政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/147180
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113648/144635 (79%)
Visitors : 51569247      Online Users : 620
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/147180


    Title: 人工智慧法遵科技應用之監理制度省思－以金融機構防制洗錢的規範與實踐為核心
    Reflecting on the Regulatory System for the Use of Artificial Intelligence in Regtech in Technological Applications: A Focus on Norms and Practices of Anti-Money Laundering in Financial Institutions
    Authors: 黃邦平
    Huang, Pang-Ping
    Contributors: 臧正運
    Tsang, Cheng-Yun
    黃邦平
    Huang, Pang-Ping
    Keywords: 人工智慧
    新興科技風險
    洗錢防制
    法遵科技
    數據治理
    Date: 2023
    Issue Date: 2023-09-01 15:58:53 (UTC+8)
    Abstract: 在國際間，監管機構對金融機構的洗錢防制要求越來越嚴格，處罰違規行為的懲罰也不斷升高。因此，金融機構需要提高法令遵循能力，同時降低不必要的遵循成本。為應對這些挑戰，金融機構開始廣泛應用法遵科技來改善遵循流程和合規文化。然而，法遵成本的上升使得金融機構的獲利能力受到影響，尤其對於資金有限的小型機構而言，面臨更大的挑戰。
    與此同時，人工智慧在金融機構，特別是洗錢防制方面的應用已成為一個關鍵趨勢。然而，隨著技術的進步和廣泛應用，也帶來了一系列的法律、道德和監管問題。在這樣的背景下，探討人工智慧法遵科技應用之監理制度對於金融監理機構、金融機構及社會大眾都具有極高的重要性。
    當引入人工智慧等新興科技時，金融機構需要仔細評估可能帶來的風險，包括對新興科技不了解的應用風險，以及數據安全性和隱私保護等方面的挑戰。因此，建立適當的風險評估和管理機制至關重要，以確保這些科技在洗錢防制方面的合規性和有效性。
    本研究將探討金融機構對法遵科技的運用，以及洗錢防制法規與實踐的現況。同時，比較各項人工智慧法遵科技核心技術及其相應的風險，並架構出一洗錢防制法遵科技風險地圖，對於金融監理機關、金融機構董事會與高階管理層，以及金融機構法遵與內部稽核單位等不同受眾，嘗試提出可行的整合解決方案。
    Reference: 朱啟恆，大數據於金融業之應用，財金資訊季刊，第84期，頁12-18，2015年10月。https://www.fisc.com.tw/Upload/8eaaa580-8592-4511-bd3d-a95d0d5ccdc8/TC/8402.pdf
    行政院洗錢防制辦公室，2021國家洗錢資恐及資武擴風險評估報告，2021年12月。https://www.amlo.moj.gov.tw/media/20211299/2021%E5%9C%8B%E5%AE%B6%E6%B4%97%E9%8C%A2%E8%B3%87%E6%81%90%E5%8F%8A%E8%B3%87%E6%AD%A6%E6%93%B4%E9%A2%A8%E9%9A%AA%E8%A9%95%E4%BC%B0%E5%A0%B1%E5%91%8A.pdf?mediaDL=true
    谷湘儀、臧正運等人，從「監理沙盒」制度展望臺灣FinTech監理思維，五南，2版，2018年4月。
    林志潔等人，監理科技與法遵科技之發展應用及其對金融穩定之影響，財團法人台北外匯市場發展基金會委託報告，2022年1月，https://www.tpefx.com.tw/uploads/download/tw/The%20development%20and%20application%20of%20supervisory%20technology%20and%20legal%20compliance%20technology%20and%20their%20impact%20on%20financial%20stability.pdf
    林鈺雄、蔡佩玲、楊雲驊、林志潔、李聖傑、李宏錦、謝建國、金延華，洗錢防制新法之立法評析，月旦刑事法評論，第4期，頁 117-129，2017年3月。
    法務部調查局洗錢防制處，洗錢防制工作年報，2021年10月，
    https：//www.mjib.gov.tw/userfiles/files/35-%E6%B4%97%E9%8C%A2%E9%98%B2%E5%88%B6%E8%99%95/files/%E6%B4%97%E9%8C%A2%E9%98%B2%E5%88%B6%E5%B7%A5%E4%BD%9C%E5%B9%B4%E5%A0%B1/annual_109.pdf
