109,271 research outputs found
VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting
Information spread in social media depends on a number of factors, including
how the site displays information, how users navigate it to find items of
interest, users' tastes, and the `virality' of information, i.e., its
propensity to be adopted, or retweeted, upon exposure. Probabilistic models can
learn users' tastes from the history of their item adoptions and recommend new
items to users. However, current models ignore cognitive biases that are known
to affect behavior. Specifically, people pay more attention to items at the top
of a list than those in lower positions. As a consequence, items near the top
of a user's social media stream have higher visibility, and are more likely to
be seen and adopted, than those appearing below. Another bias is due to the
item's fitness: some items have a high propensity to spread upon exposure
regardless of the interests of adopting users. We propose a probabilistic model
that incorporates human cognitive biases and personal relevance in the
generative model of information spread. We use the model to predict how
messages containing URLs spread on Twitter. Our work shows that models of user
behavior that account for cognitive factors can better describe and predict
user behavior in social media.Comment: SBP 201
Towards an architectural framework for intelligent virtual agents using probabilistic programming
We present a new framework called KorraAI for conceiving and building
embodied conversational agents (ECAs). Our framework models ECAs' behavior
considering contextual information, for example, about environment and
interaction time, and uncertain information provided by the human interaction
partner. Moreover, agents built with KorraAI can show proactive behavior, as
they can initiate interactions with human partners. For these purposes, KorraAI
exploits probabilistic programming. Probabilistic models in KorraAI are used to
model its behavior and interactions with the user. They enable adaptation to
the user's preferences and a certain degree of indeterminism in the ECAs to
achieve more natural behavior. Human-like internal states, such as moods,
preferences, and emotions (e.g., surprise), can be modeled in KorraAI with
distributions and Bayesian networks. These models can evolve over time, even
without interaction with the user. ECA models are implemented as plugins and
share a common interface. This enables ECA designers to focus more on the
character they are modeling and less on the technical details, as well as to
store and exchange ECA models. Several applications of KorraAI ECAs are
possible, such as virtual sales agents, customer service agents, virtual
companions, entertainers, or tutors
SPARTA: A Graphical User Interface for Malicious Mobile Code Fingerprint-ing.
This thesis introduces and describes SPARTA (for Stochastic Profiling Application for the Rendering of Trees and Automata), a graphical user interface used as a front end to a collection of tools written in C that collectively convert a log of registry system calls performed by an application into binary descriptions of PSTs (for Probabilistic Suffix Trees) and PSAs (for Probabilistic Suffix Automata), which are models used to represent application behavior on Windows-based systems. SPARTA works by rendering these binary descriptions into graphical form, showcasing a variety of features intended to make the user interaction with PSTs and PSAs informative and insightful.
The ultimate goal of SPARTA is to aid in the process of profiling applications based on the system calls they make, using characteristics from PSTs and PSAs that are more easily noticeable in their graphical form to define “normal” behavior for Windows applications. With knowledge of normal behavior, these very same models can be used to measure deviations that might ultimately result in the destructive actions of malicious mobile code, enabling the user to halt or quarantine them before they take place
Formal Verification of Probabilistic SystemC Models with Statistical Model Checking
Transaction-level modeling with SystemC has been very successful in
describing the behavior of embedded systems by providing high-level executable
models, in which many of them have inherent probabilistic behaviors, e.g.,
random data and unreliable components. It thus is crucial to have both
quantitative and qualitative analysis of the probabilities of system
properties. Such analysis can be conducted by constructing a formal model of
the system under verification and using Probabilistic Model Checking (PMC).
However, this method is infeasible for large systems, due to the state space
explosion. In this article, we demonstrate the successful use of Statistical
Model Checking (SMC) to carry out such analysis directly from large SystemC
models and allow designers to express a wide range of useful properties. The
first contribution of this work is a framework to verify properties expressed
in Bounded Linear Temporal Logic (BLTL) for SystemC models with both timed and
probabilistic characteristics. Second, the framework allows users to expose a
rich set of user-code primitives as atomic propositions in BLTL. Moreover,
users can define their own fine-grained time resolution rather than the
boundary of clock cycles in the SystemC simulation. The third contribution is
an implementation of a statistical model checker. It contains an automatic
monitor generation for producing execution traces of the
model-under-verification (MUV), the mechanism for automatically instrumenting
the MUV, and the interaction with statistical model checking algorithms.Comment: Journal of Software: Evolution and Process. Wiley, 2017. arXiv admin
note: substantial text overlap with arXiv:1507.0818
Latent User Intent Modeling for Sequential Recommenders
Sequential recommender models are essential components of modern industrial
recommender systems. These models learn to predict the next items a user is
likely to interact with based on his/her interaction history on the platform.
