CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

Design revolutions: IASDR 2019 Conference Proceedings. Volume 1: Change, Voices, Open

In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 1 includes papers from Change, Voices and Open tracks of the conference

Representation Learning for Texts and Graphs: A Unified Perspective on Efficiency, Multimodality, and Adaptability

[...] This thesis is situated between natural language processing and graph representation learning and investigates selected connections. First, we introduce matrix embeddings as an efficient text representation sensitive to word order. [...] Experiments with ten linguistic probing tasks, 11 supervised, and five unsupervised downstream tasks reveal that vector and matrix embeddings have complementary strengths and that a jointly trained hybrid model outperforms both. Second, a popular pretrained language model, BERT, is distilled into matrix embeddings. [...] The results on the GLUE benchmark show that these models are competitive with other recent contextualized language models while being more efficient in time and space. Third, we compare three model types for text classification: bag-of-words, sequence-, and graph-based models. Experiments on five datasets show that, surprisingly, a wide multilayer perceptron on top of a bag-of-words representation is competitive with recent graph-based approaches, questioning the necessity of graphs synthesized from the text. [...] Fourth, we investigate the connection between text and graph data in document-based recommender systems for citations and subject labels. Experiments on six datasets show that the title as side information improves the performance of autoencoder models. [...] We find that the meaning of item co-occurrence is crucial for the choice of input modalities and an appropriate model. Fifth, we introduce a generic framework for lifelong learning on evolving graphs in which new nodes, edges, and classes appear over time. [...] The results show that by reusing previous parameters in incremental training, it is possible to employ smaller history sizes with only a slight decrease in accuracy compared to training with complete history. Moreover, weighting the binary cross-entropy loss function is crucial to mitigate the problem of class imbalance when detecting newly emerging classes. [...

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

Perspectives on Platform Regulation

Online social media platforms set the agenda and structure for public and private communication in our age. Their influence and power is beyond any traditional media empire. Their legal regulation is a pressing challenge, but currently, they are mainly governed by economic pressures. There are now diverse legislative attempts to regulate platforms in various parts of the world. The European Union and most of its Member States have historically relied on soft law, but are now looking to introduce regulation. Leading researchers of the field analyse the hard questions and the responses given by various states. The book offers legislative solutions from various parts of the world, compares regulatory concepts and assesses the use of algorithms. With contributions by Izumi Aizu, Enni Ala-Mikkula, Alexandre Alaphilippe, Natalie Alkiviadou, Alejandro Aréchiga Morales, Siwal Ashwini, Judit Bayer, Jörg Becker, Konrad Bleyer-Simon, Elda Brogi, Shun-Ling Chen, Poren Chiang, Michael Geist, Gerard Goggin, Giovanni De Gregorio, Sarah Hartmann, Maximilian Hemmert-Halswick, Maria Carolina Herrera Rubio, Bernd Holznagel, Peng Hwa Ang, Richard Janda, Jan Christopher Kalbhenn, Juliya Kharitonova, Kristiina Koivukari, Päivi Korpisaari, Jacob Mchangama, Trisha Meyer, Kilian Müller, Larissa Sannikova, Mårten Schultz, Nicole Stremlau, Maria L. Vazquez, Kuo-Wei Wu and Lorna Woods

CIRA annual report FY 2014/2015

Reporting period July 1, 2014-March 31, 2015

Perspectives on Platform Regulation

Online social media platforms set the agenda and structure for public and private communication in our age. Their influence and power is beyond any traditional media empire. Their legal regulation is a pressing challenge, but currently, they are mainly governed by economic pressures. There are now diverse legislative attempts to regulate platforms in various parts of the world. The European Union and most of its Member States have historically relied on soft law, but are now looking to introduce regulation. Leading researchers of the field analyse the hard questions and the responses given by various states. The book offers legislative solutions from various parts of the world, compares regulatory concepts and assesses the use of algorithms. With contributions by Izumi Aizu, Enni Ala-Mikkula, Alexandre Alaphilippe, Natalie Alkiviadou, Alejandro Aréchiga Morales, Siwal Ashwini, Judit Bayer, Jörg Becker, Konrad Bleyer-Simon, Elda Brogi, Shun-Ling Chen, Poren Chiang, Michael Geist, Gerard Goggin, Giovanni De Gregorio, Sarah Hartmann, Maximilian Hemmert-Halswick, Maria Carolina Herrera Rubio, Bernd Holznagel, Peng Hwa Ang, Richard Janda, Jan Christopher Kalbhenn, Juliya Kharitonova, Kristiina Koivukari, Päivi Korpisaari, Jacob Mchangama, Trisha Meyer, Kilian Müller, Larissa Sannikova, Mårten Schultz, Nicole Stremlau, Maria L. Vazquez, Kuo-Wei Wu and Lorna Woods

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC