arXiv subjects

Grzegorz J. Nalepa

Publications and source records attributed to Grzegorz J. Nalepa.

25 records · Page 2Linked to original sources

arXiv2023

DeepVATS: Deep Visual Analytics for Time Series

The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to provide them with large-scale data processing capabilities and to unify their implementation across different data and domains. In this paper we present DeepVATS, an open-source tool that brings the field of DVA into time series data. DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted. The tool includes a back-end for data processing pipeline and model training, as well as a front-end with a interactive user interface. We report on results that validate the utility of DeepVATS, running experiments on both synthetic and real datasets. The code is publicly available on https://github.com/vrodriguezf/deepvats

cs.LG↗

arXiv2022

KnAC: an approach for enhancing cluster analysis with background knowledge and explanations

Pattern discovery in multidimensional data sets has been the subject of research for decades. There exists a wide spectrum of clustering algorithms that can be used for this purpose. However, their practical applications share a common post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue that this can be the bottleneck in the process, especially in cases where domain knowledge exists prior to clustering. Such a situation requires not only a proper analysis of automatically discovered clusters but also conformance checking with existing knowledge. In this work, we present Knowledge Augmented Clustering (KnAC). Its main goal is to confront expert-based labelling with automated clustering for the sake of updating and refining the former. Our solution is not restricted to any existing clustering algorithm. Instead, KnAC can serve as an augmentation of an arbitrary clustering algorithm, making the approach robust and a model-agnostic improvement of any state-of-the-art clustering method. We demonstrate the feasibility of our method on artificially, reproducible examples and in a real life use case scenario. In both cases, we achieved better results than classic clustering algorithms without augmentation.

cs.AI↗

arXiv2021

Jarvis for Aeroengine Analytics: A Speech Enhanced Virtual Reality Demonstrator Based on Mining Knowledge Databases

In this paper, we present a Virtual Reality (VR) based environment where the engineer interacts with incoming data from a fleet of aeroengines. This data takes the form of 3D computer-aided design (CAD) engine models coupled with characteristic plots for the subsystems of each engine. Both the plots and models can be interacted with and manipulated using speech or gestural input. The characteristic data is ported to a knowledge-based system underpinned by a knowledge-graph storing complex domain knowledge. This permits the system to respond to queries about the current state and health of each aeroengine asset. Responses to these questions require some degree of analysis, which is handled by a semantic knowledge representation layer managing information on aeroengine subsystems. This paper represents a significant step forward for aeroengine analysis in a bespoke VR environment and brings us a step closer to a Jarvis-like system for aeroengine analytics.

cs.HC↗

arXiv2020

The BIRAFFE2 Experiment. Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems

The paper describes BIRAFFE2 data set, which is a result of an affective computing experiment conducted between 2019 and 2020, that aimed to develop computer models for classification and recognition of emotion. Such work is important to develop new methods of natural Human-AI interaction. As we believe that models of emotion should be personalized by design, we present an unified paradigm allowing to capture emotional responses of different persons, taking individual personality differences into account. We combine classical psychological paradigms of emotional response collection with the newer approach, based on the observation of the computer game player. By capturing ones psycho-physiological reactions (ECG, EDA signal recording), mimic expressions (facial emotion recognition), subjective valence-arousal balance ratings (widget ratings) and gameplay progression (accelerometer and screencast recording), we provide a framework that can be easily used and developed for the purpose of the machine learning methods.

cs.HC↗

arXiv2019

Generating Local Search Neighborhood with Synthesized Logic Programs

Local Search meta-heuristics have been proven a viable approach to solve difficult optimization problems. Their performance depends strongly on the search space landscape, as defined by a cost function and the selected neighborhood operators. In this paper we present a logic programming based framework, named Noodle, designed to generate bespoke Local Search neighborhoods tailored to specific discrete optimization problems. The proposed system consists of a domain specific language, which is inspired by logic programming, as well as a genetic programming solver, based on the grammar evolution algorithm. We complement the description with a preliminary experimental evaluation, where we synthesize efficient neighborhood operators for the traveling salesman problem, some of which reproduce well-known results.

cs.NE↗

arXiv2018

"AIded with emotions" - a new design approach towards affective computer systems

As technologies become more and more pervasive, there is a need for considering the affective dimension of interaction with computer systems to make them more human-like. Current demands for this matter include accurate emotion recognition, reliable emotion modeling, and use of unobtrusive, easily accessible and preferably wearable measurement devices. While AI methods provide many possibilities for better affective information processing, it is not a common scenario for both emotion recognition and modeling to be integrated in the design phase. To address this concern, we propose a new approach based on affective design patterns in the context of video games, together with summary of experiments conducted to test the preliminary hypotheses.

cs.HC↗

arXiv2011

Visual Inference Specification Methods for Modularized Rulebases. Overview and Integration Proposal

The paper concerns selected rule modularization techniques. Three visual methods for inference specification for modularized rule- bases are described: Drools Flow, BPMN and XTT2. Drools Flow is a popular technology for workflow or process modeling, BPMN is an OMG standard for modeling business processes, and XTT2 is a hierarchical tab- ular system specification method. Because of some limitations of these solutions, several proposals of their integration are given.

cs.AI↗