    洪良明、張信一，國際內部稽核協會三道模型，內部稽核，第111期，頁 4-9，2020年10月。
    金融監督管理委員會，金融科技發展路徑圖，2020年12月，https://www.fsc.gov.tw/websitedowndoc?file=chfsc/202012241229310.pdf&filedisplay=1090827%E9%87%91%E8%9E%8D%E7%A7%91%E6%8A%80%E7%99%BC%E5%B1%95%E8%B7%AF%E5%BE%91%E5%9C%96%E5%A0%B1%E5%91%8A%E6%9B%B8.pdf
    陳慧蓉等人，監理科技與法遵科技最新發展趨勢之探討，臺灣集中保管結算所報告，2020年12月，https://m.tdcc.com.tw/TDCCWEB/upload/402897967d841dba017e3226bd08006c.pdf
    程法彰，洗錢防制與個人資料保護的兩難，全國律師，第22卷第11期，頁 65-69，2018年11月。
    程權勝，金融機構防制洗錢及打擊資恐監理新趨勢 -從兆豐商銀遭重罰案談起，政治大學法學院在職專班碩士論文，2017年6月。
    蔣念祖，美國、新加坡及香港洗錢防制國際相互評鑑報告之研析，臺灣法學雜誌，第378期，頁378: 19-54，2019年10月。
    行政院，第3輪洗錢防制評鑑 臺灣獲佳績—金流透明 世界好評，2019年10月，https://www.ey.gov.tw/Page/5A8A0CB5B41DA11E/bdf44d9a-f4f0-43aa-99f2-771f612983ac
    行政院洗錢防制辦公室，臺灣接受亞太防制洗錢組織（APG）第三輪相互評鑑之評鑑報告正式出爐!!， 2018年11月。https：//www.amlo.moj.gov.tw/1506/1507/14969/post
    陳盈州、張珍鳳，Regtech於防制洗錢之應用與發展，勤業眾信通訊，2021年10月，https://www2.deloitte.com/tw/tc/pages/audit/articles/regtech-prevent-money.html
    陳智忠、鍾宜樺、黃琪淯，輕鬆玩轉RPA的關鍵，安永台灣，2021年7月，https://www.ey.com/zh_tw/financial-accounting-advisory-services/key-to-digital-transformation-through-rpa
    孫欣、章友馨，金融機構法令遵循風險評估與法規資料庫，安建通訊電子報，2018年12月，https://kpmg.com/tw/zh/home/insights/2018/01/full-bleed-page-test.html
    Abiteboul. S. Querying Semi-Structured Data, ICDT `97: Proceedings of the 6th International Conference on Database Theory, 1–18 (1997).
    Ad Hoc Expert Group (AHEG) for the Preparation of a Draft text of a Recommendation the Ethics of Artificial Intelligence. Outcome document: first draft of the Recommendation on the Ethics of Artificial Intelligence, 23 (2020). Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000373434
    Arner, D. W., Barberis, J. N., & Buckley, R. P. The emergence of RegTech 2.0: From know your customer to know your data. Journal of Financial Transformation, 79, 17–63 (2016).
    Arner. D.W., Barberis. J.N. & Buckley. R.P. FinTech, RegTech and the Reconceptualization of Financial Regulation. Northwestern Journal of International Law & Business, Forthcoming University of Hong Kong Faculty of Law Research Paper, 37(3), 371-413 (2017).
    Arner. D.W., Barberis. J.N. & Buckley. R.P. FinTech, RegTech: Building a Better Financial System1. Handbook of Blockchain, Digital Finance, and Inclusion, 1, 359-373 (2018).
