Finnish Kullervo and chinese martial arts stories

Introduction: There are many important Finnish plays but, due to language barrier, Finnish drama is seldom exported, particularly to Hong Kong and China.. Objective: To find out differences in mentality between the Finnish and Chinese peoples by comparing the partially localized Chinese translation of Aleksis Kivi’s tragedy, Kullervo, with genuine Chinese martial arts literature. Methodology: 1. Chapman Chen has translated the Finnish classic, Kullervo, directly from Finnish into Chinese and published it in 2005. 2. In Chen’s Chinese translation, cultural markers are domesticated. On the other hand, values, characterization, plot, and rhythm remain unchanged. 3. According to Gideon Tory, the translator has to strike a golden mean between the norms of the source language and the target language. 4. Lau Tingci lists and explicates the essential components of martial arts drama. 5. According to Ehrnrooth’s “Mentality”, equality is the most important value in Finnish culture. Findings: i. Finland emphasizes independence while China emphasizes bilateral relationships. ii. The Finnish people loves freedom, but Gai Sizung argues that the Chinese people is slavish. iii. Finns are mature while many Chinese are, according to Sun Lung-kee (“The Deep Structure of Chinese Culture”; “The Deep Structure of Chinese Sexuality”), fixated at the oral and anal stages. iv. Finnish society highly values equality while Chinese interpersonal relationships are extremely complicated and hierachical. If Kullervo were a genuine Chinese kungfu story, the plot would be much more convoluted. Conclusion: The differences between Finnish and Chinese mentalities are so significant that partially localized or adapted Chinese translations of Finnish drama may still be able to introduce Finnish culture to the Chinese audience.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.