Never-ending Learning of User Interfaces

Machine learning models have been trained to predict semantic information about user interfaces (UIs) to make apps more accessible, easier to test, and to automate. Currently, most models rely on datasets that are collected and labeled by human crowd-workers, a process that is costly and surprisingly error-prone for certain tasks. For example, it is possible to guess if a UI element is "tappable" from a screenshot (i.e., based on visual signifiers) or from potentially unreliable metadata (e.g., a view hierarchy), but one way to know for certain is to programmatically tap the UI element and observe the effects. We built the Never-ending UI Learner, an app crawler that automatically installs real apps from a mobile app store and crawls them to discover new and challenging training examples to learn from. The Never-ending UI Learner has crawled for more than 5,000 device-hours, performing over half a million actions on 6,000 apps to train three computer vision models for i) tappability prediction, ii) draggability prediction, and iii) screen similarity

Swimming is never without risk: opening up on learning through activism and research

This article examines my own becoming as Elisabeth and as a researcher. It is about working as a support worker, coaching teams that are trying to realize inclusive education for a child, and my PhD process, which relies on these practices. My intention here is to unfold several aspects, blockages, possibilities, and tensions that can make sense of my messy struggle. The never-ending learning through working with people, listening to their stories, and taking responsibility are important ingredients of my engagement. It is necessary to provide insights and justify my multiple positions to avoid falling into a narcissistic trap. In doing so, I will seek help from Levinas and in concepts of Deleuze and Guattari to (re-)construct my own understanding

A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning.

The revolution in information technology and the explosion in the use of computing devices in people\u27s everyday activities has forever changed the perspective of the data mining and machine learning fields. The enormous amounts of easily accessible, information rich data is pushing the data analysis community in general towards a shift of paradigm. In the new paradigm, data comes in the form a stream of billions of records received everyday. The dynamic nature of the data and its sheer size makes it impossible to use the traditional notion of offline learning where the whole data is accessible at any time point. Moreover, no amount of human resources is enough to get expert feedback on the data. In this work we have developed a unified optimization based learning framework that approaches many of the challenges mentioned earlier. Specifically, we developed a Never-Ending Learning framework which combines incremental/online, semi-supervised, and active learning under a unified optimization framework. The established framework is based on the class of submodular optimization methods. At the core of this work we provide a novel formulation of the Semi-Supervised Support Vector Machines (S3VM) in terms of submodular set functions. The new formulation overcomes the non-convexity issues of the S3VM and provides a state of the art solution that is orders of magnitude faster than the cutting edge algorithms in the literature. Next, we provide a stream summarization technique via exemplar selection. This technique makes it possible to keep a fixed size exemplar representation of a data stream that can be used by any label propagation based semi-supervised learning technique. The compact data steam representation allows a wide range of algorithms to be extended to incremental/online learning scenario. Under the same optimization framework, we provide an active learning algorithm that constitute the feedback between the learning machine and an oracle. Finally, the developed Never-Ending Learning framework is essentially transductive in nature. Therefore, our last contribution is an inductive incremental learning technique for incremental training of SVM using the properties of local kernels. We demonstrated through this work the importance and wide applicability of the proposed methodologies

Semi-supervised never-ending learning in rhetorical relation identification

Some languages do not have enough labeled data to obtain good discourse parsing, specially in the relation identification step, and the additional use of unlabeled data is a plausible solution. A workflow is presented that uses a semi-supervised learning approach. Instead of only a pre-defined additional set of unlabeled data, texts obtained from the web are continuously added. This obtains near human perfomance (0.79) in intra sentential rhetorical relation identification. An experiment for English also shows improvement using a similar workflow.São Paulo Research Foundation (FAPESP) (grant♯2014/11632)Natural Sciences and Engineering Research Council of CanadaUniversity of Toront

RICH-CPL: Fact Extraction from Wikipedia-sized Corpora for Morphologically Rich Languages

This work deals with never-ending learning ap- proach for fact extraction from unstructured Russian text. It continues the research in the field of pattern learning techniques for morphologically rich free-word-order language. We introduce improvements for CPL-RUS algorithm and choose best initial pa- rameters. We conducted experiments with the extended version, RICH-CPL algorithm on the corpus containing over 1.3 million pages. This paper is shortened version of our paper [7] that includes also new modifications of the proposed methods

A SOFT SYSTEMS APPROACH TO INFORMATION SYSTEMS QUALITY

Traditional approaches to Information System (IS) development have concentrated upon a production view of quality associated with a controlled development process and metrics that monitor attributes such as software usability, the number of software errors, and developer productivity. IS quality is also concerned with a use view of quality-how those software artefacts are used within an organisational context, recognising the need for a never-ending learning cycle based on experience of the product in use. Soft Systems Methodology (SSM) is proposed as a framework for considering a relevant notion of IS use quality, enabling discussion to take place about the quality requirements of a technical artefact within the context of an organizational setting. Using the rigour of systemic thinking as a basis, criteria for the assessment of IS quality, labelled the 5Es (efficacy, efficiency, elegance, effectiveness and ethicality), are introduced as a way of identifying the aspects of IS quality that are of concern. A modified form of SSM that incorporates stakeholder analysis and an emphasis on the cultural aspects of quality is proposed for the definition of a relevant (in-context) notion of IS quality