• Title/Summary/Keyword: smart taxation

Search Result 3, Processing Time 0.017 seconds

Factors Affecting the Success of IT Service Venture Firms (IT서비스 벤처기업 성공에 영향을 미치는 요인)

  • An, Won Young;Oh, Jay In
    • Journal of Information Technology Services
    • /
    • v.16 no.4
    • /
    • pp.47-64
    • /
    • 2017
  • Three years after establishment, companies are said to face a period of risk called the "valley of death." To start a venture company and make it sustainable, the chance of failure must be minimized. According to an in-depth assessment report on special taxation in 2015, the one-year survival rate of Korean companies was about 60 percent and the five-year survival rate about 30%. These rates are low compared to those of major OECD member countries. Worse, such rates in Korea are decreasing year by year. The purpose of this study is to classify the success factors behind venture companies into human capital, social capital and financial capital, and verify through empirical analysis the factors influencing the success of venture companies based on the mediating roles of capability of the startup team and that for innovation. To find the success factors behind venture companies, this study first examined the theories derived from previous studies. SPSS 21 was used as the study method, while descriptive statistics, exploratory factor analysis and CMB test were conducted. In addition, SmartPLS 2 was used for confirmatory factor analysis, hypothesis test, mediation effect. The results of this study can help efforts toward job creation and economic revitalization pursued by the creative economy policy of the incumbent Korean administration. They can also be used as the cornerstone for venture companies in their pursuit of success.

An IoT based Green Home Architecture for Green Score Calculation towards Smart Sustainable Cities

  • Kumaran, K. Manikanda;Chinnadurai, M.;Manikandan, S.;Murugan, S. Palani;Elakiya, E.
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.15 no.7
    • /
    • pp.2377-2398
    • /
    • 2021
  • In the recent modernized world, utilization of natural resources (renewable & non-renewable) is increasing drastically due to the sophisticated life style of the people. The over-consumption of non-renewable resources causes pollution which leads to global warming. Consequently, government agencies have been taking several initiatives to control the over-consumption of non-renewable natural resources and encourage the production of renewable energy resources. In this regard, we introduce an IoT powered integrated framework called as green home architecture (GHA) for green score calculation based on the usage of natural resources for household purpose. Green score is a credit point (i.e.,10 pts) of a family which can be calculated once in a month based on the utilization of energy, production of renewable energy and pollution caused. The green score can be improved by reducing the consumption of energy, generation of renewable energy and preventing the pollution. The main objective of GHA is to monitor the day-to-day usage of resources and calculate the green score using the proposed green score algorithm. This algorithm gives positive credits for economic consumption of resources and production of renewable energy and also it gives negative credits for pollution caused. Here, we recommend a green score based tax calculation system which gives tax exemption based on the green score value. This direct beneficiary model will appreciate and encourage the citizens to consume fewer natural resources and prevent pollution. Rather than simply giving subsidy, this proposed system allows monitoring the subsidy scheme periodically and encourages the proper working system with tax exemption rewards. Also, our GHA will be used to monitor all the household appliances, vehicles, wind mills, electricity meter, water re-treatment plant, pollution level to read the consumption/production in appropriate units by using the suitable sensors. These values will be stored in mass storage platform like cloud for the calculation of green score and also employed for billing purpose by the government agencies. This integrated platform can replace the manual billing and directly benefits the government.

The World as Seen from Venice (1205-1533) as a Case Study of Scalable Web-Based Automatic Narratives for Interactive Global Histories

  • NANETTI, Andrea;CHEONG, Siew Ann
    • Asian review of World Histories
    • /
    • v.4 no.1
    • /
    • pp.3-34
    • /
    • 2016
  • This introduction is both a statement of a research problem and an account of the first research results for its solution. As more historical databases come online and overlap in coverage, we need to discuss the two main issues that prevent 'big' results from emerging so far. Firstly, historical data are seen by computer science people as unstructured, that is, historical records cannot be easily decomposed into unambiguous fields, like in population (birth and death records) and taxation data. Secondly, machine-learning tools developed for structured data cannot be applied as they are for historical research. We propose a complex network, narrative-driven approach to mining historical databases. In such a time-integrated network obtained by overlaying records from historical databases, the nodes are actors, while thelinks are actions. In the case study that we present (the world as seen from Venice, 1205-1533), the actors are governments, while the actions are limited to war, trade, and treaty to keep the case study tractable. We then identify key periods, key events, and hence key actors, key locations through a time-resolved examination of the actions. This tool allows historians to deal with historical data issues (e.g., source provenance identification, event validation, trade-conflict-diplomacy relationships, etc.). On a higher level, this automatic extraction of key narratives from a historical database allows historians to formulate hypotheses on the courses of history, and also allow them to test these hypotheses in other actions or in additional data sets. Our vision is that this narrative-driven analysis of historical data can lead to the development of multiple scale agent-based models, which can be simulated on a computer to generate ensembles of counterfactual histories that would deepen our understanding of how our actual history developed the way it did. The generation of such narratives, automatically and in a scalable way, will revolutionize the practice of history as a discipline, because historical knowledge, that is the treasure of human experiences (i.e. the heritage of the world), will become what might be inherited by machine learning algorithms and used in smart cities to highlight and explain present ties and illustrate potential future scenarios and visionarios.