Measuring what matters in the era of big data

In today’s era, with the wide application of artificial intelligence AI, the application of media is gradually approaching to intelligence, the application of big data in the era of smart media has gradually become an indispensable part of social life. The era of big data has promoted the development and progress of the society and facilitated people’s life. Accordingly, new media such as short video “we media” have sprung up like mushrooms and entered the public’s vision. Through the application of big data in the era of smart media, this paper analyzes the development trend of short video we media in social life, analyzes the influence of the era of big data on short video, and further reflects the general advantages of the era of smart media from the discussion of short video we media in the era of intelligent big data. This paper discusses some entertainment and convenience created by the application of the short video “we media” in the era of smart media for People’s Daily life. Based on the problems and challenges encountered in the application of smart media in some fields, it puts forward specific plans for the security protection of people’s personal information in the era of big data. The purpose of this paper is to try to reveal the application of smart media and big data to short video in this era and the corresponding research. Based on the above discussion, this paper combines big data analysis and relevant theoretical knowledge in the field of news media, combines intelligence with “we media”, and studies the value of “we media” short videos to the social development in the era of smart media. This article research results show that the wide application of wisdom media era of big data is a trend of rapid development of today’s society, in this trend, a short video from the development of the media heat continues to increase, not only make the communication between people more close, and accelerated the development of the modern intelligent society and optimize the traditional mode of transmission medium.

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

In the era of big data, through the analysis of educational data, find out the conditions that fit the students and teaching theory, so as to formulate more realistic educational teaching strategies. For this reason, this article brings up the research of college English translation mixed teaching in the era of educational big data. This paper designs a mixed teaching mode, and compares the traditional teaching mode with the mixed teaching mode of this article by setting up an experimental group and a control group. This study finds that the difference between the pre-teaching results of the experimental group and post-teaching is statistically significant (t = 9.270, P < 0.001), which proves that the mixed teaching mode has significantly improved the students’ English translation level, and their scores have improved significantly. The comparison between the control group score (80.04 ± 6.53) and the experimental group score (85.09 ± 6.33) was statistically significant (t = 4.263, P < 0.001), indicating that in the practical teaching research stage, the translation scores of the students in the control group and the experimental group were both It has improved, but the experimental group score is higher than the control group. The results of this paper provide reference value for the reform of the mixed teaching model of English translation in colleges and universities.

Just-in-time learning (JITL) has become a widely used industrial process modeling tool. With the advent of the industrial big data era, rich data information has brought new opportunities to JITL. Specifically, the completeness of data samples in the era of big data provides an important premise and support for the JITL method, prompting us to rethink the application value of JITL in the context of industrial big data. At the same time, the huge amount of data causes certain difficulties in data searching, which is a key issue for JITL. In this article, a parallel computing strategy is adopted to divide the entire computational searching task into several subtasks, and assign the subtasks to parallel computing nodes to complete parallel searching. In this way, not only the full advantage of big data information is effectively utilized, but also the search capability and efficiency under big data are improved. In addition, in order to improve the real-time nature of JITL, a model library management (MLM) strategy is adopted, and the query similar samples are used to operate with existing similar models. And by selectively adding new data, the database management (DBM) strategy is also developed, which not only alleviates the problem of information redundancy, but also reduces the search pressure caused by the increasingly large database. Obviously, MLM and DBM are particularly important as JITL auxiliary tools under industrial big data. Combining parallel computing, a parallel JITL (P-JITL) framework is proposed. As an example, the variational Bayesian factor regression model is transformed into the parallel Bayesian-JITL method for big process data modeling, which is further extended to a nonlinear form. To evaluate the feasibility and efficiency of the developed methods, a real industrial case is demonstrated.

In the era of big data, the sports industry is facing transformation and upgrading. Big data has become the key technology to lead the cross-border sports industry and strengthen the sports industry. In the era of big data, the development direction of sports industry is based on the development concept of big data sharing of sports industry, relying on the refinement and personalized service of big data to the market, aiming to build a sports industry chain of industrial integration and build a big data platform of sports industry, so as to realize online sports consumption and offline sports experience. Based on the above background, this paper aims to study the digital management of sports industry in the era of big data. With the maturity of big data technology, the combination of big data and sports industry has become inevitable. Based on the simple analysis of the characteristics and background of the era of big data, this paper discusses the opportunities and challenges faced by the big data sports industry, and proposes to promote the research of digital management of sports industry from the aspects of improving data storage and processing capacity. In the era of big data, sports industry management should be able to use big data technology to analyze valuable information from massive data. In the era of big data, the storage of massive information requires data analysis and processing, and timely feedback of information, which all bring great challenges to the sports industry.

