75,784 research outputs found
Trust in Vehicle-to-Vehicle Communication
In traditional Pedestrian Automatic Emergency Braking (PAEB) system, vehicles equipped with onboard sensors such as radar, camera, and infrared detect pedestrians, alert the driver and/ or automatically take actions to prevent vehicle-pedestrian collision. In some situations, a vehicle may not be able to detect a pedestrian due to blind spots. Such a vehicle could benefit from the sensor data from neighboring vehicles in making such safety critical decisions. We propose a trust model for ensuring shared data are valid and trustworthy for use in making safety critical decisions. Simulation results of the proposed trust model show promise
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Rankings are the primary interface through which many online platforms match
users to items (e.g. news, products, music, video). In these two-sided markets,
not only the users draw utility from the rankings, but the rankings also
determine the utility (e.g. exposure, revenue) for the item providers (e.g.
publishers, sellers, artists, studios). It has already been noted that
myopically optimizing utility to the users, as done by virtually all
learning-to-rank algorithms, can be unfair to the item providers. We,
therefore, present a learning-to-rank approach for explicitly enforcing
merit-based fairness guarantees to groups of items (e.g. articles by the same
publisher, tracks by the same artist). In particular, we propose a learning
algorithm that ensures notions of amortized group fairness, while
simultaneously learning the ranking function from implicit feedback data. The
algorithm takes the form of a controller that integrates unbiased estimators
for both fairness and utility, dynamically adapting both as more data becomes
available. In addition to its rigorous theoretical foundation and convergence
guarantees, we find empirically that the algorithm is highly practical and
robust.Comment: First two authors contributed equally. In Proceedings of the 43rd
International ACM SIGIR Conference on Research and Development in Information
Retrieval 202
Sequential Selection of Correlated Ads by POMDPs
Online advertising has become a key source of revenue for both web search
engines and online publishers. For them, the ability of allocating right ads to
right webpages is critical because any mismatched ads would not only harm web
users' satisfactions but also lower the ad income. In this paper, we study how
online publishers could optimally select ads to maximize their ad incomes over
time. The conventional offline, content-based matching between webpages and ads
is a fine start but cannot solve the problem completely because good matching
does not necessarily lead to good payoff. Moreover, with the limited display
impressions, we need to balance the need of selecting ads to learn true ad
payoffs (exploration) with that of allocating ads to generate high immediate
payoffs based on the current belief (exploitation). In this paper, we address
the problem by employing Partially observable Markov decision processes
(POMDPs) and discuss how to utilize the correlation of ads to improve the
efficiency of the exploration and increase ad incomes in a long run. Our
mathematical derivation shows that the belief states of correlated ads can be
naturally updated using a formula similar to collaborative filtering. To test
our model, a real world ad dataset from a major search engine is collected and
categorized. Experimenting over the data, we provide an analyse of the effect
of the underlying parameters, and demonstrate that our algorithms significantly
outperform other strong baselines
Learning Contextual Bandits in a Non-stationary Environment
Multi-armed bandit algorithms have become a reference solution for handling
the explore/exploit dilemma in recommender systems, and many other important
real-world problems, such as display advertisement. However, such algorithms
usually assume a stationary reward distribution, which hardly holds in practice
as users' preferences are dynamic. This inevitably costs a recommender system
consistent suboptimal performance. In this paper, we consider the situation
where the underlying distribution of reward remains unchanged over (possibly
short) epochs and shifts at unknown time instants. In accordance, we propose a
contextual bandit algorithm that detects possible changes of environment based
on its reward estimation confidence and updates its arm selection strategy
respectively. Rigorous upper regret bound analysis of the proposed algorithm
demonstrates its learning effectiveness in such a non-trivial environment.
Extensive empirical evaluations on both synthetic and real-world datasets for
recommendation confirm its practical utility in a changing environment.Comment: 10 pages, 13 figures, To appear on ACM Special Interest Group on
Information Retrieval (SIGIR) 201
A flexible architecture for privacy-aware trust management
In service-oriented systems a constellation of services cooperate, sharing potentially sensitive information and responsibilities. Cooperation is only possible if the different participants trust each other. As trust may depend on many different factors, in a flexible framework for Trust Management (TM) trust must be computed by combining different types of information. In this paper we describe the TAS3 TM framework which integrates independent TM systems into a single trust decision point. The TM framework supports intricate combinations whilst still remaining easily extensible. It also provides a unified trust evaluation interface to the (authorization framework of the) services. We demonstrate the flexibility of the approach by integrating three distinct TM paradigms: reputation-based TM, credential-based TM, and Key Performance Indicator TM. Finally, we discuss privacy concerns in TM systems and the directions to be taken for the definition of a privacy-friendly TM architecture.\u
Using On-Line Quizzes to Help Students Learn Probability and Statistics
Online quizzes can be an effective and flexible means of helping learners develop key skills in
probability and statistics. Quizzes give instant feedback, to help reinforce correct understanding
and eliminate fundamental errors at an early stage in learning. We will describe our experience of
designing and using quizzes with non-specialist and specialist students, on several different
platforms including, most recently, Moodle. We describe Model Choice, a tool that helps students
identify from a brief scenario the standard family of probability distributions they should work with
to solve a problem. We will emphasize key design aspects of a successful quiz system, such as the
importance of giving informative feedback to the learner. Using a standard platform, such as
Moodle, is likely to require some compromise on design principles but building a stand-alone
system to implement ideal design choices is very time-consuming
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