Ponencias (Lenguajes y Sistemas Informáticos)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11394

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Análisis de la Presencia de Estudiantes Femeninas en las Escuelas de Ingeniería Informática: Una metodología de Aplicación para cambiar la tendencia
(IEEE, 2022) Romero Moreno, Luisa María; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Las llamadas materias STEM constituyen hoy un enorme foco de interés desde el mundo académico y desde las instituciones, sobre todo, si atendemos a una perspectiva de género. Si se analiza el caso de la Ingeniería Informática los resultados arrojados son enormemente sorprendentes. En el curso 2018-19 las matriculaciones femeninas en Ingeniería Informática (en el caso de España) fueron solamente 1,08 % de las matriculaciones femeninas de nuevo ingreso. Y desde esta fecha parece que la tendencia se mantiene e incluso empeora. También ocurre algo similar en los países de nuestro entorno. En el presente trabajo intentamos recoger datos, realizar una reflexión y dar unas pautas que ayuden a cambiar la tendencia, partiendo de la escasa bibliografía de que se dispone al respecto.
Enhancing Event Log Manipulation and Insight Discovery through Querying Process Representations with DFGs
(CEUR-WS, 2023) Salas Urbano, María; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; MCIN/ AEI/ 10.13039/501100011033/
In this doctoral thesis project we will address some limitations of the current process mining tools to analyze business processes. To achieve this, we propose to develop and evaluate a tool based on a query language for analyzing and visualizing business processes from event logs.
Towards Integrating Interface Behavior Models for Components of Distributed Control Software
(IEEE, 2023) Wiesmayr, Bianca; Gutiérrez Fernández, Antonio Manuel; Zoitl, Alois; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Abstract—Modeling distributed control software facilitates reacting to changing hardware configurations due to the higher abstraction level. Well-designed modules of such systems may be arranged flexibly to perform production tasks, which need to be adapted during the life-cycle of the system. Although IEC 61499 supports modeling the relations between inputs and outputs of its software components as service sequence models, these models are not integrated with the rest of the development process. In this paper, we outline our vision of using the captured information on the interface behavior for development tasks of distributed control software modeled according to IEC 61499. The proposed research directions can contribute to the acceptance of modeling methods in the domain, as appropriate tool support can notably leverage the benefit of models.
Modularization Technique to Support Software Variability in Cyber-Physical Production Systems
(ACM, 2023) Sharma, Shubham; Fadhlillah, Hafiyyan Sayyid; Gutiérrez Fernández, Antonio Manuel; Rabiser, Rick; Zoitl, Alois; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Industries still have problems managing and organizing control software variants for different machine processes in Cyber-Physical Production Systems (CPPSs). They still rely mostly on clone-andown approaches to create new control software variants when introducing new process variability in reaction to customers’ requirements. This approach not only results in code duplication but over time particularly makes it more difficult to maintain and evolve the software. Due to a lack of modularization, this approach also often results in unnecessary code in delivered software, locked by parameters, which can have a further negative effect on maintenance. In this paper, we discuss modularization approaches to organize control software in CPPSs. Specifically, for IEC 61499- based control software, we propose the combination of explicit variants described in 150% modules, standardized interfaces, and separation of concerns. We discuss how our approach could help industry to decrease the effort for new projects and at the same time get a better overview of the product and process variability of their CPPSs.
Managing Cyber-Physical Production Systems Variability using V4rdiac: Industrial Experiences
(ACM, 2023-08-28) Fadhlillah, Hafiyyan Sayyid; Gutiérrez Fernández, Antonio Manuel; Rabiser, Rick; Zoitl, Alois; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Cyber-Physical Production Systems (CPPSs) are highly robust and versatile production systems that utilize diverse hardware components through control software. Employing a systematic variability management approach for developing variants of control software can reduce cost and time-to-market to build such complex systems. However, employing this approach in the CPPS domain is challenging. Engineering CPPSs require multidisciplinary engineering knowledge (e.g., process, signal, mechanical). Knowledge about CPPS variability is thus typically scattered across diverse engineering artifacts. Also, variability knowledge is usually not documented explicitly but rather tacit knowledge of mostly senior engineers. Furthermore, control software is commonly implemented using a graphical Domain-Specific Modeling Language (DSML) which only provides minimal support to express variability. This paper describes our experiences dealing with these challenges in an industrial context using a multidisciplinary variability management approach called Variability for 4diac (V4rdiac). V4rdiac is an integrated approach that allows CPPS engineers to conduct stepwise product configuration based on heterogeneous variability models from multiple engineering disciplines. V4rdiac also provides a mechanism to automatically generate control software based on a set of selected configuration options. We evaluate how V4rdiac implements and manages CPPS control software variants in the metallurgical production plant domain. We describe the benefits and lessons learned from using V4rdiac in this domain based on feedback from industrial practitioners.
