分类： 图书馆学、情报学 >> 情报学 提交时间： 2016-06-12
摘要： Technology assessment is a systematic examination of the effects on or of new developments such as technologies, processes, policies, organizations, and so on. In this paper, we present a systematic method for technology assessment, as a part of the suite of tools for Forecasting Innovation Pathways (FIP). We explore means to combine tech mining tools with human intelligence in several idea exchange rounds to uncover potential secondary effects, and array them in terms of likelihood and magnitude. Big Data is studied as the case study. This is on-going research. We are currently on the 2nd round of stage 2. Technology assessment is a necessary component of FIP. It identifies areas in which significant impacts may occur, their likelihood, and their significance. The forecaster must evaluate these impacts, consider measures to enhance or inhibit them, and factor them into the planning process for developing the technology.
分类： 动力与电气工程 >> 动力与电气工程其他学科 提交时间： 2022-11-16 合作期刊: 《Electrical & Electronic Engineering Research》
摘要：
It is obvious that the modern agriculture needs the support of modern finance. The agricultural credit guarantee system, is an important means to promote the financial capital into the agriculture industry, and to solve its financing difficulties and its high cost. The designing and application of agricultural guarantee digital system platform will not only be beneficial to accelerate transformation and promotion of the mode of agricultural development, but also be positive significance on the steady growth, promoting the reform, restructuring and livelihood. This paper summarizes the relevant literatures on agricultural guarantee system, designs and develops a set of agricultural guarantee digital system platform based on the application of blockchain and big data technology, and finds it achieves a lot of benefits and good effects based on the practice in China.
分类： 心理学 >> 心理统计 提交时间： 2018-12-07
摘要： Human brain mapping (HBM) is increasingly becoming a multi-disciplinary field where some scientific issues are fundamental for all scientists and applications of using the technology to investigate individual differences. Reliability represents a significant issue for all scientific fields and has particularly been overlooked for decades by the HBM field [1]. Meanwhile, recent advances in open science have offered the field big data for developing novel methodological frameworks as well as performing large-scale investigations of the brain-mind associations based upon the individual differences assessed with HBM [2]. A systematic investigation of reliability seems still far behind these HBM developments. It is critical that reliability is evaluated ahead of these applications, motivating the current commentary on delineation of the anatomy of reliability for future HBM.
分类： 图书馆学、情报学 >> 情报学 提交时间： 2017-12-05 合作期刊: 《数据分析与知识发现》
摘要： Big data analytics is often prohibitively costly. It is typically conducted by parallel processing with a cluster of machines, and is considered a privilege of big companies that can afford the resources. This position paper argues that big data analytics is accessible to small companies with constrained resources. As an evidence, we present BEAS, a framework for querying big rela- tions with constrained resources, based on bounded evaluation and data-driven approximation.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-28 合作期刊: 《数据智能（英文）》
摘要： In this study, we uncover the topics of Chinese public cultural activities in 2020 with a two-step short text clustering (self-taught neural networks and graph-based clustering) and topic modeling approach. The dataset we use for this research is collected from 108 websites of libraries and cultural centers, containing over 17,000 articles. With the novel framework we propose, we derive 3 clusters and 8 topics from 21 provincial#2; level regions in China. By plotting the topic distribution of each cluster, we are able to shows unique tendencies of local cultural institutes, that is, free lessons and lectures on art and culture, entertainment and service for socially vulnerable groups, and the preservation of intangible cultural heritage respectively. The findings of our study provide decision-making support for cultural institutes, thus promoting public cultural service from a data-driven perspective.
分类： 心理学 >> 社会心理学 提交时间： 2022-06-29
摘要： Morality is an eternal topic that has been contemplated and pursued by both philosophers and lay people alike for thousands of years. Psychologists have found that individuals moral judgments, moral emotions, moral intentions, moral motivations, moral reasoning and moral behaviors are not internally consistent. Among which, moral behavior is most relevant to everyday life. Given that moral behaviors are influenced by various factors such as personality traits (e.g., virtue), social situations (e.g., time pressure), and social desirability (e.g., moral image), it is quite challenging to effectively and accurately measure moral behaviors both in the laboratory and in real-life social situations. Our current work synthesizes differing concepts of moral behaviors and their conceptual distinctions from diverse disciplinary perspectives. We then offer a selective review on differing paradigms such as scale method, laboratory experiment, virtual reality, field experiment, big data approaches and experience-sampling method. It is our hope that this work would inspire researchers to better capture and explore the complex and dynamic moral behaviors, and provide potential future prospects to the emerging trends of novel thoughts, theories, methods, paradigms and applications for unveiling moral behaviors and their underlying processes.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-25 合作期刊: 《数据智能（英文）》
摘要： Data-intensive science is reality in large scientific organizations such as the Max Planck Society, but due to the inefficiency of our data practices when it comes to integrating data from different sources, many projects cannot be carried out and many researchers are excluded. Since about 80% of the time in data#2;intensive projects is wasted according to surveys we need to conclude that we are not fit for the challenges that will come with the billions of smart devices producing continuous streams of dataour methods do not scale. Therefore experts worldwide are looking for strategies and methods that have a potential for the future. The first steps have been made since there is now a wide agreement from the Research Data Alliance to the FAIR principles that data should be associated with persistent identifiers (PIDs) and metadata (MD). In fact after 20 years of experience we can claim that there are trustworthy PID systems already in broad use. It is argued, however, that assigning PIDs is just the first step. If we agree to assign PIDs and also use the PID to store important relationships such as pointing to locations where the bit sequences or different metadata can be accessed, we are close to defining Digital Objects (DOs) which could indeed indicate a solution to solve some of the basic problems in data management and processing. In addition to standardizing the way we assign PIDs, metadata and other state information we could also define a Digital Object Access Protocol as a universal exchange protocol for DOs stored in repositories using different data models and data organizations. We could also associate a type with each DO and a set of operations allowed working on its content which would facilitate the way to automatic processing which has been identified as the major step for scalability in data science and data industry. A globally connected group of experts is now working on establishing testbeds for a DO-based data infrastructure.
分类： 管理学 >> 管理学其他学科 提交时间： 2022-11-24 合作期刊: 《2022年第三届传播、创新和经济管理国际研讨会》
摘要： In the era of digital economy, as one of the important means for the country to promote the modernization of the governance system and governance capacity in the field of tax collection and management, the Golden Tax Phase III big data tax collection and management system has had a positive impact on the total factor productivity of the manufacturing industry. Taking A-share listed manufacturing companies from 2010 to 2019 as a research sample, and using the Golden Tax Phase III tax collection and management system as a quasi-natural experimental scenario, this paper empirically tests the impact of big data tax collection and management on the total factor productivity of enterprises. The study found that the improvement of the level of big data tax collection and management has significantly improved the total factor productivity of enterprises. Exploring its influence mechanism found that big data tax collection and management can improve the total factor productivity of enterprises by stimulating technological innovation of enterprises. The article enriches the research on the policy consequences of big data collection and management, and also provides important empirical data support for government departments to continue to promote and improve the golden tax phase IV.
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