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"Topic": models, code, and papers

Process Mining on Uncertain Event Data

Apr 08, 2022
Marco Pegoraro

With the widespread adoption of process mining in organizations, the field of process science is seeing an increase in the demand for ad-hoc analysis techniques of non-standard event data. An example of such data are uncertain event data: events characterized by a described and quantified attribute imprecision. This paper outlines a research project aimed at developing process mining techniques able to extract insights from uncertain data. We set the basis for this research topic, recapitulate the available literature, and define a future outlook.

* CEUR Workshop Proceedings 3098 (2022) 1-2 
* 2 pages, 1 figure, 2 tables, 9 references 

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Is there an aesthetic component of language?

Feb 19, 2022
Harshit Parmar, Jeffrey P. Williams

Speakers of all human languages make use of grammatical devices to express attributional qualities, feelings, and opinions as well as to provide meta-commentary on topics in discourse. In general, linguists refer to this category as 'expressives'in spite of the fact that defining exactly what 'expressives' are remains elusive. The elusiveness of expressives has given rise to considerable speculation about the nature of expressivity as a linguistic principle. Specifically, several scholars have pointed out the 'special' or 'unusual' nature of expressives vis-a-vis 'normal' or 'natural' morpho-syntax.

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Variable-Length Codes Independent or Closed with respect to Edit Relations

Apr 29, 2021
Jean Néraud

We investigate inference of variable-length codes in other domains of computer science, such as noisy information transmission or information retrieval-storage: in such topics, traditionally mostly constant-length codewords act. The study is relied upon the two concepts of independent and closed sets. We focus to those word relations whose images are computed by applying some peculiar combinations of deletion, insertion, or substitution. In particular, characterizations of variable-length codes that are maximal in the families of $\tau$-independent or $\tau$-closed codes are provided.

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Molecular modeling with machine-learned universal potential functions

Mar 06, 2021
Ke Liu, Zekun Ni, Zhenyu Zhou, Suocheng Tan, Xun Zou, Haoming Xing, Xiangyan Sun, Qi Han, Junqiu Wu, Jie Fan

Molecular modeling is an important topic in drug discovery. Decades of research have led to the development of high quality scalable molecular force fields. In this paper, we show that neural networks can be used to train an universal approximator for energy potential functions. By incorporating a fully automated training process we have been able to train smooth, differentiable, and predictive potential functions on large scale crystal structures. A variety of tests have also performed to show the superiority and versatility of the machine-learned model.

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Activation Functions in Artificial Neural Networks: A Systematic Overview

Jan 25, 2021
Johannes Lederer

Activation functions shape the outputs of artificial neurons and, therefore, are integral parts of neural networks in general and deep learning in particular. Some activation functions, such as logistic and relu, have been used for many decades. But with deep learning becoming a mainstream research topic, new activation functions have mushroomed, leading to confusion in both theory and practice. This paper provides an analytic yet up-to-date overview of popular activation functions and their properties, which makes it a timely resource for anyone who studies or applies neural networks.

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A Fast and Effective Method of Macula Automatic Detection for Retina Images

Oct 07, 2020
Yukang Jiang, Jianying Pan, Yanhe Shen, Jin Zhu, Jiamin Huang, Huirui Xie, Xueqin Wang, Yan Luo

Retina image processing is one of the crucial and popular topics of medical image processing. The macula fovea is responsible for sharp central vision, which is necessary for human behaviors where visual detail is of primary importance, such as reading, writing, driving, etc. This paper proposes a novel method to locate the macula through a series of morphological processing. On the premise of maintaining high accuracy, our approach is simpler and faster than others. Furthermore, for the hospital's real images, our method is also able to detect the macula robustly.

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Vietnamese transition-based dependency parsing with supertag features

Nov 09, 2019
Kiet Van Nguyen, Ngan Luu-Thuy Nguyen

In recent years, dependency parsing is a fascinating research topic and has a lot of applications in natural language processing. In this paper, we present an effective approach to improve dependency parsing by utilizing supertag features. We performed experiments with the transition-based dependency parsing approach because it can take advantage of rich features. Empirical evaluation on Vietnamese Dependency Treebank showed that, we achieved an improvement of 18.92% in labeled attachment score with gold supertags and an improvement of 3.57% with automatic supertags.

* 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE) 

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Reinforcement Learning: Prediction, Control and Value Function Approximation

Aug 28, 2019
Haoqian Li, Thomas Lau

With the increasing power of computers and the rapid development of self-learning methodologies such as machine learning and artificial intelligence, the problem of constructing an automatic Financial Trading Systems (FTFs) becomes an increasingly attractive research topic. An intuitive way of developing such a trading algorithm is to use Reinforcement Learning (RL) algorithms, which does not require model-building. In this paper, we dive into the RL algorithms and illustrate the definitions of the reward function, actions and policy functions in details, as well as introducing algorithms that could be applied to FTFs.

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CS563-QA: A Collection for Evaluating Question Answering Systems

Jul 02, 2019
Katerina Papantoniou, Yannis Tzitzikas

Question Answering (QA) is a challenging topic since it requires tackling the various difficulties of natural language understanding. Since evaluation is important not only for identifying the strong and weak points of the various techniques for QA, but also for facilitating the inception of new methods and techniques, in this paper we present a collection for evaluating QA methods over free text that we have created. Although it is a small collection, it contains cases of increasing difficulty, therefore it has an educational value and it can be used for rapid evaluation of QA systems.

* 11 pages 

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Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"

Jul 01, 2019
Roland S. Zimmermann

A recent paper by Liu et al. combines the topics of adversarial training and Bayesian Neural Networks (BNN) and suggests that adversarially trained BNNs are more robust against adversarial attacks than their non-Bayesian counterparts. Here, I analyze the proposed defense and suggest that one needs to adjust the adversarial attack to incorporate the stochastic nature of a Bayesian network to perform an accurate evaluation of its robustness. Using this new type of attack I show that there appears to be no strong evidence for higher robustness of the adversarially trained BNNs.

* 3 pages 

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