Greece

In this paper, we introduce our efficient simple method which can locate all occurrences of pattern P of k subpatterns with “don’t cares” of length m in text S of length n. Our algorithm employs advanced data structure and the Kangaroo method, which can be applied to selected suffixes of the suffix tree of S to answer subsequent queries in O(k) time using a predefined computational method to find all occurrences of pattern P with “don’t cares” in text S in a fast and effective manner.

EANN 2020

Detecting Pattern Efficiently with Don’t Cares

detect

don’t cares

pattern

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

technical paper

21st edition of EANN (International Conference on Engineering Applications of Neural Networks) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually and Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference. 

Knowledge of permeability, a measure of the ability of rocks to allow fluids to 
flow through them, is essential for building accurate models of oil and gas reservoirs. Permeability is best measured in the laboratory using special core analysis (SCAL), but this is expensive and time-consuming. This is the first major work on predicting permeability in the in the UK Continental Shelf (UKCS) based only on routine
core analysis (RCA) data and a machine-learning approach. We present a
comparative analysis of the various machine learning algorithms and validate
the results, using permeability measured on 273 core samples from
104 wells. Results suggest that machine learning can predict permeability with relatively high accuracy. This opens new research directions in
particular in the oil and gas sector.

Predicting Permeability Based On Core Analysis

Robotic grasping of unknown objects in cluttered scenes is already well established, mainly based on advances in Deep Learning methods. A major drawback is the need for a big amount of real-world training data. Furthermore these networks are not interpretable in a sense that it is not clear why certain grasp attempts fail. To make the process of robotic grasping traceable and simplify the overall model we suggest to divide the complex task of robotic grasping into three simpler tasks to find stable grasp points. The first task is to find all grasp points where the gripper can be lowered onto the table without colliding with the object. The second task is to determine for the grasp points and gripper parameters from the first step how the object moves while the gripper is closed. Finally in the third step for all grasp points from the second step it is predicted whether the object slips out of the gripper during lifting. By this simplification it is possible to understand for each grasp point why it is stable and - just as important - why others are unstable or not feasible. In this study we focus on the second task, the prediction of the physical interaction between gripper and object while the gripper is closed. We investigate different Convolutional Neural Network (CNN) architectures and identify the architecture(s) that predict the physical interactions in image space best. We perform the experiments for training data generation in the robot and physics simulator V-REP.

Visual Movement Prediction for Stable Grasp Point Prediction

The aim of the present study is to investigate the separation abilities of three statistical parameters for grouping participants in the visual-motor experiment by their age and gender. These parameters represent different characteristics of the decision-making process and were determined by applying the hierarchical drift diffusion model to the response time and accuracy of the experimental data [1]. The objective function cluster analysis was applied to explore distinct data spaces formed by the parameters’ data. The ability for grouping is assessed and interpreted according to the differences in the subjects’ capabilities to perform the visuo-motor task. The study compares the conclusions based by drift-diffusion model using Bayesian parameter estimation with those based on the cluster analysis in terms of ability to distinguish the performance of different age groups. The investigation of gender effects are uniquely investigated by cluster analysis technique.

Eye Movement Data Analysis

This paper presents a Data Science-oriented application for image classification tasks that is able to automatically: a) gather images needed for training Deep Learning (DL) models with a built-in search engine crawler; b) remove duplicate images; c) sort images using built-in pre-trained DL models or user’s own trained DL model; d) apply data augmentation; e) train a DL classification model; f) evaluate the performance of a DL model and system by using an accuracy calculator as well as the Accuracy Per Consumption (APC), Accuracy Per Energy Cost (APEC), Time to closest APC (TTCAPC) and Time to closest APEC (TTCAPEC) metrics calculators. Experimental results show that the proposed Computer Vision application has several unique features and advantages, proving to be efficient regarding execution time and much easier to use when compared to similar applications.

Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities

Metalearning is a methodology aiming at recommending the most suitable algorithm (or method) from several alternatives for a par-
ticular dataset. Its classification rule is learned over an available training database of datasets. It gradually penetrates to various applications in
computer science and has also the potential to recommend the most suitable statistical estimator for a given dataset. We consider the non-linear regression model. While there are some robust alternatives to the
traditional (and very non-robust) nonlinear least squares available, it is not theoretically known which estimator performs the best for a partic-
ular dataset. In this work, we perform a metalearning study performed over 721 datasets predicting the best nonlinear regression estimator for an independent dataset. The estimators considered here include stan-
dard nonlinear least squares as well as its robust alternatives with a high breakdown point. On the whole, the presented study brings new argu-
ments in favor of the nonlinear least weighted squares estimator, which is based on the idea to assign implicit weights to individual observations
based on outlyingness of their residuals.

A metalearning study for nonlinear robust regression

This presentation shows the application CNNs in semantic segmentation of vineyard images

Semantic Segmentation of Vineyard Images Using Convolutional Neural Networks

Car driver’s performance is affected by physiologic behavior, causing automotive accidents. A system that predicts incoming behaviors is proposed considering vital signs as data to infer by Evolving Systems (EVOS) paradigms and several behaviors as prediction targets. A black-box like approach is proposed. It implements data loging of the vital signs and it applies an evolving prediction paradigm. Long term data log enables post-event analysis and data set building for supervised learning for the prediction paradigms

Personal Car Driver Black Box: a Wearable System for Data Log and Prediction Based on EVOS Paradigms

Real-world time series often present missing values due to sensor malfunctions or human errors. Traditionally, missing values are simply omitted or reconstructed through imputation or interpolation methods. Omitting missing values may cause temporal discontinuity. Reconstruction methods, on the other hand, alter in some way the original time series. In this paper, we consider an application in the field of meteorological variables that exploits end-to-end machine learning. The idea is to entrust the task of dealing with missing values to a suitably trained recurrent neural network that completely by-passes the phase of reconstruction of missing values. A difficult case of reproduction of a rainfall field from five rain gauges in Northern Italy is used as an example, and the results are compared to those computed by more traditional methods. The proposed methodology is general-purpose and can be easily applied to every kind of spatial time series prediction problem, quite common in many environmental studies.

Reconstructing Environmental Variables with Missing Field Data via End-to-End Machine Learning

Transition towards Industry 4.0 relies heavily on manufacturing digitalisation. Digital twin plays a significant role among the pool of relevant technologies as a powerful tool that is expected provide digital access to detailed real-time monitoring of the physical processes and enable significant optimisation due to utilisation of big data acquired from them. Over the past years a significant number of works produced conceptual frameworks of digital twins and discussed their requirements and benefits. The research literature demonstrates application examples and proofs of concepts, although the content is less rich.
This paper presents a generative model based on generative adversarial networks (GAN) for machining vibration data, discusses its performance and analyses the drawbacks. The proposed model includes process parameter inputs used to condition the features of generated signals. The control over the generator and a neural network architecture utilising techniques from style-transfer research provide the means to analyse the signal building blocks learned by the model and explore their relationship. The quality of the learned process representation is demonstrated using a dataset obtained from a machining time-domain simulation. The novel results constitute a critical component of a machining digital twin and open new research directions towards development of comprehensive manufacturing digital twins.

Towards a digital twin with generative adversarial network modelling of machining vibration

This paper introduces a semantic-segmentation guided image recoloring approach of digitized art paintings to enhance the color perception of color- blind people that suffer from protanopia and deuteranopia. Semantic segmentation using trans-fer learning between natural images and art paintings is applied to extract annotat-ed color information. By using a standard technique, the annotated colors are transformed to simulate the effects of protanopia and deuteranopia. Then, a spe-cialized objective function is minimized to recolor only the colors that are signifi-cantly different from the respective simulated ones, because these colors are per-ceived as confusing by the color blind. The effectiveness of the proposed method is demonstrated through its comparison with other algorithms in several experi-mental cases.

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