Nowadays, with the rapid development of society, great changes have taken place in the flow of information. Big data is the product of this era. It has triggered the birth and development of MOOC, which has brought an opportunity for the development and reform of traditional college physical education. This paper discusses the emergence and characteristics of big data and the origin and development of MOOC, mainly focusing on the analysis of the impact of MOOC on College Physical Education in the era of big data, and combining with the actual situation of College Physical Education under the background of MOOC in the era of big data, Firstly, it explains and discusses the influence of MOOC on College Physical Education in the era of big data from the positive and negative aspects. Secondly, it analyzes the idea of applying MOOC in College Physical Education in the era of big data, further highlights the influence of MOOC on College Physical Education in the era of big data, and gives full play to the value of MOOC teaching mode, aiming to provide reference for relevant research Information.KeywordsBig data eraMOOCCollege physical educationTeaching influence

The article expounds the changes in financial analysis methods and analytical thinking of manufacturing enterprises in the era of big data, and then points out that big data technology has improved the “decision usefulness” of financial analysis, and built a financial accounting sharing information platform in the era of Internet big data to achieve The financial information between the corporate departments is effectively shared. Finally, the paper analyzes the challenges brought by the big data era to the financial accounting work, and points out the countermeasures to deal with the challenges.

Privacy was defined as a fundamental human right in the Universal Declaration of Human Rights at the 1948 United Nations General Assembly. However, there is still no consensus on what constitutes privacy. In this review, we look at the evolution of privacy as a concept from the era of Hippocrates to the era of social media and big data. To appreciate the modern measures of patient privacy protection and correctly interpret the current regulatory framework in the United States, we need to analyze and understand the concepts of individually identifiable information, individually identifiable health information, protected health information, and de-identification. The Privacy Rule of the Health Insurance Portability and Accountability Act defines the regulatory framework and casts a balance between protective measures and access to health information for secondary (scientific) use. The rule defines the conditions when health information is protected by law and how protected health information can be de-identified for secondary use. With the advents of artificial intelligence and computational linguistics, computational text de-identification algorithms produce de-identified results nearly as well as those produced by human experts, but much faster, more consistently and basically for free. Modern clinical text de-identification systems now pave the road to big data and enable scientists to access de-identified clinical information while firmly protecting patient privacy. However, clinical text de-identification is not a perfect process. In order to maximize the protection of patient privacy and to free clinical and scientific information from the confines of electronic healthcare systems, all stakeholders, including patients, health institutions and institutional review boards, scientists and the scientific communities, as well as regulatory and law enforcement agencies must collaborate closely. On the one hand, public health laws and privacy regulations define rules and responsibilities such as requesting and granting only the amount of health information that is necessary for the scientific study. On the other hand, developers of de-identification systems provide guidelines to use different modes of operations to maximize the effectiveness of their tools and the success of de-identification. Institutions with clinical repositories need to follow these rules and guidelines closely to successfully protect patient privacy. To open the gates of big data to scientific communities, healthcare institutions need to be supported in their de-identification and data sharing efforts by the public, scientific communities, and local, state, and federal legislators and government agencies.

Recently, there has been burgeoning interest in developing more effective and robust clinical decision support systems (CDSSs) for oncology. This has been primarily driven by the demands for more personalized and precise medical practice in oncology in the era of so-called Big Data (BD); an era that promises to harness the power of large-scale data flow to revolutionize cancer treatment. This interest in BD analytics has created new opportunities as well as new unmet challenges. These include: routine aggregation and standardization of clinical data; patient privacy; transformation of current analytical approaches to handle such noisy and heterogeneous data; and expanded use of advanced statistical learning methods based on confluence of modern statistical methods and machine learning algorithms. In this review, we present the current status of CDSSs in oncology, the prospects and current challenges of BD analytics, and the promising role of integrated modern statistics and machine learning algorithms in predicting complex clinical endpoints, individualizing treatment rules, and optimizing dynamic personalized treatment regimens. We discuss issues pertaining to these topics and present application examples from an aggregate of experiences. We also discuss the role of human factors in improving the utilization and acceptance of such enhanced CDSSs and how to mitigate possible sources of human error to achieve optimal performance and wider acceptance.

