141 research outputs found
A Virtual Reality Tool for Representing, Visualizing and Updating Deep Learning Models
Deep learning is ubiquitous, but its lack of transparency limits its impact
on several potential application areas. We demonstrate a virtual reality tool
for automating the process of assigning data inputs to different categories. A
dataset is represented as a cloud of points in virtual space. The user explores
the cloud through movement and uses hand gestures to categorise portions of the
cloud. This triggers gradual movements in the cloud: points of the same
category are attracted to each other, different groups are pushed apart, while
points are globally distributed in a way that utilises the entire space. The
space, time, and forces observed in virtual reality can be mapped to
well-defined machine learning concepts, namely the latent space, the training
epochs and the backpropagation. Our tool illustrates how the inner workings of
deep neural networks can be made tangible and transparent. We expect this
approach to accelerate the autonomous development of deep learning applications
by end users in novel areas
Adaptive Automated Machine Learning
The ever-growing demand for machine learning has led to the development of automated machine learning (AutoML) systems that can be used off the shelf by non-experts. Further, the demand for ML applications with high predictive performance exceeds the number of machine learning experts and makes the development of AutoML systems necessary. Automated Machine Learning tackles the problem of finding machine learning models with high predictive performance. Existing approaches incorporating deep learning techniques assume that all data is available at the beginning of the training process (offline learning). They configure and optimise a pipeline of preprocessing, feature engineering, and model selection by choosing suitable hyperparameters in each model pipeline step. Furthermore, they assume that the user is fully aware of the choice and, thus, the consequences of the underlying metric (such as precision, recall, or F1-measure). By variation of this metric, the search for suitable configurations and thus the adaptation of algorithms can be tailored to the user’s needs. With the creation of a vast amount of data from all kinds of sources every day, our capability to process and understand these data sets in a single batch is no longer viable. By training machine learning models incrementally (i.ex. online learning), the flood of data can be processed sequentially within data streams. However, if one assumes an online learning scenario, where an AutoML instance executes on evolving data streams, the question of the best model and its configuration remains open.
In this work, we address the adaptation of AutoML in an offline learning scenario toward a certain utility an end-user might pursue as well as the adaptation of AutoML towards evolving data streams in an online learning scenario with three main contributions:
1. We propose a System that allows the adaptation of AutoML and the search for neural architectures towards a particular utility an end-user might pursue.
2. We introduce an online deep learning framework that fosters the research of deep learning models under the online learning assumption and enables the automated search for neural architectures.
3. We introduce an online AutoML framework that allows the incremental adaptation of ML models.
We evaluate the contributions individually, in accordance with predefined requirements and to state-of-the- art evaluation setups. The outcomes lead us to conclude that (i) AutoML, as well as systems for neural architecture search, can be steered towards individual utilities by learning a designated ranking model from pairwise preferences and using the latter as the target function for the offline learning scenario; (ii) architectual small neural networks are in general suitable assuming an online learning scenario; (iii) the configuration of machine learning pipelines can be automatically be adapted to ever-evolving data streams and lead to better performances
A competencies framework of visual impairments for enabling shared understanding in design
Existing work in Human Computer Interaction and accessibility research has long sought to investigate the experiences of people with visual impairments in order to address their needs through technology design and integrate their participation into different stages of the design process. Yet challenges remain regarding how disabilities are framed in technology design and the extent of involvement of disabled people within it. Furthermore, accessibility is often considered a specialised job and misunderstandings or assumptions about visually impaired people’s experiences and needs occur outside dedicated fields. This thesis presents an ethnomethodology-informed design critique for supporting awareness and shared understanding of visual impairments and accessibility that centres on their experiences, abilities, and participation in early-stage design. This work is rooted in an in-depth empirical investigation of the interactional competencies that people with visual impairments exhibit through their use of technology, which informs and shapes the concept of a Competencies Framework of Visual Impairments. Although past research has established stances for considering the individual abilities of disabled people and other social and relational factors in technology design, by drawing on ethnomethodology and its interest in situated competence this thesis employs an interactional perspective to investigate the practical accomplishments of visually impaired people. Thus, this thesis frames visual impairments in terms of competencies to be considered in the design process, rather than a deficiency or problem to be fixed through technology. Accordingly, this work favours supporting awareness and reflection rather than the design of particular solutions, which are also strongly needed for advancing accessible design at large.
This PhD thesis comprises two main empirical studies branched into three different investigations. The first and second investigations are based on a four-month ethnographic study with visually impaired participants examining their everyday technology practices. The third investigation comprises the design and implementation of a workshop study developed to include people with and without visual impairments in collaborative reflections about technology and accessibility. As such, each investigation informed the ones that followed, revisiting and refining concepts and design materials throughout the thesis. Although ethnomethodology is the overarching approach running through this PhD project, each investigation has a different focus of enquiry:
• The first is focused on analysing participants’ technology practices and unearthing the interactional competencies enabling them.
• The second is focused on analysing technology demonstrations, which were a pervasive phenomenon recorded during fieldwork, and the work of demonstrating as exhibited by visually impaired participants.
• Lastly, the third investigation defines a workshop approach employing video demonstrations and a deck of reflective design cards as building blocks for enabling shared understanding among people with and without visual impairments from different technology backgrounds; that is, users, technologists, designers, and researchers.