    Bakir. G., Hofmann. T. & Scholkopf. B. Predicting Structured Data - （Neural Information Processing). Cambridge: The MIT Press (2007).
    Berk, R. A. Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement, 4, 209-237 (2020).
    Bishop, C. M. Pattern Recognition and Machine Learning. New York, NY: Springer (2006).
    Bostrom, N. Superintelligence: Paths, dangers, strategies. Oxford: Oxford University Press (2014).
    Börner. K. & Polley. D. E. Visual Insights: A Practical Guide to Making Sense of Data, 114-185 (2014).
    Buneman. P., Davidson. S., Fernandez. M. & Suciu. D. Adding structure to unstructured data, ICDT 1997: Database Theory, 336–350 (1997).
    Braden. R. Allenby, Governance and Technology Systems: The Challenge of Emerging Technologies, The International Library of Ethics, Law and Technology 7, 20-22 (2011).
    Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P. & Oliveira, A. L. Computational intelligence and fnancial markets: A survey and future directions. Expert Systems with Applications, 55, 194–211 (2016).
    Chandrinos, S. K., Sakkas, G. & Lagaros, N. D. AIRMS: A risk management tool using machine learning. Expert Systems with Applications, 105, 34–48 (2018).
    Choi, T. M., Chan, H. K., & Yue, X. Recent development in big data analytics for business operations and risk management. IEEE Transactions on Cybernetics, 47(1), 81–92 (2017).
    Colladon, A. F. & Remondi, E. Using social network analysis to prevent money laundering. Expert Systems with Applications, 67, 49–58 (2017).
    Crawford, K. & Calo. R. There is a Blind Spot in AI Research (2016), Retrieved from https://www.nature.com/news/there-is-a-blind-spot-in-ai-research-1.20805. Last accessed 6 December 2021.
    Data Taxonomy. NSW Government website (2023), Retrieved from https://data.nsw.gov.au/IDMF/data-structure-and-coordination/data-taxonomy
    Day, S. Quants turn to machine learning to model market impact. Risk.net (2017), Retrieved from https://www.risk.net/asset-management/4644191/quants-turnto-machine-learning-to-model-market-impact. Last accessed 3 May 2023.
    Deloitte Insight. 2023 banking and capital markets outlook (2023), Retrieved from https://www2.deloitte.com/content/dam/insights/articles/us175544_cfs-fsi-outlook-banking/DI_CFS_FSI_Outlook-Banking.pdf
    Deloitte Insight. RegTech Universe 2023 (2022), Retrieved from https://www2.deloitte.com/lu/en/pages/technology/articles/regtech-companies-compliance.html
    Demetis, D. S. Fighting money laundering with technology: A case study of Bank X in the UK. Decision Support Systems, 105. 96-107 (2018）.
    Emirbayer, M., Goodwin, J. Network Analysis, Culture, and the Problem of Agency. American Journal of Sociology, 99(6), 1411-1454 (1994).
    European Commission. White Paper On Artificial Intelligence–A European approach to excellence and trust, Brussels, 19,2 (2020).
    EU. Proposal for a Regulation laying down harmonised rules on artificial intelligence (2021). Retrieved from https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence
    EU. A European approach to artificial intelligence-Shaping Europe’s digital future, Retrieved from https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-age/shaping-europes-digital-future_en. Last accessed 8 October 2022.
    EUR-Lex. Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence. Brussels (2021). Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52021PC0206
    Farley, S. S., Dawson, A., Goring, S. J. & Williams, J. W. Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions. BioScience, 68(8), 563–576 (2018).