Most sequential recommenders however lack a higher-level understanding of user
intents, which often drive user behaviors online. Intent modeling is thus
critical for understanding users and optimizing long-term user experience. We
propose a probabilistic modeling approach and formulate user intent as latent
variables, which are inferred based on user behavior signals using variational
autoencoders (VAE). The recommendation policy is then adjusted accordingly
given the inferred user intent. We demonstrate the effectiveness of the latent
user intent modeling via offline analyses as well as live experiments on a
large-scale industrial recommendation platform.Comment: The Web Conference 2023, Industry Trac
Using Variable Dwell Time to Accelerate Gaze-Based Web Browsing with Two-Step Selection
In order to avoid the "Midas Touch" problem, gaze-based interfaces for
selection often introduce a dwell time: a fixed amount of time the user must
fixate upon an object before it is selected. Past interfaces have used a
uniform dwell time across all objects. Here, we propose a gaze-based browser
using a two-step selection policy with variable dwell time. In the first step,
a command, e.g. "back" or "select", is chosen from a menu using a dwell time
that is constant across the different commands. In the second step, if the
"select" command is chosen, the user selects a hyperlink using a dwell time
that varies between different hyperlinks. We assign shorter dwell times to more
likely hyperlinks and longer dwell times to less likely hyperlinks. In order to
infer the likelihood each hyperlink will be selected, we have developed a
probabilistic model of natural gaze behavior while surfing the web. We have
evaluated a number of heuristic and probabilistic methods for varying the dwell
times using both simulation and experiment. Our results demonstrate that
varying dwell time improves the user experience in comparison with fixed dwell
time, resulting in fewer errors and increased speed. While all of the methods
for varying dwell time resulted in improved performance, the probabilistic
models yielded much greater gains than the simple heuristics. The best
performing model reduces error rate by 50% compared to 100ms uniform dwell time
while maintaining a similar response time. It reduces response time by 60%
compared to 300ms uniform dwell time while maintaining a similar error rate.Comment: This is an Accepted Manuscript of an article published by Taylor &
Francis in the International Journal of Human-Computer Interaction on 30
March, 2018, available online:
http://www.tandfonline.com/10.1080/10447318.2018.1452351 . For an eprint of
the final published article, please access:
https://www.tandfonline.com/eprint/T9d4cNwwRUqXPPiZYm8Z/ful
Data quality maintenance in Data Integration Systems
A Data Integration System (DIS) is an information system that integrates data from a set of heterogeneous and autonomous information sources and provides it to users. Quality in these systems consists of various factors that are measured in data. Some of the usually considered ones are completeness, accuracy, accessibility, freshness, availability. In a DIS, quality factors are associated to the sources, to the extracted and transformed information, and to the information provided by the DIS to the user. At the same time, the user has the possibility of posing quality requirements associated to his data requirements. DIS Quality is considered as better, the nearer it is to the user quality requirements. DIS quality depends on data sources quality, on data transformations and on quality required by users. Therefore, DIS quality is a property that varies in function of the variations of these three other properties. The general goal of this thesis is to provide mechanisms for maintaining DIS quality at a level that satisfies the user quality requirements, minimizing the modifications to the system that are generated by quality changes.
The proposal of this thesis allows constructing and maintaining a DIS that is tolerant to quality changes. This means that the DIS is constructed taking into account previsions of quality behavior, such that if changes occur according to these previsions the system is not affected at all by them. These previsions are provided by models of quality behavior of DIS data, which must be maintained up to date. With this strategy, the DIS is affected only when quality behavior models change, instead of being affected each time there is a quality variation in the system. The thesis has a probabilistic approach, which allows modeling the behavior of the quality factors at the sources and at the DIS, allows the users to state flexible quality requirements (using probabilities), and provides tools, such as certainty, mathematical expectation, etc., that help to decide which quality changes are relevant to the DIS quality. The probabilistic models are monitored in order to detect source quality changes, strategy that allows detecting changes on quality behavior and not only punctual quality changes. We propose to monitor also other DIS properties that affect its quality, and for each of these changes decide if they affect the behavior of DIS quality, taking into account DIS quality models. Finally, the probabilistic approach is also applied at the moment of determining actions to take in order to improve DIS quality. For the interpretation of DIS situation we propose to use statistics, which include, in particular, the history of the quality models
Expectation maximization clustering algorithm for user modeling in web usage mining system
To provide intelligent personalized online services such as web recommender systems, it is usually necessary to model users’ web access behavior. To achieve this, one of the promising approaches is web usage mining, which mines web logs for user models and recommendations. Web usage mining algorithms have been widely utilized for modeling user web navigation behavior. In this study we advance a model for mining of user’s navigation pattern. The model is based on expectation-maximization (EM) algorithm and it is used for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. The experimental results represent that by decreasing the number of clusters, the log likelihood converges toward lower values and probability of the largest cluster will be decreased while the number of
the clusters increases in each treatment. The results also indicate that kind of behavior given by EM clustering algorithm has improved the visit-coherence (accuracy) of
navigation pattern mining
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