25,762 research outputs found
Inferring User Needs and Tasks from User Interactions
The need for search often arises from a broad range of complex information needs or tasks (such as booking travel, buying a house, etc.) which lead to lengthy search processes characterised by distinct stages and goals. While existing search systems are adept at handling simple information needs, they offer limited support for tackling complex tasks. Accurate task representations could be useful in aptly placing users in the task-subtask space and enable systems to contextually target the user, provide them better query suggestions, personalization and recommendations and help in gauging satisfaction. The major focus of this thesis is to work towards task based information retrieval systems - search systems which are adept at understanding, identifying and extracting tasks as well as supporting user’s complex search task missions. This thesis focuses on two major themes: (i) developing efficient algorithms for understanding and extracting search tasks from log user and (ii) leveraging the extracted task information to better serve the user via different applications. Based on log analysis on a tera-byte scale data from a real-world search engine, detailed analysis is provided on user interactions with search engines. On the task extraction side, two bayesian non-parametric methods are proposed to extract subtasks from a complex task and to recursively extract hierarchies of tasks and subtasks. A novel coupled matrix-tensor factorization model is proposed that represents user based on their topical interests and task behaviours. Beyond personalization, the thesis demonstrates that task information provides better context to learn from and proposes a novel neural task context embedding architecture to learn query representations. Finally, the thesis examines implicit signals of user interactions and considers the problem of predicting user’s satisfaction when engaged in complex search tasks. A unified multi-view deep sequential model is proposed to make query and task level satisfaction prediction
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Knowledge modelling for integrating semantic web services in e-government applications
Service integration and domain interoperability are
the basic requirements in the development of current
service-oriented e-Government applications. Semantic
Web and, in particular, Semantic Web Service (SWS)
technology aim to address these issues. However, the integration between e-Government applications and SWS is not an easy task. We argue that a more complex semantic layer needs to be modeled. The aim of our work is to provide an ontological framework that maps such a semantic layer. In this paper, we describe our approach for creating a project-independent and reusable model, and provide a case study that demonstrates its applicability
Autonomy Infused Teleoperation with Application to BCI Manipulation
Robot teleoperation systems face a common set of challenges including
latency, low-dimensional user commands, and asymmetric control inputs. User
control with Brain-Computer Interfaces (BCIs) exacerbates these problems
through especially noisy and erratic low-dimensional motion commands due to the
difficulty in decoding neural activity. We introduce a general framework to
address these challenges through a combination of computer vision, user intent
inference, and arbitration between the human input and autonomous control
schemes. Adjustable levels of assistance allow the system to balance the
operator's capabilities and feelings of comfort and control while compensating
for a task's difficulty. We present experimental results demonstrating
significant performance improvement using the shared-control assistance
framework on adapted rehabilitation benchmarks with two subjects implanted with
intracortical brain-computer interfaces controlling a seven degree-of-freedom
robotic manipulator as a prosthetic. Our results further indicate that shared
assistance mitigates perceived user difficulty and even enables successful
performance on previously infeasible tasks. We showcase the extensibility of
our architecture with applications to quality-of-life tasks such as opening a
door, pouring liquids from containers, and manipulation with novel objects in
densely cluttered environments
On inferring intentions in shared tasks for industrial collaborative robots
Inferring human operators' actions in shared collaborative tasks, plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space but also forces and the execution of a task. In this article, we present a robotic system which is able to identify different human's intentions and to adapt its behavior consequently, only by means of force data. In order to accomplish this aim, three major contributions are presented: (a) force-based operator's intent recognition, (b) force-based dataset of physical human-robot interaction and (c) validation of the whole system in a scenario inspired by a realistic industrial application. This work is an important step towards a more natural and user-friendly manner of physical human-robot interaction in scenarios where humans and robots collaborate in the accomplishment of a task.Peer ReviewedPostprint (published version
ConCall: An information service for researchers based on EdInfo
In this paper, we present new types of web information services, where users and information brokers collaborate in creating a user-adaptive information service. Such services impose a novel task on information brokers: they become responsible for maintaining the inference strategies used in user modeling. In return, information brokers obtain more accurate information about user needs, since the adaptivity ensures that user profiles are kept up to date and consistent with what users actually prefer, not only what they say that they prefer. We illustrate the approach by an example application, in which conference calls are collected and distributed to interested readers
StoryDroid: Automated Generation of Storyboard for Android Apps
Mobile apps are now ubiquitous. Before developing a new app, the development
team usually endeavors painstaking efforts to review many existing apps with
similar purposes. The review process is crucial in the sense that it reduces
market risks and provides inspiration for app development. However, manual
exploration of hundreds of existing apps by different roles (e.g., product
manager, UI/UX designer, developer) in a development team can be ineffective.
For example, it is difficult to completely explore all the functionalities of
the app in a short period of time. Inspired by the conception of storyboard in
movie production, we propose a system, StoryDroid, to automatically generate
the storyboard for Android apps, and assist different roles to review apps
efficiently. Specifically, StoryDroid extracts the activity transition graph
and leverages static analysis techniques to render UI pages to visualize the
storyboard with the rendered pages. The mapping relations between UI pages and
the corresponding implementation code (e.g., layout code, activity code, and
method hierarchy) are also provided to users. Our comprehensive experiments
unveil that StoryDroid is effective and indeed useful to assist app
development. The outputs of StoryDroid enable several potential applications,
such as the recommendation of UI design and layout code
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