    FATF. The FATF Recommendations-International Standards on Combating Money Laundering and the Financing of Terrorism & Proliferation (2012), Retrieved from https://www.fatf-gafi.org/publications/fatfrecommendations/documents/fatf-recommendations.html
    FATF. Risk-Based Approach for the Banking Sector (2014), Retrieved from https://www.fatf-gafi.org/documents/riskbasedapproach/documents/risk-based-approach-banking-sector.html?hf=10&b=0&s=desc (fatf_releasedate)
    FATF. Digital Transformation of AML/CFT (2020), Retrieved from https://www.fatf-gafi.org/publications/digitaltransformation/digital-transformation.html?hf=10&b=0&s=desc
    FATF.Opportunities and Challenges of New Technologies for AML/CFT (2020), Retrieved from https://www.fatf-gafi.org/media/fatf/documents/reports/Opportunities-Challenges-of-New-Technologies-for-AML-CFT.pdf
    FATF. Stocktake on Data Pooling, Collaborative Analytics and Data Protectio (2020), Retrieved from https://www.fatf-gafi.org/media/fatf/documents/reports/Opportunities-Challenges-of-New-Technologies-for-AML-CFT.pdf
    FATF. Methodology for Assessing Technical Compliance with the FATF Recommendations and the Effectiveness of AML/CFT Systems (2021), Retrieved from https://www.fatf-gafi.org/en/publications/Mutualevaluations/Fatf-methodology.html
    FCA. Call for Input:Supporting the development and adoption of RegTech (2015), Retrieved from https://www.fca.org.uk/publication/call-for-input/regtech-call-for-input.pdf
    Federal Register. Executive Order on Maintaining American Leadership in Artificial Intelligence (2019). Retrieved from https://www.federalregister.gov/documents/2019/02/14/2019-02544/maintaining-american-leadership-in-artificial-intelligence
    Federal Register. Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government (2020). Retrieved from https://www.federalregister.gov/documents/2020/12/08/2020-27065/promoting-the-use-of-trustworthy-artificial-intelligence-in-the-federal-government
    Figini, S., Bonelli, F. & Giovannini, E. Solvency prediction for small and medium enterprises in banking. Decision Support Systems, 102, 91–97 (2017）.
    Financial Stability Board. Artifcial intelligence and machine learning in fnancial services (2017). Retrieved from http://www.fsb.org/wp-content/uploads/P011117.pdf.
    Financial Stability Institute. Innovative technology in financial supervision (suptech) - the experience of early users. FSI Insights (2018), Retrieved from https://www.bis.org/fsi/publ/insights9.pdf
    Freeman, L.C. The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver, BC: Empirical Press (2004).
    Goldberg, Y. A Primer on Neural Network Models for Natural Language Processing. Journal of Artificial Intelligence Research, 57, 345–420 (2015).
    Godsiff, P. & Wood, Z. Financing the Digital Economy: From financing products and purchases to financing service and use. Index initiative in the Digital Economy at Exeter (2020).
    Goodfellow, I., Bengio, Y., Courville, A. & Bengio, Y. Deep learning. Cambridge: MIT Press, 1, 96-161(2016).
    Goldwasser. S., Micali. S. & Rakoff. C. The knowledge complexity of interactive proof-systems. STOC `85: Proceedings of the seventeenth annual ACM symposium on Theory of computing, 291-304 (1985).
    Heaton, J. B., Polson, N. G. & Witte, J. H. Deep learning for finance: Deep portfolios. Applied Stochastic Models in Business and Industry, 33(1), 3–12 (2017).
    Hendricks, D. & Wilcox, D. A reinforcement learning extension to the Almgren-Chriss framework for optimal trade execution. In IEEE Conference on Computational Intelligence for Financial Engineering & Economics .CIFEr, 457–464 (2014).
    Hu, Y., Zhang, X., Feng, B., Xie, K. & Liu, M. iTrade: A mobile datadriven stock trading system with concept drift adaptation. International Journal of Data Warehousing and Mining (IJDWM), 11(1), 66–83 (2015).