Implementing a PLC-Service bus with IEC 61499
(IEEE, 2022) Ashiwal, Virendra; Gutiérrez Fernández, Antonio Manuel; Zoitl, Alois; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
With an increased demand for customization, a better quality of products, and available variability in machines, enforcing machine developers to rethink how PLC-software is structured and developed. It demands the PLC-software to be adaptable, flexible, and modular. Current PLC-software uses global variables for interaction between its program organization units (POUs). Global variables increase tight coupling and reduce adaptability, flexibility, and modularity. A new architectural concept, the PLC-Service bus, was suggested to address this issue. To reduce coupling, POUs and software components of the PLC communicate via a service bus. This concept leads to two problems. There are no standard models to describe these interactions clearly, and there is no implementation of the PLC-Service bus to develop PLC-software based on message interaction patterns (MIPs). This paper uses the capping station from the VDMA R+A OPC UA Demonstrator as a running example. With an Event-driven process chain model (EPC), we describe the interactions between various software components of the capping station to identify MIPs. Based on these MIPs, we implement the PLC-software of the capping station with IEC 61499.
Identifying repeating patterns in IEC 61499 systems using Feature-Based embeddings
(IEEE, 2022) Unterdechler, Markus; Gutiérrez Fernández, Antonio Manuel; Sonnleithner, Lisa; Rabiser, Rick; Zoitl, Alois; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Cyber-Physical Production Systems (CPPSs) are highly variable systems of systems comprised of software and hardware interacting with each other and the environment. The increasing integration of technologies and devices has brought an unprecedented level of automation and customization. At the same time, it has also increased the efforts to maintain highly complex and heterogeneous systems. Although engineering practices support the reuse of common components to ease the development and maintenance of the systems in different projects, the identification of common components is still manually performed, which is a time-consuming, error-prone task. In this paper, a novel approach identifying repeating patterns in CPPSs based on artificial intelligence techniques is presented. This approach allows finding exact and similar components to support the CPPS design. Furthermore, it enables the maintenance of common components by reusing predefined types thereby reducing development effort. We implemented and evaluated our approach in an industry case study on developing CPPS control software with IEC 61499.
Delta Modeling in IEC 61499: Expressing Control Software Variability in Cyber-Physical Production Systems
(IEEE, 2023) Fadhlillah, Hafiyyan Sayyid; Sharma, Shubham; Gutiérrez Fernández, Antonio Manuel; Rabiser, Rick; Zoitl, Alois; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
IEC 61499 is a standard for developing distributed industrial control software. It supports the configurability of the control software as the application model is platformindependent and separated from the system model that defines the hardware configuration specification. Thus, it can be used to develop control software for highly configurable Cyber-Physical Production Systems (CPPS). To address their customers’ requirements, companies that build CPPSs over time develop many variants of such production systems, typically through cloneand- own reuse, which increases engineering and maintenance efforts. Managing the control software variability in CPPSs in a systematic manner would facilitate software reuse and reduce the cost of developing and maintaining these systems. However, the IEC 61499 standard itself offers only limited capabilities for managing variability, e.g., by utilizing a library of modular components or by following certain design patterns. In this paper, we propose an approach to systematically manage IEC 61499- based control software using a (textual) delta modeling approach. Delta modeling is a prominent concept in the Software Product Line (SPL) domain for expressing and maintaining software variability. We show how our delta modeling approach can express control software variability and provide a semi-automatic control software generator. We evaluate the usability of our approach by applying it to multiple case studies as well as by performing a user study, in which control software engineers used our delta modeling approach. Our evaluation results indicate potential benefits of our delta modeling approach to implement and maintain control software through software generation.
