Collaborating Authors


How AI, ML, Big Data are facilitating innovation in the fashion industry


The fashion industry is one of the largest industries globally, constituting two percent of the total global GDP (about $3 trillion). A thriving sector in India, according to the latest study by the Indian Brand Equity Foundation (IBEF), the textile industry is estimated at around $108 billion and was expected to reach $223 billion by end of 2021. The fashion industry employs over 105 million people, directly and indirectly, contributing almost 5 percent to the country's GDP. In fact, the sector is one of the largest contributors to India's export. The industry has gone through its course of milestones and innovations -- from the invention of the sewing machine to the rise of online shopping.

Forecasting: theory and practice Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Utilizing Textual Reviews in Latent Factor Models for Recommender Systems Machine Learning

Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are unable to handle large datasets (with millions of observations). We propose a recommender algorithm that combines a rating modelling technique (i.e., Latent Factor Model) with a topic modelling method based on textual reviews (i.e., Latent Dirichlet Allocation), and we extend the algorithm such that it allows adding extra user- and item-specific information to the system. We evaluate the performance of the algorithm using datasets with different sizes, corresponding to 23 product categories. After comparing the built model to four other models we found that combining textual reviews with ratings leads to better recommendations. Moreover, we found that adding extra user and item features to the model increases its prediction accuracy, which is especially true for medium and large datasets.

Global Big Data Conference


Artificial intelligence and machine learning (AI/ML) have made inroads into enterprises for a variety of different uses, including decision support, product recommendations and process control. These fields are employing big-data concepts to train software algorithms to evaluate data and respond in a similar manner to human decision-makers. These systems are boosted by data collected in the problem domain and used to successively adjust the algorithms to model that domain. For example, a retailer might use detailed data on sales experiences to recommend additional products for shoppers to purchase. By correlating purchases made by past customers, the retailer may be able to entice shoppers to make larger purchases than they had originally intended.

Managing Uncertainty With AI and Big Data Beyond the Pandemic


Throughout the global pandemic, people in every step of life were forced to interact with and rely on technology in new ways. Older generations adopted new habits like online grocery shopping, businesses quickly shifted to virtual meetings, and processes like vaccine distribution required a collaborative use of both AI and mass notification technology across all levels of government and industry. These experiences demonstrated how the intentional use of advanced technology can help to improve the lives and well-being of people during times of volatility. Particularly in the last 18 months, an unprecedented level of instability has upended business as usual. As a result, organizations of all kinds are now preparing for unpredictability more than ever before and are turning to modern technology, such as AI and big data, to help them manage these uncharted waters.

The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

Climate Researchers Enlist Big Cloud Providers for Big Data Challenges WSJD - Technology

And the shift hasn't gone unnoticed by the Big Three cloud providers. AWS and others offer subscription-based remote data storage and online tools, and researchers say they can be an affordable alternative to setting up and maintaining their own hardware. The cloud's added computing power can also make it easier for researchers to run machine-learning algorithms designed to identify patterns and extract insights from vast amounts of climate data, for instance, on ocean temperatures and rainfall patterns, as well as decades' worth of satellite imagery. "The data sets are getting larger and larger," said Werner Vogels, chief technology officer of Inc. "So machine learning starts to play a more important role to look for patterns in the data."

Artificial Intelligence: Research Impact on Key Industries; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020) Artificial Intelligence

The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Privacy-Preserving Dynamic Personalized Pricing with Demand Learning Machine Learning

The prevalence of e-commerce has made detailed customers' personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over $T$ time periods with an \emph{unknown} demand function of posted price and personalized information. At each time $t$, the retailer observes an arriving customer's personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third party agent might infer the personalized information and purchase decisions from price changes from the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer's information and purchasing decisions. To this end, we first introduce a notion of \emph{anticipating} $(\varepsilon, \delta)$-differential privacy that is tailored to dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for $d$-dimensional personalized information, our algorithm achieves the expected regret at the order of $\tilde{O}(\varepsilon^{-1} \sqrt{d^3 T})$, when the customers' information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to $\tilde{O}(\sqrt{d^2T} + \varepsilon^{-2} d^2)$

An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting Machine Learning

This paper proposes a new approach to sales forecasting for new products with long lead time but short product life cycle. These SKUs are usually sold for one season only, without any replenishments. An exponential factorization machine (EFM) sales forecast model is developed to solve this problem which not only considers SKU attributes, but also pairwise interactions. The EFM model is significantly different from the original Factorization Machines (FM) from two-fold: (1) the attribute-level formulation for explanatory variables and (2) exponential formulation for the positive response variable. The attribute-level formation excludes infeasible intra-attribute interactions and results in more efficient feature engineering comparing with the conventional one-hot encoding, while the exponential formulation is demonstrated more effective than the log-transformation for the positive but not skewed distributed responses. In order to estimate the parameters, percentage error squares (PES) and error squares (ES) are minimized by a proposed adaptive batch gradient descent method over the training set. Real-world data provided by a footwear retailer in Singapore is used for testing the proposed approach. The forecasting performance in terms of both mean absolute percentage error (MAPE) and mean absolute error (MAE) compares favourably with not only off-the-shelf models but also results reported by extant sales and demand forecasting studies. The effectiveness of the proposed approach is also demonstrated by two external public datasets. Moreover, we prove the theoretical relationships between PES and ES minimization, and present an important property of the PES minimization for regression models; that it trains models to underestimate data. This property fits the situation of sales forecasting where unit-holding cost is much greater than the unit-shortage cost.