    ICDPPC. Declaration on Ethics and Data Protection in Artifical Intelligence. 40th International Conference of Data Protection and Privacy Commissioners (2018). Retrieved from http://globalprivacyassembly.org/wp-content/uploads/2018/10/20180922_ICDPPC-40th_AI-Declaration_ADOPTED.pdf
    IIF. Regtech in Financial Services: Solutions for Compliance and Reporting (2016). Retrieved from https://www.iif.com/Publications/ID/1686/Regtech-in-Financial-Services-Solutions-for-Compliance-and-Reporting+
    Investopedia - Market Fragmentation. Retrieved from https://www.investopedia.com/terms/m/market-fragmentation.asp. Last accessed 11 June 2021.
    Institute for Economics and Peace. Global Terrorism Index 2023 (2023). Retrieved from https://www.visionofhumanity.org/maps/global-terrorism-index/#/
    Leenes. R. et al. Regulatory Challenges of Robotics: Some Guidelines for Addressing Legal and Ethical. INNOVATION & TECH, 9, 1-2 (2017).
    Janssen, M. & Brous, P. & Janowski, T. Data governance: Organizing data for trustworthy Artificial Intelligence. Computer Science (2020).
    Kessler. An Overview of Cryptography (2023). Retrieved from https://www.garykessler.net/library/crypto.html. Last accessed 17 March 2022.
    Khandani, A. E., Kim, A. J. & Lo, A. W. Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787 (2010).
    Kleppmann, M. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. O`Reilly (2017).
    KPMG. How RegTech can transform your business (2022), https://home.kpmg/dp/en/home/media/press-releases/2022/02/how-regtech-can-transform-your-business.html
    Kumar, P. P. Machine learning for model development in market risk. GARP Institute (2018). Retrieved from https://www.garp.org/#!/risk-intelligence/all/all/a1Z1W000003fM0yUAE?utm_medium=social&utm_source=facebook&utm_content=org_whitepaper&utm_term=machinelearning&utm_campaign=sm_riskintelligence. Last accessed 17 August 2022.
    Lin, L. & Nestarcova, D. Venture Capital in the Rise of Crypto Economy: Problems and Prospects, BERKELEY, 16, 533-568 (2016).
    Guihot. M., Matthew. A. F. & Suzor. N. P. Nudging Robots: Innovative Solutions to Regulate Artificial Intelligence, 385-414 (2017).
    Milgram, S. The Small World Problem. Psychology Today, 2, 60-67 (1967).
    Mitchell, J.C. The concept and use of social networks. Annual Review of Anthropology, 3, 179-299 (1974).
    Moosa, I. A. Operational risk management. New York: Palgrave Macmillan (2007).
    Mugarura, N. Uncoupling the relationship between corruption and money laundering crimes. Journal of Financial Regulation and Compliance, 24(1), 74-89 (2016).
    Nazemi, A., Heidenreich, K. & Fabozzi, F. J. Improving corporate bond recovery rate prediction using multi-factor support vector regressions. European Journal of Operational Research, forthcoming (2018).
    Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y. & Sun, X. The application of data mining techniques in fnancial fraud detection: A classifcation framework and an academic review of literature. Decision Support Systems, 50(3), 559–569 (2011).
    NIST. AI Risk Management Framework Concept Paper (2021), Retrieved from https://www.nist.gov/system/files/documents/2021/12/14/AI%20RMF%20Concept%20Paper_13Dec2021_posted.pdf
    NIST. AI Risk Management Framework: Initial Draft (2022), Retrieved from https://www.nist.gov/system/files/documents/2022/03/17/AI-RMF-1stdraft.pdf
    NIST. AI Risk Management Framework: Second Draft (2022), Retrieved from https://www.nist.gov/system/files/documents/2022/08/18/AI_RMF_2nd_draft.pdf
    NIST. AI Risk Management Framework: 1.0 (2023), Retrieved from https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
    OECD. Policies, Data and Analysis for Trustworthy Artificial Intelligence. Retrieved from https://oecd.ai/en/. Last accessed 6 April 2023.
    OECD. OECD launches framework for classifying AI systems (2022). Retrieved fromhttps://www.oecd-ilibrary.org/docserver/cb6d9eca-en.pdf?expires=1647274180&id=id&accname=guest&checksum=68ACD0EBC793F9EF24824263215774CD
    OECD. OECD AI Principles overview. Retrieved from https://oecd.ai/en/ai-principles. Last accessed 7 December 2022.