Complexity of Structured Text in IEC 61499 Function Blocks: A Survey
(IEEE, 2023) Sonnleithner, Lisa; Wiesmayr, Bianca; Gutiérrez Fernández, Antonio Manuel; Rabiser, Rick; Zoitl, Alois; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
The rising complexity of modern production systems raises the need for evaluating the code quality also in control software engineering. Complexity metrics can help identifying code that is difficult to maintain and may benefit from a refactoring. Although complexity metrics have already been adapted to the domain-specific languages (DSLs) used for cyberphysical production systems (CPPSs), literature on whether developers benefit from using these metrics is limited. Also, the complexity perception of code by the domain experts remains to be investigated. The perceived difficulty of comprehending a program can vary among developers and also between different implementation variants of the same functionality. We therefore asked industrial experts, who have experience in the respective DSLs, to rate the complexity of semantically equivalent implementations to find out which metric best represents their complexity perception and whether there are differences between implementation variants. The examples used in the study were inspired, in parts, by an open source library for IEC 61131-3. We found that the Cognitive Complexity metric is a useful representation of the software complexity in the domain, as it best matched the domain experts’ perception of complexity. These metrics should therefore be supported by the analysis tools.
Apache Kafka as a middleware to support the PLC-service bus architecture with IEC 61499
(Springer Nature, 2023) Ashiwal, Virendra; Gutiérrez Fernández, Antonio Manuel; Aschbacher, Konstantin; Zoitl, Alois; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Flexible, loosely coupled, and adaptable PLC software is required to meet the current market demand for customization, improved product quality, and variability in manufacturing systems. The PLCService bus is an architecture that improves flexibility and adaptability and provides loosely coupled PLC software. This paper integrates Apache Kafka as a network layer into Eclipse 4diacTM (open source IEC 61499 implementation) to use state-of-the-art messaging functionality and implement the PLC-Service bus architecture. With this tool, we can easily configure and orchestrate PLC software based on events. Also, the number of monitor tools for Apache Kafka allows us to avoid ad-hoc developments and use existing out-of-the-box solutions to monitor and analyze the message traffic.
Towards a Systematic Comparison Framework for Cloud Services Customer Agreements
(Springer Nature, 2024) Molino Peña, María Elena; García Rodríguez, José María; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
The growing need to understand and compare elements in service agreements has generated strong interest in the industry. Although there are projects and tools for the automatic detection of information in contracts, automatic analysis is still a developing area of research. This becomes even more relevant with the rise of cloud service organizations, which highlights the need for tools for comparing contractual agreements. In this paper, we present a framework designed to automate contract analysis and comparison. In order to demonstrate the effectiveness of this approach, we created a prototype that uses language models to automatically detect obligations, rights, and parties involved in contracts. In addition, we applied an initial metric to determine the extent to which the customer benefits compared to the provider. The results of the evaluation support the effectiveness of the system by facilitating the understanding and reasoning of both parties regarding the terms of the agreement.
HLNet: a novel hierarchical deep neural network for time series forecasting
(Springer Nature, 2022) Jiménez Navarro, Manuel Jesús; Martínez-Álvarez, F.; Troncoso, A.; Asencio-Cortés, G.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Economía y Competitividad. España
Time series forecasting is a well-known application area for deep learning, in which the historical data are used to predict the future behavior of the series. Several deep learning methods have been proposed in this context, but they usually try to generate the output from the input, with no data transformation. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels, from more simple ones to more complex. Simpler levels handle smoothed versions of the input, while the most complex level processes the original time series. This method follows the human learning process, that is, general tasks are firstly performed and, afterwards, more precise ones are accomplished. Our proposed method has been applied with LSTM architectures, showing remarkable performance in a variety of time series. Moreover, comparisons to standard LSTM are reported.
