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Machine learning for activity recognition
This paper surveys the activity recognition task from a machine learning perspective. I give a definition of this problem, and I classify different activity recognition problems into two categories. I show the activities can be hierarchical, and based on such hierarchies I synthesize a language to describe activities. I give a general criteria set to evaluate activity recognition methods. I summarize some off-the-shelf machine learning methods for activity recognition and evaluate them based on this criteria set. Finally, I discuss some methods that I believe can improve the activity recognition performance
Adaptive user support in educational environments: A Bayesian Network approach
This paper is concerned with the design and implementation of an innovative
user support system in the frame of an open educational environment. The
environment adapted is ModelsCreator (MC), an educational system supporting
learning through modelling activities. The pupils typical interaction with the
system was modelled us-ing Bayesian Belief Networks (BBN). This model has been
used in ModelsCreator to build an adaptive help system providing the most
useful guidelines according to the current state of interaction. A brief
description of the system and an overview of application of Bayesian techniques
to educational systems is presented together with discussion about the process
of building of the Bayesian Network derived from actual student interaction
data. A preliminary evaluation of the developed prototype indicates that the
proposed approach produces systems with promising performance
Computational models of attention
This chapter reviews recent computational models of visual attention. We
begin with models for the bottom-up or stimulus-driven guidance of attention to
salient visual items, which we examine in seven different broad categories. We
then examine more complex models which address the top-down or goal-oriented
guidance of attention towards items that are more relevant to the task at hand
Mining Determinism in Human Strategic Behavior
This work lies in the fusion of experimental economics and data mining. It
continues author's previous work on mining behaviour rules of human subjects
from experimental data, where game-theoretic predictions partially fail to
work. Game-theoretic predictions aka equilibria only tend to success with
experienced subjects on specific games, what is rarely given. Apart from game
theory, contemporary experimental economics offers a number of alternative
models. In relevant literature, these models are always biased by psychological
and near-psychological theories and are claimed to be proven by the data. This
work introduces a data mining approach to the problem without using vast
psychological background. Apart from determinism, no other biases are regarded.
Two datasets from different human subject experiments are taken for evaluation.
The first one is a repeated mixed strategy zero sum game and the second -
repeated ultimatum game. As result, the way of mining deterministic
regularities in human strategic behaviour is described and evaluated. As future
work, the design of a new representation formalism is discussed.Comment: 8 pages, no figures, EEML 201
Coordinates: Probabilistic Forecasting of Presence and Availability
We present methods employed in Coordinate, a prototype service that supports
collaboration and communication by learning predictive models that provide
forecasts of users s AND availability.We describe how data IS collected about
USER activity AND proximity FROM multiple devices, IN addition TO analysis OF
the content OF users, the time of day, and day of week. We review applications
of presence forecasting embedded in the Priorities application and then present
details of the Coordinate service that was informed by the earlier efforts.Comment: Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002
Method and apparatus for automatic text input insertion in digital devices with a restricted number of keys
A device which contains number of symbol input keys, where the number of
available keys is less than the number of symbols of an alphabet of any given
language, screen, and dynamic reordering table of the symbols which are mapped
onto those keys, according to a disambiguation method based on the previously
entered symbols. The device incorporates a previously entered keystrokes
tracking mechanism, and the key selected by the user detector, as well as a
mechanism to select the dynamic symbol reordering mapped onto this key
according to the information contained to the reordering table. The reordering
table occurs from a disambiguation method which reorders the symbol appearance.
The reordering information occurs from Bayesian Belief network construction and
training from text corpora of the specific language.Comment: European patent offic
Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions
Treatment effects can be estimated from observational data as the difference
in potential outcomes. In this paper, we address the challenge of estimating
the potential outcome when treatment-dose levels can vary continuously over
time. Further, the outcome variable may not be measured at a regular frequency.