Overall, this thesis makes several contributions to audiences within and outside academia, such as the detailed accounts of some of the main technology practices of people with visual impairments and the methodological analysis of demonstrations in empirical Human Computer Interaction and accessibility research. Moreover, the main contribution lies in the conceptualisation of a Competencies Framework of Visual Impairments from the empirical analysis of interactional competencies and their practical exhibition through demonstrations, as well as the creation and use of a deck of cards that encapsulates the competencies and external elements involved in the everyday interactional accomplishments of people with visual impairments. All these contributions are lastly brought together in the implementation of the workshop approach that enabled participants to interact with and learn from each other. Thus, this thesis builds upon and advances contemporary strands of work in Human Computer Interaction that call for re-orienting how visual impairments and, overall, disabilities are framed in technology design, and ultimately for re-shaping the design practice itself
Geographic information extraction from texts
A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction
A Deep Generative Model for Interactive Data Annotation through Direct Manipulation in Latent Space
The impact of machine learning (ML) in many fields of application is
constrained by lack of annotated data. Among existing tools for ML-assisted
data annotation, one little explored tool type relies on an analogy between the
coordinates of a graphical user interface and the latent space of a neural
network for interaction through direct manipulation. In the present work, we 1)
expand the paradigm by proposing two new analogies: time and force as
reflecting iterations and gradients of network training; 2) propose a network
model for learning a compact graphical representation of the data that takes
into account both its internal structure and user provided annotations; and 3)
investigate the impact of model hyperparameters on the learned graphical
representations of the data, identifying candidate model variants for a future
user study
Modern Socio-Technical Perspectives on Privacy
This open access book provides researchers and professionals with a foundational understanding of online privacy as well as insight into the socio-technical privacy issues that are most pertinent to modern information systems, covering several modern topics (e.g., privacy in social media, IoT) and underexplored areas (e.g., privacy accessibility, privacy for vulnerable populations, cross-cultural privacy). The book is structured in four parts, which follow after an introduction to privacy on both a technical and social level: Privacy Theory and Methods covers a range of theoretical lenses through which one can view the concept of privacy. The chapters in this part relate to modern privacy phenomena, thus emphasizing its relevance to our digital, networked lives. Next, Domains covers a number of areas in which privacy concerns and implications are particularly salient, including among others social media, healthcare, smart cities, wearable IT, and trackers. The Audiences section then highlights audiences that have traditionally been ignored when creating privacy-preserving experiences: people from other (non-Western) cultures, people with accessibility needs, adolescents, and people who are underrepresented in terms of their race, class, gender or sexual identity, religion or some combination. Finally, the chapters in Moving Forward outline approaches to privacy that move beyond one-size-fits-all solutions, explore ethical considerations, and describe the regulatory landscape that governs privacy through laws and policies. Perhaps even more so than the other chapters in this book, these chapters are forward-looking by using current personalized, ethical and legal approaches as a starting point for re-conceptualizations of privacy to serve the modern technological landscape. The book’s primary goal is to inform IT students, researchers, and professionals about both the fundamentals of online privacy and the issues that are most pertinent to modern information systems. Lecturers or teacherscan assign (parts of) the book for a “professional issues” course. IT professionals may select chapters covering domains and audiences relevant to their field of work, as well as the Moving Forward chapters that cover ethical and legal aspects. Academicswho are interested in studying privacy or privacy-related topics will find a broad introduction in both technical and social aspects
EG-ICE 2021 Workshop on Intelligent Computing in Engineering
The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways
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Privacy-aware Smart Home Interface Framework
Smart home user interfaces are pervasive and shared by multiple users who occupy the space. Therefore, they pose a risk to interpersonal privacy of occupants because an individual’s sensitive information can be leaked to other co-occupants (information privacy), or they can be disturbed by intrusions into their personal space (physical privacy) when the co-occupant interacts with the smart home user interfaces. This thesis hypothesises that interpersonal privacy violations can be mitigated by adapting the user interface layer and presents insights into how to achieve usable user interface adaptation to mitigate or minimise interpersonal privacy violations in smart homes.
The thesis reports two case studies and two user studies. The first case study identifies the key characteristics needed to model the rich context of interpersonal privacy violations scenarios. Then it presents knowledge representation models that are required to represent the identified characteristics and evaluates them for adequacy in modelling the context information of interpersonal privacy violation scenarios. The second case study presents a software architecture and a set of algorithms that can detect interpersonal privacy violations and generate usable user interface adaptations. Then it evaluates the architecture and the algorithms for adequacy in generating usable privacy-aware user interface adaptations. The first user study (N=15) evaluates the usability of the adaptive user interfaces generated from the framework where storyboards were used as the stimulant. Extending the findings from the usability study and expanding the coverage of example scenarios, the second user study (N=23) evaluates the overall user experience of the adaptive user interfaces, using video prototypes as the stimulant.
The research demonstrates that the characteristics identified, and the respective knowledge representation models adequately captured the context of interpersonal privacy violation scenarios. Furthermore, the software architecture and the algorithms could detect possible interpersonal privacy violations and generate usable user interface adaptations to mitigate them. The two user studies demonstrate that the adaptive user interfaces, when used in appropriate situations, were a suitable solution for addressing interpersonal privacy violations while providing high usability and a positive user experience. The thesis concludes by providing recommendations for developing privacy-aware user interface adaptations and suggesting future work that can extend this research
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