Collaborating Authors


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.

Time Series Data Mining Algorithms Towards Scalable and Real-Time Behavior Monitoring Artificial Intelligence

In recent years, there have been unprecedented technological advances in sensor technology, and sensors have become more affordable than ever. Thus, sensor-driven data collection is increasingly becoming an attractive and practical option for researchers around the globe. Such data is typically extracted in the form of time series data, which can be investigated with data mining techniques to summarize behaviors of a range of subjects including humans and animals. While enabling cheap and mass collection of data, continuous sensor data recording results in datasets which are big in size and volume, which are challenging to process and analyze with traditional techniques in a timely manner. Such collected sensor data is typically extracted in the form of time series data. There are two main approaches in the literature, namely, shape-based classification and feature-based classification. Shape-based classification determines the best class according to a distance measure. Feature-based classification, on the other hand, measures properties of the time series and finds the best class according to the set of features defined for the time series. In this dissertation, we demonstrate that neither of the two techniques will dominate for some problems, but that some combination of both might be the best. In other words, on a single problem, it might be possible that one of the techniques is better for one subset of the behaviors, and the other technique is better for another subset of behaviors. We introduce a hybrid algorithm to classify behaviors, using both shape and feature measures, in weakly labeled time series data collected from sensors to quantify specific behaviors performed by the subject. We demonstrate that our algorithm can robustly classify real, noisy, and complex datasets, based on a combination of shape and features, and tested our proposed algorithm on real-world datasets.

Harnessing the power of AI to elucidate mobile traffic consumption at city scale


Accurate and timely understanding of mobile traffic consumption patterns, especially at city scale, is becoming increasingly important in the planning of digital infrastructure that can meet the diversifying requirements of end-user applications, including e-healthcare, autonomous cars, and industrial automation. Mobile traffic insights can further inform the provisioning and optimisation of public transportation systems, to reduce both their carbon footprint and citizens' commute times. Nonetheless, in the context of the Covid pandemic, mobile traffic consumption can be also indicative of social events and interactions, hence a potentially useful signal in modelling disease spread. Gaining fine-grained knowledge from mobile network traffic is not straightforward. To begin with, this requires dedicated equipment (probes) for measurements collection, which runs e.g. on network gateways.

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.

Can computers ever replace the classroom?

The Guardian

For a child prodigy, learning didn't always come easily to Derek Haoyang Li. When he was three, his father – a famous educator and author – became so frustrated with his progress in Chinese that he vowed never to teach him again. "He kicked me from here to here," Li told me, moving his arms wide. Yet when Li began school, aged five, things began to click. Five years later, he was selected as one of only 10 students in his home province of Henan to learn to code. At 16, Li beat 15 million kids to first prize in the Chinese Mathematical Olympiad. Among the offers that came in from the country's elite institutions, he decided on an experimental fast-track degree at Jiao Tong University in Shanghai. It would enable him to study maths, while also covering computer science, physics and psychology. In his first year at university, Li was extremely shy.

Artificial Intelligence for Social Good: A Survey Artificial Intelligence

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.

Time Series Classification to Improve Poultry Welfare Machine Learning

-- Poultry farms are an important contributor to the human food chain. Worldwide, humankind keeps an enormous number of domesticated birds (e.g. However, around the world, there have been growing concerns about the quality of life for the livestock in poultry farms; and increasingly vocal demands for improved standards of animal welfare. Recent advances in sensing technologies and machine learning allow the possibility of automatically assessing the health of some individual birds, and employing the lessons learned to improve the welfare for all birds. This task superficially appears to be easy, given the dramatic progress in recent years in classifying human behaviors, and given that human behaviors are presumably more complex. However, as we shall demonstrate, classifying chicken behaviors poses several unique challenges, chief among which is creating a generalizable "dictionary" of behaviors from sparse and noisy data. In this work we introduce a novel time series dictionary learning algorithm that can robustly learn from weakly labeled data sources. Poultry farms are a major source of high-protein and low-fat food. Given the ever-increasing population of the world, the demand for such food sources has been steadily growing.