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Twitter mood predicts the stock market.

A prevalent and pervasive view of designing is that it can be modeled using variables and decisions made about what values should be taken by these variables. The activity of designing is carried out with the expectation that the designed artifact will operate in the natural world and the social world. These worlds impose constraints on the variables and their values; so, design could be described as a goal-oriented, constrained, decision- making activity. However, design distinguish- es itself from other similarly described activities not only by its domain but also by additional necessary features. Designing involves exploration, exploring what variables might be appropriate. The process of explo- ration involves both goal variables and deci- sion variables. In addition, designing involves learning: Part of the exploration activity is learning about emerging features as a design proceeds. Finally, design activity occurs within two contexts: the context within which the designer operates and the context produced by the developing design itself. The designer’s perception of what the context is affects the implication of the context on the design. The context shifts as the designer’s perceptions change. Design activity can be now characterized as a goal-oriented, con- strained, decision-making, exploration, and learning activity that operates within a con- text that depends on the designer’s percep- tion of the context.

Design Prototypes: A Knowledge Representation Schema for Design

http://www.diva-portal.org/smash/get/diva2:710518/FULLTEXT02

A review of unsupervised feature learning and deep learning for time-series modeling ☆

This study investigates the effectiveness of using e-learning in teaching in tertiary institutions. In institutions of higher education, the issue of utilizing modern information and communication technologies for teaching and learning is very important. This study reviews literature and gives a scholarly background to the study by reviewing some contributions made by various researchers and institutions on the concept of e-learning, particularly its usage in teaching and learning in higher educational institutions. It unveils some views that people and institutions have shared globally on the adoption and integration of e-learning technologies in education through surveys and other observations. It looks at the meaning or definitions of e-learning as given by different researchers and the role that e-learning plays in higher educational institutions in relation to teaching and learning processes, and the advantages and disadvantages of its adoption and implemention.

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The role of e-learning, the advantages and disadvantages of its adoption in Higher Education.

http://www.upv.es/deioac/Investigacion/revision.pdf

The hybrid flow shop scheduling problem

Recent studies show that there is a significant bidirectional nonlinear causality between stock return and trading volume. In this research, we reinforce this statement and the results presented in some earlier literatures and further investigate whether trading volume can significantly improve the prediction performance of neural networks under short-, medium-and long-term forecasting horizons. An application of component-based neural networks is used in forecasting one-step ahead stock index increments. The models are also augmented by the addition of different combinations of indices' and component stocks' trading volumes as inputs to form more general ex-ante forecasting models. Neural networks are trained with the data of stock returns and volumes from NASDAQ, DJIA and STI indices. Results indicate that augmented neural network models with trading volumes lead to improvements, at different extents, in forecasting performance under different terms of forecasting horizon. Empirical results indicate that trading volumes lead to modest improvements on the performance of stock index increments prediction under medium-and long-term horizons.

Predicting stock index increments by neural networks: The role of trading volume under different horizons

For the past three decades or so the flexible flow shop (FFS) scheduling problem has attracted many researchers. Numerous research articles have been published on this topic. This study reviews research on the FFS scheduling problem from the past and the present. The solution approaches reviewed range from the optimum to heuristics and to artificial intelligence search techniques. I not only discuss the details from the selected methods and compare them, but also provide insights and suggestions for future research.

Flexible flow shop scheduling: optimum, heuristics and artificial intelligence solutions

A 3PL supplier selection model based on fuzzy sets

Among the many technologies that support knowledge management, electronic learning or e-learning has become as popular as business intelligence. Along with the rapid development of modern communications and information technology and the increasing demand for building and maintaining business continuity capabilities, e-learning has emerged into a viable solution for continuous, on-demand training and organizational learning. It becomes critical for organizations to design and develop an effective e-learning system by using well-researched theories and technologies that fit their organizational environment. As we discovered in our study, a successful e-learning system is supported by many critical success factors (CSFs), and technology is the key factor among the CSFs. We also conducted a comparative study on e-learning theories, technologies and products from the perspectives of both western practitioners and Chinese counterparts. Due to the rapid economic development in China and its fast adoption of communications and information technology, the ways Chinese practitioners are developing e-learning theories, technologies and products have inevitably become a focal point in the world of information technology. Through the analysis of the current e-learning technologies and products, we outline the trends of developing e-learning systems and emphasize the importance of proper theories in guiding e-learning systems development. Copyright © 2009 John Wiley & Sons, Ltd.

A comparative study on e‐learning technologies and products: from the East to the West

As a new kind of intelligence optimization method, genetic algorithms, with the features of simple structure and strong adaptability, achieves great success in many real applications. However, it has many shortcomings such as a greater computation complexity and more chance of being trapped in local states. In this paper, through analyzing the deficiency of the existing genetic operation and the essential characteristics of creature evolution from the angle of improving evolution efficiency, we propose a compound mutation strategy based on mutation criteria function, a multi-reserved strategy based on intelligence evolution, and a weak arithmetic crossover strategy reflecting different evolution modes. Furthermore, we establish an intelligent bionic genetic algorithm with structural features (denoted by IB-GA, for short). Finally, we analyze the performances of IB-GA with the theory of Markov chains and simulation technology. The results indicate that IB-GA is essentially an extension of ordinary GA and obviously better than ordinary GA in terms of computation efficiency and convergence performance.

Hong Wang

Papers

Predicting stock index increments by neural networks: The role of trading volume under different horizons

Flexible flow shop scheduling: optimum, heuristics and artificial intelligence solutions

A 3PL supplier selection model based on fuzzy sets

A comparative study on e‐learning technologies and products: from the East to the West

Intelligent bionic genetic algorithm (IB-GA) and its convergence