Electricity Consumption Time Series Forecasting Using Temporal Convolutional Networks
(Springer, 2021) Torres, J. F.; Jiménez Navarro, Manuel Jesús; Martínez-Álvarez, F.; Troncoso, A.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Forecasting electricity demand is crucial for the management of smart grids to ensure a secure, reliable and sustainable supply. Recently, a variant of convolutional neural networks, called temporal convolutional networks, has emerged for data sequence, competing directly with deep recurrent neural networks in terms of execution time and memory requirements. In this work, we propose a deep temporal convolutional network to predict time series, namely, the electricity consumption with a 4-h forecast horizon. Results using nine and a half years of Spanish electricity load, with a 10-min sampling rate, are reported and discussed. In addition, the performance of the proposed model is compared with linear regression, decision trees, gradient boosted trees, random forests, deep feed forward neural networks that use different techniques to find the optimal hyper-parameters and a deep Long Short- Term Memory network. The proposed model reaches competitive results in terms of accuracy, with the smallest error verging on 1%.
A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery
(Springer Nature, 2021) Molina, M.A.; Jiménez Navarro, Manuel Jesús; Martínez-Álvarez, F.; Asencio-Cortés, G.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
A new transfer learning strategy is proposed for classification in this work, based on fully connected neural networks. The transfer learning process consists in a training phase of the neural network on a source dataset. Then, the last two layers are retrained using a different small target dataset. Clustering techniques are also applied in order to determine the most suitable data to be used as target. A preliminary study has been conducted to train and test the transfer learning proposal on the classification problem of phenology forecasting, by using up to sixteen different parcels located in Spain. The results achieved are quite promising and encourage conducting further research in this field, having led to a 7.65% of improvement with respect to other three different strategies with both transfer and non-transfer learning models.
Performance improvement of cardiorespiratory measurements using pressure sensors with mechanical coupling techniques
(Elsevier, 2023) Asadov, Akhmadbek; Ortega Ramírez, Juan Antonio; Martínez Madrid, Natividad; Seepold, Ralf; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Monitoring heart rate and breathing is essential in understanding the physiological processes for sleep analysis. Polysomnography (PSG) system have traditionally been used for sleep monitoring, but alternative methods can help to make sleep monitoring more portable in someone’s home. This study conducted a series of experiments to investigate the use of pressure sensors placed under the bed as an alternative to PSG for monitoring heart rate and breathing during sleep. The following sets of experiments involved the addition of small rubber domes - transparent and black - that were glued to the pressure sensor. The resulting data were compared with the PSG system to determine the accuracy of the pressure sensor readings. The study found that the pressure sensor provided reliable data for extracting heart rate and respiration rate, with mean absolute errors (MAE) of 2.32 and 3.24 for respiration and heart rate, respectively. However, the addition of small rubber hemispheres did not significantly improve the accuracy of the readings, with MAEs of 2.3 bpm and 7.56 breaths per minute for respiration rate and heart rate, respectively. The findings of this study suggest that pressure sensors placed under the bed may serve as a viable alternative to traditional PSG systems for monitoring heart rate and breathing during sleep. These sensors provide a more comfortable and non-invasive method of sleep monitoring. However, the addition of small rubber domes did not significantly enhance the accuracy of the readings, indicating that it may not be a worthwhile addition to the pressure sensor system.
Oral Health Phenotype of Postmenopausal Women Using AI
(Springer, 2024) Kraft, Rodion; Ortega Ramírez, Juan Antonio; Simon-Soro, Aurea; Martínez, Natividad; Ortega Ramírez, Juan Antonio; González Abril, Luis; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Estomatología; Universidad de Sevilla. Departamento de Economía Aplicada I
Menopause is the permanent cessation of menstruation occurring naturally in women’s aging. The most frequent symptoms associated with menopausal phases are mucosal dryness, increased weight and body fat, and changes in sleep patterns. Oral symptoms in menopause derived from saliva flowreduction can lead to dry mouth, ulcers, and alterations of taste and swallowing patterns. However, the oral health phenotype of postmenopausal women has not been characterized. The aim of the study was to determine postmenopausal women’s oral phenotype, including medical history, lifestyle, and oral assessment through artificial intelligence algorithms. We enrolled 100 postmenopausal women attending the Dental School of the University of Seville were included in the study. We collected an extensive questionnaire, including lifestyle, medication, and medical history. We used an unsupervised k-means algorithm to cluster the data following standard features for data analysis. Our results showed the main oral symptoms in our postmenopausal cohort were reduced salivary flow and periodontal disease. Relying on the classical assessment of the collected data, we might have a biased evaluation of postmenopausal women. Then, we used artificial intelligence analysis to evaluate our data obtaining the main features and providing a reduced feature defining the oral health phenotype.We found 6 clusters with similar features, including medication affecting salivation or smoking as essential features to obtain different phenotypes. Thus, we could obtain main features considering differential oral health phenotypes of postmenopausal women with an integrative approach providing new tools to assess the women in the dental clinic.