    Rivest. R. L. Cryptography and machine learning. International Conference on the Theory and Application of Cryptology, 739 (2005).
    Sanford, A. & Moosa, I. Operational risk modelling and organizational learning in structured fnance operations: A Bayesian network approach. Journal of the Operational Research Society, 66(1), 86-115 (2015).
    Schmitz. S., Schluetter. M. & Epple. U. Automation of Automation — Definition, components and challenges (2009).
    Shieber, S. M. The turing test: Verbal behavior as the hallmark of intelligence. Cambridge: MIT Press (2004).
    Singh, H. Adopting RegTech: A practical guide. Journal of Financial Compliance. Henry Stewart Publications. 6(15). 80-94 (2022).
    Son, Y., Byun, H. & Lee, J. Nonparametric machine learning models for predicting the credit default swaps: An empirical study. Expert Systems with Applications. 58. 210-220 (2016).
    Subramanya. S. R. & Yi. B. K. Digital Signatures. IEEE Potentials, 25(2), 5-8 (2006).
    Taylor, C. R., Wilson, C., Holttinen, E., & Morozova, A. Institutional Arrangements for Fintech Regulation and Supervision. IMF (2020). Retrieved from https://www.imf.org/en/Publications/fintech-notes/Issues/2020/01/09/Institutional-Arrangements-for-Fintech-Regulation-and-Supervision-48809
    The Wolfsberg Group. Wolfsberg Financial Crime Principles for Correspondent Banking (2022). Retrieved from https://www.wolfsberg-principles.com/sites/default/files/wb/pdfs/wolfsberg-standards/15.%20Wolfsberg_RBA_Guidance_%282006%29.pdf
    Van Liebergen, B. Machine learning: A revolution in risk management and compliance?. Journal of Financial Transformation, 45, 60-67 (2017).
    Walport, M. FinTech Futures: The UK as a World Leader in Financial Technologiesh-A report by the UK Government Chief Scientific Adviser (2015), Retrieved from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/413095/gs-15-3-fintech-futures.pdf
    Wellman, B. Network analysis: Some basic principles. Sociological Theory, 1, 155-200 (1983).
    Wil M. P. Aalst. V. D., Bichler. M. & Heinzl. A. Robotic Process Automation. Business & Information Systems Engineering, 60, 269–272 (2018).
    Wilson, H. J., Daugherty, P. & Bianzino, N. The jobs that artificial intelligence will create. MIT Sloan Management Review, 58(4), 14-16 ( 2017).
    Wolfsberg Group. Wolfsberg Group Endorses Use of AI/ML for Financial Crime Compliance (2022). Retrieved from https://www.wolfsberg-principles.com/sites/default/files/wb/Wolfsberg%20Principles%20for%20Using%20Artificial%20Intelligence%20and%20Machine%20Learning%20in%20Financial%20Crime%20Compliance.pdf
    Woodall, L. Model risk managers eye benefts of machine learning. Risk.net, https://www.risk.net/risk-management/4646956/model-risk-managers-eye-benefts-of-machine-learning (2017). Last accessed 17 August 2018.
    Wu. G., Mu. Y. , Susilo. W., Guo. F. & Zhang. F. Privacy-preserving certificateless cloud auditing with multiple users, Wireless Personal Communications, 106, 1161-1182 (2019).
    Yang, C.C., Shi, X. & Wei C-P. Discovering Event Evolution Graphs from News Corpora. IEEE Transactions on Systems Man and Cybernetics-Part A Systems and Humans, 39(4), 850-863 (2009).
    Description: 碩士
    國立政治大學
    法學院碩士在職專班
    107961053
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107961053
    Data Type: thesis
    Appears in Collections:[Master of Laws Program for Executives] Theses

    Files in This Item:

    File Description SizeFormat
    105301.pdf41698KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback