198 listed companies in heavily polluting industries were selected to examine the relationship between their environmental disclosure quality and the respective local government’s environmental oversight level.FindingsThe results provide evidence that local/provincial government’s environmental oversight significantly influences environmental disclosure in China. Despite the coercive pressure from the powerful Central government on corporate environmental disclosure, local/provincial governments are able to buffer the pressure and adjust the intensity of their environmental oversight on companies during the implementation of central policies to retain local economic and political interests. This may partially explain the persistent issue of low environmental disclosure quality in China.Research limitations/implicationsThis study enriches our understanding of the significant role of local governments in China in enhancing or sometimes discounting the regulatory enforcement of the Central government on corporate environmental disclosure, pointing to the need for concerted efforts by both local and Central governments to advance environmental disclosure development.Originality/valueResearch on environmental disclosure in developed countries has been well established in the literature. However, such research in developing nations is still limited, especially in China, the world largest developing country. The existing literature on environmental disclosure in China links it with either market demands, or regulatory enforcement from the Central government. The importance of local/provincial governments and their environmental oversight has long been ignored, which motivates this research.

The biggest feature of the era of big data is that people can easily generate, access, and make use of massive data resources. As one of the most important and popular kind of big data, location big data and its application technology provide users with convenient services. However, improper collection, analysis and publishing of location big data also brings huge crisis of personal privacy disclosure. Spatial decomposition is one of the effective ways to achieve the statistics publication of location big data. In order to make full use of the redundant characteristics of location big data in spatial and temporal distribution, a hierarchical differential privacy hybrid decomposition algorithm is proposed in this paper. In the first layer of decomposition, an adaptive density grid structure is used to cluster the location big data, which not only reduces the uniform assumption errors but also avoids noise errors caused by large number of empty nodes. In order to guide the reasonable decomposition for skewed grids in the second layer, a heuristic quad-tree decomposition algorithm based on regional uniformity is designed, which solved the difficult problem for determining stop condition of the top-down decomposition of two-dimensional space. Comparative experiments show that the hierarchical differential privacy hybrid decomposition algorithm proposed in this paper has good effect in improving the accuracy of regional counting queries. The proposed algorithm has low computational complexity and obvious advantages in the publishing environment of big data.

Abstract. With the wide application of Big Data, Artificial Intelligence and Internet of Things in geographic information technology and industry, geospatial big data arises at the historic moment. In addition to the traditional "5V" characteristics of big data, which are Volume, Velocity, Variety, Veracity and Valuable, geospatial big data also has the characteristics of "Location Attribute". At present, the study of geospatial big data are mainly concentrated in: knowledge mining and discovery of geospatial data, Spatiotemporal big data mining, the impact of geospatial big data on visualization, social perception and smart city, geospatial big data services for government decision-making support four aspects. Based on the connotation and extension of geospatial big data, this paper comprehensively defines geospatial big data comprehensively. The application of geospatial big data in location visualization, industrial thematic geographic information comprehensive service and geographic data science and knowledge service is introduced in detail. Furthermore, the key technologies and design indicators of the National Geospatial Big Data Platform are elaborated from the perspectives of infrastructure, functional requirements and non-functional requirements, and the design and application of the National Geospatial Public Service Big Data Platform are illustrated. The challenges and opportunities of geospatial big data are discussed from the perspectives of open resource sharing, management decision support and data security. Finally, the development trend and direction of geospatial big data are summarized and prospected, so as to build a high-quality geospatial big data platform and play a greater role in social public application services and administrative management decision-making.

There are a wide variety of established toxicity databases being routinely used to support hazard and risk assessment. The increasing number and size of toxicology databases and methods for automatically recording toxicology studies has led to a diverse series of formats for organisation of the information. Currently, such approaches span from simple one-to-one data exchange formats to complex formats incorporating semantic representations and data models covering additional information (such as experimental design, results and findings). Such developments support the volume, variety and value of (big) data and the transition from insular, isolated and simple structures to big data technologies for big toxicology data resources. This chapter reviews current solutions for the most known resources and identifies opportunities and gaps that such resources show in the era of big data. It covers a variety of approaches to data exchange and database design for organising toxicity and related data, highlights a number of databases with toxicity data as well as information on alternative approaches, and discusses project management and regulatory compliance databases. The chapter concludes with reflections on how these systems can integrate diverse toxicology-related data in preparation for the adoption of new big data technologies.

With the advent of the era of network, video surveillance technology is more and more integrated into the IT emerging technologies. Big data technology in the field of video surveillance has shown a broad development path. The research of surveillance video data mining based on big data is committed to achieve a perfect fusion of the big data technology and video surveillance business, in order to create a solution of intelligent video surveillance in big data era. In this paper, based on the application requirements of video surveillance on big data, we designed the framework of big data analysis for surveillance video, and study the key technology of storage and processing for surveillance video data based on big data and the value of data mining. The study on real-time intelligence analysis and data mining, progresses the video surveillance from manual sampling to efficiently advance warning, post analysis, realization of intelligent information analysis, forecasting. It brings profound changes in the field of video surveillance business development.

Vertical fiscal imbalance, transfer payments, and fiscal sustainability of local governments in China

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China's national policy formulation undergone economic transformation and shaped the making of policies in promotion of private business and stock market regulation. China's distinctive process of central-local interaction in policy generation is one of its core strengths in reforming its economy.

Overview of the role of big data in mental health: A scoping review