In the era of big data, it is particularly necessary to use Internet plus technology to promote tax law teaching reform and innovation. This paper analyzes the current situation, points out the existing problems, and puts forward specific measures for Internet plus promoting tax reform and innovation in the era of big data, so as to facilitate the reform and development of tax law teaching in Chinese universities. In the era of big data, with the rapid development of computer technology and the continuous improvement of Internet plus, people's lives have entered the information society. People are increasingly demanding information, big data has become a hot term, and infiltrate into various industries. Nowadays, science and technology are developed, big data information is unblocked, people communicate more closely, life is more convenient, big data is the product of high-tech era.“ Internet plus” is the new form of Internet development under the innovation, which promotes social and economic entities and becomes a platform for reform, innovation and development. Internet plus relying on Internet technology to integrate Internet and traditional industries, optimize production factors and restructure business models to achieve economic transformation and upgrading, give full play to the advantages of the Internet, upgrade industrial productivity and increase social wealth by upgrading industries. Through the characteristics of openness, equality and interaction, the Internet uses big data analysis to transform the traditional industrial model and enhance the power of economic development, so as to promote the healthy and rapid development of national economy [1]. Based on the background of Internet plus in the era of big data, this article discusses the development and innovation of tax law teaching reform in our country. It expects that the traditional tax law teaching mode can help students integrate new teaching and big data technology with the help of big data technology and Internet plus platform, and better train new talents suitable for the big data era. It is a contribution to the construction and economic development of "double first-class" in universities in China.

New and expanding sources of spatial big data hold tremendous potential for regional policy analysis. Such data enable us to analyze regional policies in ways not possible with traditional sources of data, such as administrative records. At the same time, the use of spatial big data is fraught with issues and challenges that must be addressed. In this paper, we discuss both the opportunities and challenges of using spatial big data for regional policy analysis. We also explore analytical issues tied to the use of regional policy analysis methods in the era of big data, as well as the state of art in applying such methods to spatial big data. Our discussion focusses on three types of methods: (1) statistical and regression modeling, (2) traditional nonparametric modeling, and (3) deep neural learning.

With the rapid development of the economy and the continuous development of Internet technology, China has entered the era of big data. In the era of big data, the application of computer software technology is more extensive and in-depth. This article is mainly from the following four aspects. First, a brief introduction to computer software technology and the era of big data; Second, the application of computer software technology in the next generation of big data; Third, the challenges that computer software technology needs to face in the era of big data; Fourth, in the era of big data considerations for using computer software technology. This paper briefly introduces and analyzes the application of computer software technology in the era of big data.

In the era of big data, the platformization and openness of teaching resources, the diversification and innovation of teaching modes, and the ubiquity and initiative of teaching subjects will inevitably have an epoch-making impact on teaching, thereout call for the profound reform of traditional teaching by big data. Combing with the analysis of the basic characteristics of teaching in the era of big data, it is necessary to reflect on many practical problems of traditional teaching, actively explore the practice path of teaching reform in the era of big data and advocate teaching reform from the aspects of teaching concept, role awareness, teaching management, teaching methods and teaching assessment and so on in order to better adapt to the basic requirements of teaching in the era of big data.

The Conference aimed to improve understanding of the central issues in the era of Big Data, to promote multidisciplinary communication and collaboration, to help the development of young data scientists, to encourage the revitalization of traditional research approaches and to contribute to and support the Chinese national strategy to promote innovation. Around the world, there is talk of a ‘data revolution’ – this conference aimed to place China at the forefront of this revolution, providing the communication, skills and training to help Chinese scientists seize the opportunities of Big Data and ‘ride the wave’ of increasing data volumes, velocity and variety. Spanning two days, the conference featured two plenary sessions and fourteen breakout sessions. There were four major keynotes, three invited reports and three reports on projects and initiatives. The keynote lectures focused on the hot issues in the Big Data era, including integration and notation of Big Data, development opportunities and major challenges for science and technology. The breakout sessions also included technical sessions and open forums, with topics including:

Profiling of individuals based on inborn, acquired, and assigned characteristics is central for decision making in health care. In the era of omics and big smart data, it becomes urgent to differentiate between different data governance affordances for different profiling activities. Typically, diagnostic profiling is in the focus of researchers and physicians, and other types are regarded as undesired side-effects; for example, in the connection of health care insurance risk calculations. Profiling in a legal sense is addressed, for example, by the EU data protection law. It is defined in the General Data Protection Regulation as automated decision making. This term does not correspond fully with profiling in biomedical research and healthcare, and the impact on privacy has hardly ever been examined. But profiling is also an issue concerning the fundamental right of non-discrimination, whenever profiles are used in a way that has a discriminatory effect on individuals. Here, we will focus on genetic profiling, define related notions as legal and subject-matter definitions frequently differ, and discuss the ethical and legal challenges.