Sustainable Prosperity in the New Economy?: Business Organization and High-Tech Employment in the United States

Lazonick explores the origins of the new era of employment insecurity and income inequality, and considers what governments, businesses, and individuals can do about it. He also asks whether the United States can refashion its high-tech business model to generate stable and equitable economic growth.https://research.upjohn.org/up_press/1029/thumbnail.jp

“Less Is More”: New Property Paradigm in the Information Age?

Before striking down laws increasing copyright’s domain, judges and legislators are asking for evidence that information products will be created even if copyright protection is not provided. The future of Internet technology depends on locating this evidence in time to limit expansive copyright. United States law, however, already protects information products under copyright. Hence, this counterfactual evidence that judges request cannot be generated in the United States. In response to the demand for data, American legal scholars have attempted to mine evidence from open software and other non-commercial endeavors on the Internet. However, these endeavors have been dismissed as exceptions or “cults,” unrelated to mainstream industry needs. This Article, for the first time, provides evidence of growth in the commercial software industry without intellectual property protection. Between 1993 and 2010, the software industry in India emerged as the fastest growing in the world, accounting for $76 billion in revenues by 2010. In the same time period, the software industry in India remained unaffected by changes in intellectual property protection for software. By demonstrating industry growth without strong intellectual property protections, the Indian data fills the critical gap in American literature. Moreover, the comparative data from India enables scholars to separate causality from outcomes in specific empirical and analytical studies emerging out of the United States. In the case study of California’s Silicon Valley, for instance, there is a risk that causality may be extrapolated to alternative California statutes, giving rise to errors of second order. The comparative analysis checks this potential inaccuracy. The industry in India also provides illuminating data from contracting practices—decisive evidence of the legal infrastructure firms need and will create by contract, if not found in a priori law. This study equips policy-makers to go beyond the “historic accident” explanation to understand why the software industry flourishes where it does

Publications from NIAS: January 1988-June 2013 (NIAS Report No. R23-2014)

This report has a bibliographic listing of all the publications from NIAS since inception till June 201

Gender dependent word-level emotion detection using global spectral speech features

In this study, global spectral features extracted from word and sentence levels are studied for speech emotion recognition. MFCC (Mel Frequency Cepstral Coefficient) were used as spectral information for recognition purpose. Global spectral features representing gross statistics such as mean of MFCC are used. This study also examine words at different positions (initial, middle and end) separately in a sentence. Word-level feature extraction is used to analyze emotion recognition performance of words at different positions. Word boundaries are manually identified. Gender dependent and independent models are also studied to analyze the gender impact on emotion recognition performance. Berlin’s Emo-DB (Emotional Database) was used for emotional speech dataset. Performance of different classifiers also been studied. NN (Neural Network), KNN (K-Nearest Neighbor) and LDA (Linear Discriminant Analysis) are included in the classifiers. Anger and neutral emotions were also studied. Results showed that, using all 13 MFCC coefficients provide better classification results than other combinations of MFCC coefficients for the mentioned emotions. Words at initial and ending positions provide more emotion, specific information than words at middle position. Gender dependent models are more efficient than gender independent models. Moreover, female are more efficient than male model and female exhibit emotions better than the male. General, NN performs the worst compared to KNN and LDA in classifying anger and neutral. LDA performs better than KNN almost 15% for gender independent model and almost 25% for gender dependent