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This is an open access book that covers the complete set of experiences and results of the FemTech.dk research which we have had conducted between 2016-2021 – from initiate idea to societal communication.
Diversity in Computer Science: Design Artefacts for Equity and Inclusion presents and documents the principles, results, and learnings behind the research initiative FemTech.dk, which was created in 2016 and continues today as an important part of the Department of Computer Science at the University of Copenhagen’s strategic development for years to come. FemTech.dk was created in 2016 to engage with research within gender and diversity and to explore the role of gender equity as part of digital technology design and development. FemTech.dk considers how and why computer science ... |
In Machine Learning, the datasets used to build models are one of the main factors limiting what these models can achieve and how good their predictive performance is. Machine Learning applications for cyber-security or computer security are numerous including cyber threat mitigation and security infrastructure enhancement through pattern recognition, real-time attack detection, and in-depth penetration testing. Therefore, for these applications in particular, the datasets used to build the models must be carefully thought to be representative of real-world data. |
People use various nonverbal communicative channels to convey emotions, among which facial expressions are considered the most important ones. Thus, automatic Facial Expression Recognition (FER) is a fundamental task to increase the perceptive skills of computers, especially in human-computer interaction. Like humans, state-of-art FER systems are able to recognize emotions from the entire face of a person. |
The analysis of linear ill-posed problems often is carried out in function spaces using tools from functional analysis. However, the numerical solution of these problems typically is computed by first discretizing the problem and then applying tools from finite-dimensional linear algebra. The present paper explores the feasibility of applying the Chebfun package to solve ill-posed problems with a regularize-first approach numerically. This allows a user to work with functions instead of vectors and with integral operators instead of matrices. The solution process therefore is much closer to the analysis of ill-posed problems than standard linear algebra-based solution methods. |
The INTERACT Conferences are an important platform for researchers and practitioners in the field of human-computer interaction (HCI) to showcase their work. They are organised biennially by the International Federation for Information Processing (IFIP) Technical Committee on Human-Computer Interaction (IFIP TC13), an international committee of 30 member national societies and nine Working Groups. INTERACT is truly international in its spirit and has attracted researchers from several countries and cultures. With an emphasis on inclusiveness, it works to lower the barriers that prevent people in developing countries from participating in conferences. As a multidisciplinary field, HCI requires interaction and discussion among diverse people with different interests and backgrounds. T... |
Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain. In practice, these challenges are often coupled with the lack of example pairs, which inhibits the application of supervised learning strategies. To address these challenges, we propose an efficient adversarial video enhancement framework that learns directly from unpaired video examples. In particular, our framework introduces new recurrent cells that consist of interleaved local and global modules for implicit integration of spatial and temporal information. |
omplexity measures aim to characterize the underlying complexity of supervised data. These measures tackle factors hindering the performance of Machine Learning (ML) classifiers like overlap, density, linearity, etc. The state-of-the-art has mainly focused on the dataset perspective of complexity, i.e., offering an estimation of the complexity of the whole dataset. Recently, the instance perspective has also been addressed. In this paper, the hostility measure, a complexity measure offering a multi-level (instance, class, and dataset) perspective of data complexity is proposed. |
Computational intelligence (CI) is the philosophy, architecture, execution, and creation of cognitive paradigms that are biologically and linguistically driven. Neural networks, fuzzy systems, evolutionary computation, learning theory, and probabilistic methods are historically the five main pillars of CI. In this book, CI and the applicable nature of CI are highlighted by explaining different practical applications. This book starts with some applications of CI, and then proceeds with the usage of data mining in the field of CI. The book ends with some real-life and practical applications of deep learning in various fields of studies from video detection to patient disease. |
We study the problem of multimodal embedding-based entity alignment (EA) between different knowledge graphs. Recent works have attempted to incorporate images (visual context) to address EA in a multimodal view. While the benefits of multimodal information have been observed, its negative impacts are non-negligible as injecting images without constraints brings much noise. It also remains unknown under what circumstances or to what extent visual context is truly helpful to the task. |
The lack of fine-grained 3D shape segmentation data is the main obstacle to developing learning-based 3D segmentation techniques. We propose an effective semi-supervised method for learning 3D segmentations from a few labeled 3D shapes and a large amount of unlabeled 3D data. For the unlabeled data, we present a novel multilevel consistency loss to enforce consistency of network predictions between perturbed copies of a 3D shape at multiple levels: point level, part level, and hierarchical level. For the labeled data, we develop a simple yet effective part substitution scheme to augment the labeled 3D shapes with more structural variations to enhance training. |