Influence of Stress in Driving Behaviour
(Springer Nature, 2016) Yay, E.; Martínez Madrid, Natividad; Ortega Ramírez, Juan Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
In the last decades, several driving systems were developed to improve the driving behaviour in energy-efficiency or safety. However, these driving systems cover either the area of energy-efficiency or safety. Furthermore, they do not consider the stress level of the driver when showing a recommendation, although stress can lead to an unsafe or inefficient driving behaviour. In this paper, an approach is presented to consider the driver stress level in a driving system for safe and energyefficient driving behaviour. The driving system tries to suppress a recommendation when the driver is in stress in order not to stress the driver additionally with recommendations in a stressful driving situation. This can lead to an increase in the road safety and in the user acceptance of the driving system, as the driver is not getting bothered or stressed by the driving system. The evaluation of the approach showed, that the driving system is able to show recommendations to the driver, while also reacting to a high stress level by suppressing recommendations in order not to stress the driver additionally.
Experiment design for Stress data collection while driving in a simulator
(Elsevier, 2023) Scherz, Wilhelm Daniel; Seepold, Ralf; Ortega Ramírez, Juan Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
The principal objective of this study is to investigate the impact of perceived stress on traffic and road safety. Therefore, we designed a study that allows the generation and collection of stress-relevant data. Drivers often experience stress due to their perception of lack of control during the driving process. This can lead to an increased likelihood of traffic accidents, driver errors, and traffic violations. To explore this phenomenon, we used the Stress Perceived Questionnaire (PSQ) to evaluate perceived stress levels during driving simulations and the EPQR questionnaire to determine the personality of the driver. With the presented study, participants can categorised based on their emotional stability and personality traits. Wearable devices were utilised to monitor each participant's instantaneous heart rate (HR) due to their non-intrusive and portable nature. The findings of this study deliver an overview of the link between stress and traffic and road safety. These findings can be utilised for future research and implementing strategies to reduce road accidents and promote traffic safety.
Evaluating body movement and breathing signals for identification of sleep/wake states
(Springer, 2022) Gaiduk, Maksym; Seepold, Ralf; Martínez Madrid, Natividad; Penzel, Thomas; Weber, Lucas; Conti, Massimo; Orcioni, Simone; Ortega Ramírez, Juan Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Recognition of sleep andwake states is one of the relevant parts of sleep analysis. Performing this measurement in a contactless way increases comfort for the users. We present an approach evaluating only movement and respiratory signals to achieve recognition, which can be measured non-obtrusively. The algorithm is based on multinomial logistic regression and analyses features extracted out of mentioned above signals. These features were identified and developed after performing fundamental research on characteristics of vital signals during sleep. The achieved accuracy of 87% with the Cohen’s kappa of 0.40 demonstrates the appropriateness of a chosen method and encourages continuing research on this topic.
Definition of Emotional States Interval for Application of Artificial Intelligence and Stress Estimation
(Springer, 2024) Scherz, Wilhelm Daniel; Perea, Juan J.; Seepold, Ralf; Ortega Ramírez, Juan Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
The perception of the amount of stress is subjective to every person, and the perception of it changes depending on many factors. One of the factors that has an impact on perceived stress is the emotional state. In this work, we compare the emotional state of 40 German driving students and present different partitions that can be advantageous for using artificial intelligence and classification. Like this, we evaluate the data quality and prepare for the specific use. The Stress Perceived Questionnaire (PSQ20) was employed to assess the level of stress experienced by individuals while participating in a driving simulation for 5 and 25 min. As a result of our analysis, we present a categorisation of various emotional states into intervals, comparing different classifications and facilitating a more straightforward implementation of artificial intelligence for classification purposes.

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