Computational Technologies for Fashion Recommendation: A Survey

Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years. Due to the great demand for applications, various fashion recommendation tasks, such as personalized fashion product recommendation, complementary (mix-and-match) recommendation, and outfit recommendation, have been posed and explored in the literature. The continuing research attention and advances impel us to look back and in-depth into the field for a better understanding. In this paper, we comprehensively review recent research efforts on fashion recommendation from a technological perspective. We first introduce fashion recommendation at a macro level and analyse its characteristics and differences with general recommendation tasks. We then clearly categorize different fashion recommendation efforts into several sub-tasks and focus on each sub-task in terms of its problem formulation, research focus, state-of-the-art methods, and limitations. We also summarize the datasets proposed in the literature for use in fashion recommendation studies to give readers a brief illustration. Finally, we discuss several promising directions for future research in this field. Overall, this survey systematically reviews the development of fashion recommendation research. It also discusses the current limitations and gaps between academic research and the real needs of the fashion industry. In the process, we offer a deep insight into how the fashion industry could benefit from fashion recommendation technologies. the computational technologies of fashion recommendation

Crowdsourcing for Engineering Design: Objective Evaluations and Subjective Preferences

Crowdsourcing enables designers to reach out to large numbers of people who may not have been previously considered when designing a new product, listen to their input by aggregating their preferences and evaluations over potential designs, aiming to improve ``good'' and catch ``bad'' design decisions during the early-stage design process. This approach puts human designers--be they industrial designers, engineers, marketers, or executives--at the forefront, with computational crowdsourcing systems on the backend to aggregate subjective preferences (e.g., which next-generation Brand A design best competes stylistically with next-generation Brand B designs?) or objective evaluations (e.g., which military vehicle design has the best situational awareness?). These crowdsourcing aggregation systems are built using probabilistic approaches that account for the irrationality of human behavior (i.e., violations of reflexivity, symmetry, and transitivity), approximated by modern machine learning algorithms and optimization techniques as necessitated by the scale of data (millions of data points, hundreds of thousands of dimensions). This dissertation presents research findings suggesting the unsuitability of current off-the-shelf crowdsourcing aggregation algorithms for real engineering design tasks due to the sparsity of expertise in the crowd, and methods that mitigate this limitation by incorporating appropriate information for expertise prediction. Next, we introduce and interpret a number of new probabilistic models for crowdsourced design to provide large-scale preference prediction and full design space generation, building on statistical and machine learning techniques such as sampling methods, variational inference, and deep representation learning. Finally, we show how these models and algorithms can advance crowdsourcing systems by abstracting away the underlying appropriate yet unwieldy mathematics, to easier-to-use visual interfaces practical for engineering design companies and governmental agencies engaged in complex engineering systems design.PhDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133438/1/aburnap_1.pd

Living with Machines. Ethical Implications and Imaginative Agency as local tactics of dwelling and resistance in Everyday Interactions with Artificial Intelligence

With the widespread of the Internet of things (IoT), algorithms are increasingly managing our everyday life. From navigating our way in cities to keeping track of our health, artificial intelligence has been beneficial to us in many ways. However, its algorithms can also be detrimental as a consequence of biased human programming. The result is that while technological progress delivers more and more human-like artificial intelligence, humans become dehumanised and therefore, disempowered in their everyday interactions with artificial intelligence.The solution(s) is not single-handed and calls for combined interventions at the macro and micro levels. Whilst reviewing recent top-down developments on the front of AI ethics, this article delves into the question of to what extent ordinary citizens can exercise any kind of agency when it comes to artificial intelligence. It does so through a multidimensional approach including analogies and intertextual motions between history, literature, and visual culture. Focussing on the case study of facial recognition software, the article explores the possibilities of imaginative agency as a form of local intelligence capable of dwelling in and contesting [human-made] algorithmic bias

Fair representations in the data domain

Algorithmic fairness is a multi-faceted topic which is of significant consequence to a diverse range of people. The issue that this thesis investigates is a fairness-specific instance of a yet even broader concern — that data can be biased due to spurious correlations. Machine learning models trained on such data learn to exploit these spurious correlations that do not hold in the test distribution. When spurious correlations are found with respect to protected demographic attributes, trained models could be biased towards certain subgroups or populations. A promising approach to counteract biased data is by producing a fair representation as a pre-processing step. The main drawback, however, of existing fair representation learning approaches is that the data often become obscured when projected into an uninterpretable latent space, making intuitive assessment difficult. Noticing that the domain the data resides in is often interpretable, with the structure providing richer information that is easier to understand on a per sample basis, I develop fair representations in the data domain. These convey additional per-sample information that can be easily shared and explained to system designers and stakeholders. This thesis investigates three aspects of fair representations in the data domain. Firstly, I demonstrate a novel application of fair representations to generate counterfactual samples in the data domain. The aim of this application is to promote positive actions to address discrimination in an already existing system; Secondly, I develop a method to produce fair representations in the data domain based on statistical dependence principles; Lastly, I take this approach further, introducing two further methods to achieve fair representations in the data domain based on adversarial learning

Human and Artificial Intelligence

Although tremendous advances have been made in recent years, many real-world problems still cannot be solved by machines alone. Hence, the integration between Human Intelligence and Artificial Intelligence is needed. However, several challenges make this integration complex. The aim of this Special Issue was to provide a large and varied collection of high-level contributions presenting novel approaches and solutions to address the above issues. This Special Issue contains 14 papers (13 research papers and 1 review paper) that deal with various topics related to human–machine interactions and cooperation. Most of these works concern different aspects of recommender systems, which are among the most widespread decision support systems. The domains covered range from healthcare to movies and from biometrics to cultural heritage. However, there are also contributions on vocal assistants and smart interactive technologies. In summary, each paper included in this Special Issue represents a step towards a future with human–machine interactions and cooperation. We hope the readers enjoy reading these articles and may find inspiration for their research activities

Aesthetic Programming

Aesthetic Programming explores the technical as well as cultural imaginaries of programming from its insides. It follows the principle that the growing importance of software requires a new kind of cultural thinking — and curriculum — that can account for, and with which to better understand the politics and aesthetics of algorithmic procedures, data processing and abstraction. It takes a particular interest in power relations that are relatively under-acknowledged in technical subjects, concerning class and capitalism, gender and sexuality, as well as race and the legacies of colonialism. This is not only related to the politics of representation but also nonrepresentation: how power differentials are implicit in code in terms of binary logic, hierarchies, naming of the attributes, and how particular worldviews are reinforced and perpetuated through computation. Using p5.js, it introduces and demonstrates the reflexive practice of aesthetic programming, engaging with learning to program as a way to understand and question existing technological objects and paradigms, and to explore the potential for reprogramming wider eco-socio-technical systems. The book itself follows this approach, and is offered as a computational object open to modification and reversioning

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot