Mining positive and negative patterns for relevance feature discovery

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences, but many experiments do not support this hypothesis. The innovative technique presented in paper makes a breakthrough for this difficulty. This technique discovers both positive and negative patterns in text documents as higher level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the higher level features. Substantial experiments using this technique on Reuters Corpus Volume 1 and TREC topics show that the proposed approach significantly outperforms both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and pattern based methods on precision, recall and F measures.

Design of experiments for bivariate binary responses modelled by Copula functions

Optimal design for generalized linear models has primarily focused on univariate data. Often experiments are performed that have multiple dependent responses described by regression type models, and it is of interest and of value to design the experiment for all these responses. This requires a multivariate distribution underlying a pre-chosen model for the data. Here, we consider the design of experiments for bivariate binary data which are dependent. We explore Copula functions which provide a rich and flexible class of structures to derive joint distributions for bivariate binary data. We present methods for deriving optimal experimental designs for dependent bivariate binary data using Copulas, and demonstrate that, by including the dependence between responses in the design process, more efficient parameter estimates are obtained than by the usual practice of simply designing for a single variable only. Further, we investigate the robustness of designs with respect to initial parameter estimates and Copula function, and also show the performance of compound criteria within this bivariate binary setting.

Innovation, interaction, and inclusion : heritage luxury brands in collusion with the consumer

Many luxury heritage brands operate on the misconception that heritage is interchangeable with history rather than representative of the emotional response they originally developed in their customer. This idea of heritage as static history inhibits innovation, prevents dynamic renewal and impedes their ability to redefine, strengthen and position their brand in current and emerging marketplaces. This paper examines a number of heritage luxury brands that have successfully identified the original emotional responses they developed in their customers and, through innovative approaches in design, marketing, branding and distribution evoke these responses in contemporary consumers. Using heritage and innovation hand-in-hand, these brands have continued to grow and develop a vision of heritage that incorporates both historical and contemporary ideas to meet emerging customer needs. While what constitutes a ‘luxury’ item is constantly challenged in this era of accessible luxury products, up scaling and aspirational spending, this paper sees consumers’ emotional needs as the key element in defining the concept of luxury. These emotional qualities consistently remain relevant due to their ability to enhance a positive sense of identity for the brand user. Luxury is about the ‘experience’ not just the product providing the consumer with a sense of enhanced status or identity through invoked feelings of exclusivity, authenticity, quality, uniqueness and culture. This paper will analyse luxury heritage brands that have successfully combined these emotional values with those of their ‘heritage’ to create an aura of authenticity and nostalgia that appeals to contemporary consumers. Like luxury, the line where clothing becomes fashion is blurred in the contemporary fashion industry; however, consumer emotion again plays an important role. For example, clothing becomes ‘fashion’ for consumers when it affects their self perception rather than fulfilling basic functions of shelter and protection. Successful luxury heritage brands can enhance consumers’ sense of self by involving them in the ‘experience’ and ‘personality’ of the brand so they see it as a reflection of their own exclusiveness, authentic uniqueness, belonging and cultural value. Innovation is a valuable tool for heritage luxury brands to successfully generate these desired emotional responses and meet the evolving needs of contemporary consumers. While traditionally fashion has been a monologue from brand to consumer, new technology has given consumers a voice to engage brands in a conversation to express their evolving needs, ideas and feedback. As a result, in this consumer-empowered era of information sharing, this paper defines innovation as the ability of heritage luxury brands to develop new design and branding strategies in response to this consumer feedback while retaining the emotional core values of their heritage. This paper analyses how luxury heritage brands can effectively position themselves in the contemporary marketplace by separating heritage from history to incorporate innovative strategies that will appeal to consumer needs of today and tomorrow.

Managing knowledge for enterprise systems in indian organizations: Case study insights

Many Enterprise Systems (ES) projects have reported nil or detrimental impacts despite the substantial investment in the system. Having expected positive outcomes for the organization and its functions through the weighty spend, the effective management of ES-related knowledge has been suggested as a critical success factor for these ES projects in ES implementations. This paper suggests theoretical views purporting the importance of understanding on knowledge management for ES success. To explain the complex, dynamic and multifaceted of knowledge management, we adopt the concepts in Learning Network Theory. We then conceptualized the impact of knowledge management on ES by analyzing five case studies in several industries in India, based on the Knowledge-based Theory of the Firm that captures the performance of the system.

