This paper presents a new probabilistic graphical model used to model and
recognize words representing the names of Tunisian cities. In fact, this work
is based on a dynamic hierarchical Bayesian network. The aim is to find the
best model of Arabic handwriting to reduce the complexity of the recognition
process by permitting the partial recognition. Actually, we propose a
segmentation of the word based on smoothing the vertical histogram projection
using different width values to reduce the error of segmentation. Then, we
extract the characteristics of each cell using the Zernike and HU moments,
which are invariant to rotation, translation and scaling. Our approach is
tested using the IFN / ENIT database, and the experiment results are very
promising.

本文提出了一种新的概率图模型，用于对突尼斯城市名称的文字进行建模和识别。采用基于动态分层贝叶斯网络的方法，采用分割和特征提取技术来减少识别过程中的复杂性和误差，实验结果非常有前途。

用于阿拉伯手写字识别的动态分层贝叶斯网络

Dynamic Hierarchical Bayesian Network for Arabic Handwritten Word  Recognition

We propose a new probabilistic graphical model that jointly models the
difficulties of questions, the abilities of participants and the correct
answers to questions in aptitude testing and crowdsourcing settings. We devise
an active learning/adaptive testing scheme based on a greedy minimization of
expected model entropy, which allows a more efficient resource allocation by
dynamically choosing the next question to be asked based on the previous
responses. We present experimental results that confirm the ability of our
model to infer the required parameters and demonstrate that the adaptive
testing scheme requires fewer questions to obtain the same accuracy as a static
test scenario.

提出了一种新的概率图模型，用于联合建模能力测试和众包设置中的问题难度、参与者能力和正确答案。并基于期望模型熵的贪婪最小化制定了一种主动式学习 / 自适应测试方案，通过根据以前的答案动态选择下一个问题来实现更为有效的资源分配。实验结果表明，我们的模型能够推断所需参数，并证明自适应测试方案需要较少的问题来获得与静态测试场景相同的准确性。

如何在不知道答案的情况下评分 -- 一种用于自适应众包和能力测试的贝叶斯图模型

How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical  Model for Adaptive Crowdsourcing and Aptitude Testing

In practical data integration systems, it is common for the data sources
being integrated to provide conflicting information about the same entity.
Consequently, a major challenge for data integration is to derive the most
complete and accurate integrated records from diverse and sometimes conflicting
sources. We term this challenge the truth finding problem. We observe that some
sources are generally more reliable than others, and therefore a good model of
source quality is the key to solving the truth finding problem. In this work,
we propose a probabilistic graphical model that can automatically infer true
records and source quality without any supervision. In contrast to previous
methods, our principled approach leverages a generative process of two types of
errors (false positive and false negative) by modeling two different aspects of
source quality. In so doing, ours is also the first approach designed to merge
multi-valued attribute types. Our method is scalable, due to an efficient
sampling-based inference algorithm that needs very few iterations in practice
and enjoys linear time complexity, with an even faster incremental variant.
Experiments on two real world datasets show that our new method outperforms
existing state-of-the-art approaches to the truth finding problem.

本研究提出了一个基于概率图模型的方法，可以自动推断真实记录和数据源质量，为解决数据集成中存在的真实性问题提供了一种新的方法。在两个真实世界的数据集上进行实验，我们的方法在真实性问题方面优于现有的最先进方法。