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Enhancing legal compliance towards corporate anti-corruption has become a tendency to assure corporate operation sustainable. Corruption that jeopardizes construction supervision, project management, operation maintenance remains a governance challenge in public construction and procurement. The research purpose is to explore the impact factors for anti-corruption legal compliance towards top 5 engineering consulting companies in the Taiwan construction industry. A comprehensive literature review brings 8 hypotheses based on the Structural Equation Modeling (SEM) involving 28 stems for a 5-scale Likert questionnaire. The pilot survey with 22 effective returns demonstrates the validation for the questionnaire. The comprehensive survey collects 314 valid returns from top engineering consulting firms and proves that 5 out of 8 hypotheses stand. The findings conclude the legal compliance framework and top 5 impact factors: commitment, ethical management policy, organization and responsibility, prevention programs, and legal compliance policy. The contributions lie on (1) the identification and ranking for the impact factors, and (2) establishment of legal compliance framework toward corporate legal compliance and anti-corruption for top engineering consulting firms in the Taiwan construction industry. The legal compliance framework is correspondingly built to present how engineering consulting companies carry out anti-corruption legal compliance to assure corporate operation sustainable. [ABSTRACT FROM AUTHOR]

This paper describes the winning solution of team National Taiwan University for track 1 of KDD Cup 2013. The track 1 in KDD Cup 2013 considers the paper-author identification problem, which is to identify whether a paper is truly written by an author. First, we conduct feature engineering to transform the various types of provided text information into 97 features. Second, we train classification and ranking models using these features. Last, we combine our individual models to boost the performance by using results on the internal validation set and the official Valid set. Some effective post-processing techniques have also been proposed. Our solution achieves 0.98259 MAP score and ranks the first place on the private leaderboard of the Test set. [ABSTRACT FROM AUTHOR]

Track 2 of KDD Cup 2013 aims at determining duplicated authors in a data set from Microsoft Academic Search. This type of problems appears in many large-scale applications that compile information from different sources. This paper describes our solution developed at National Taiwan University to win the first prize of the competition. We propose an effective name matching framework and realize two implementations. An important strategy in our approach is to consider Chinese and non-Chinese names separately because of their different naming conventions. Post-processing including merging results of two predictions further boosts the performance. Our approach achieves F1-score 0.99202 on the private leader board, while 0.99195 on the public leader board. [ABSTRACT FROM AUTHOR]