Evaluating multivariate volatility forecasts : how effective are statistical and economic loss functions?

Multivariate volatility forecasts are an important input in many financial applications, in particular portfolio optimisation problems. Given the number of models available and the range of loss functions to discriminate between them, it is obvious that selecting the optimal forecasting model is challenging. The aim of this thesis is to thoroughly investigate how effective many commonly used statistical (MSE and QLIKE) and economic (portfolio variance and portfolio utility) loss functions are at discriminating between competing multivariate volatility forecasts. An analytical investigation of the loss functions is performed to determine whether they identify the correct forecast as the best forecast. This is followed by an extensive simulation study examines the ability of the loss functions to consistently rank forecasts, and their statistical power within tests of predictive ability. For the tests of predictive ability, the model confidence set (MCS) approach of Hansen, Lunde and Nason (2003, 2011) is employed. As well, an empirical study investigates whether simulation findings hold in a realistic setting. In light of these earlier studies, a major empirical study seeks to identify the set of superior multivariate volatility forecasting models from 43 models that use either daily squared returns or realised volatility to generate forecasts. This study also assesses how the choice of volatility proxy affects the ability of the statistical loss functions to discriminate between forecasts. Analysis of the loss functions shows that QLIKE, MSE and portfolio variance can discriminate between multivariate volatility forecasts, while portfolio utility cannot. An examination of the effective loss functions shows that they all can identify the correct forecast at a point in time, however, their ability to discriminate between competing forecasts does vary. That is, QLIKE is identified as the most effective loss function, followed by portfolio variance which is then followed by MSE. The major empirical analysis reports that the optimal set of multivariate volatility forecasting models includes forecasts generated from daily squared returns and realised volatility. Furthermore, it finds that the volatility proxy affects the statistical loss functions’ ability to discriminate between forecasts in tests of predictive ability. These findings deepen our understanding of how to choose between competing multivariate volatility forecasts.

Accelerated leap methods for simulating discrete stochastic chemical kinetics

Biologists are increasingly conscious of the critical role that noise plays in cellular functions such as genetic regulation, often in connection with fluctuations in small numbers of key regulatory molecules. This has inspired the development of models that capture this fundamentally discrete and stochastic nature of cellular biology - most notably the Gillespie stochastic simulation algorithm (SSA). The SSA simulates a temporally homogeneous, discrete-state, continuous-time Markov process, and of course the corresponding probabilities and numbers of each molecular species must all remain positive. While accurately serving this purpose, the SSA can be computationally inefficient due to very small time stepping so faster approximations such as the Poisson and Binomial τ-leap methods have been suggested. This work places these leap methods in the context of numerical methods for the solution of stochastic differential equations (SDEs) driven by Poisson noise. This allows analogues of Euler-Maruyuma, Milstein and even higher order methods to be developed through the Itô-Taylor expansions as well as similar derivative-free Runge-Kutta approaches. Numerical results demonstrate that these novel methods compare favourably with existing techniques for simulating biochemical reactions by more accurately capturing crucial properties such as the mean and variance than existing methods.

Feedback information and consumer motivation : the moderation role of positive and negative reference values in self-regulation

Marketers spend considerable resources to motivate people to consume their products and services as a means of goal attainment (Bagozzi and Dholakia, 1999). Why people increase, decrease, or stop consuming some products is based largely on how well they perceive they are doing in pursuit of their goals (Carver and Scheier, 1992). Yet despite the importance for marketers in understanding how current performance influences a consumer’s future efforts, this topic has received little attention in marketing research. Goal researchers generally agree that feedback about how well or how poorly people are doing in achieving their goals affects their motivation (Bandura and Cervone, 1986; Locke and Latham, 1990). Yet there is less agreement about whether positive and negative performance feedback increases or decreases future effort (Locke and Latham, 1990). For instance, while a customer of a gym might cancel his membership after receiving negative feedback about his fitness, the same negative feedback might cause another customer to visit the gym more often to achieve better results. A similar logic can apply to many products and services from the use of cosmetics to investing in mutual funds. The present research offers managers key insights into how to engage customers and keep them motivated. Given that connecting customers with the company is a top research priority for managers (Marketing Science Institute, 2006), this article provides suggestions for performance metrics including four questions that managers can use to apply the findings.