Our proposed solution represents the treatment response curves using linear
time-invariant dynamical systems---this provides a flexible means for modeling
response over time to highly variable dose curves. Moreover, for multivariate
data, the proposed method: uncovers shared structure in treatment response and
the baseline across multiple markers; and, flexibly models challenging
correlation structure both across and within signals over time. For this, we
build upon the framework of multiple-output Gaussian Processes. On simulated
and a challenging clinical dataset, we show significant gains in accuracy over
state-of-the-art models.Comment: In Proceedings of the Thirty-Third Conference on Uncertainty in
Artificial Intelligence (UAI-2017), Sydney, Australia, August 2017. The first
two authors contributed equally to this wor
ALARMS: Alerting and Reasoning Management System for Next Generation Aircraft Hazards
The Next Generation Air Transportation System will introduce new, advanced
sensor technologies into the cockpit. With the introduction of such systems,
the responsibilities of the pilot are expected to dramatically increase. In the
ALARMS (ALerting And Reasoning Management System) project for NASA, we focus on
a key challenge of this environment, the quick and efficient handling of
aircraft sensor alerts. It is infeasible to alert the pilot on the state of all
subsystems at all times. Furthermore, there is uncertainty as to the true
hazard state despite the evidence of the alerts, and there is uncertainty as to
the effect and duration of actions taken to address these alerts. This paper
reports on the first steps in the construction of an application designed to
handle Next Generation alerts. In ALARMS, we have identified 60 different
aircraft subsystems and 20 different underlying hazards. In this paper, we show
how a Bayesian network can be used to derive the state of the underlying
hazards, based on the sensor input. Then, we propose a framework whereby an
automated system can plan to address these hazards in cooperation with the
pilot, using a Time-Dependent Markov Process (TMDP). Different hazards and
pilot states will call for different alerting automation plans. We demonstrate
this emerging application of Bayesian networks and TMDPs to cockpit automation,
for a use case where a small number of hazards are present, and analyze the
resulting alerting automation policies.Comment: Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010
Memory-augmented Dialogue Management for Task-oriented Dialogue Systems
Dialogue management (DM) decides the next action of a dialogue system
according to the current dialogue state, and thus plays a central role in
task-oriented dialogue systems. Since dialogue management requires to have
access to not only local utterances, but also the global semantics of the
entire dialogue session, modeling the long-range history information is a
critical issue. To this end, we propose a novel Memory-Augmented Dialogue
management model (MAD) which employs a memory controller and two additional
memory structures, i.e., a slot-value memory and an external memory. The
slot-value memory tracks the dialogue state by memorizing and updating the
values of semantic slots (for instance, cuisine, price, and location), and the
external memory augments the representation of hidden states of traditional
recurrent neural networks through storing more context information. To update
the dialogue state efficiently, we also propose slot-level attention on user
utterances to extract specific semantic information for each slot. Experiments
show that our model can obtain state-of-the-art performance and outperforms
existing baselines.Comment: 25 pages, 9 figures, Under review of ACM Transactions on Information
Systems (TOIS
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
A fundamental challenge in developing high-impact machine learning
technologies is balancing the need to model rich, structured domains with the
ability to scale to big data. Many important problem areas are both richly
structured and large scale, from social and biological networks, to knowledge
graphs and the Web, to images, video, and natural language. In this paper, we
introduce two new formalisms for modeling structured data, and show that they
can both capture rich structure and scale to big data. The first, hinge-loss
Markov random fields (HL-MRFs), is a new kind of probabilistic graphical model
that generalizes different approaches to convex inference. We unite three
approaches from the randomized algorithms, probabilistic graphical models, and
fuzzy logic communities, showing that all three lead to the same inference
objective. We then define HL-MRFs by generalizing this unified objective. The
second new formalism, probabilistic soft logic (PSL), is a probabilistic
programming language that makes HL-MRFs easy to define using a syntax based on
first-order logic. We introduce an algorithm for inferring most-probable
variable assignments (MAP inference) that is much more scalable than
general-purpose convex optimization methods, because it uses message passing to
take advantage of sparse dependency structures. We then show how to learn the
parameters of HL-MRFs. The learned HL-MRFs are as accurate as analogous
discrete models, but much more scalable. Together, these algorithms enable
HL-MRFs and PSL to model rich, structured data at scales not previously
possible
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