F. Basso, Bruno Marcelo Soares Ferreira, Rafael Torres, R. Z. Frantz, D. Kreutz, Maicon Bernardino, Elder de Macedo Rodrigues
Open Services for Lifecycle Collaboration (OSLC) is an open standard for tool interoperability, which allows data federation throughout Software Engineering (SE) application lifecycles. The OSLC community has been active since 2008, and there is still an open question: "What is the state-of-the-art and practice of OSLC for tool integration in Application Lifecycle Management (ALM) for Software Engineering environments?". Objective: To answer this question, our main goal is to map the state-of-the-art and practice on the adoption of OSLC in SE lifecycles. Method: This paper presents a Systematic Mapping Study (SMS) by analyzing 59 primary studies and addressing integration issues such as building SE toolchains. Results: Our findings show that OSLC has been mostly implemented with the development of adapters and MDE. Conclusions: The main advantages of OSLC are related to linked data, involving not only tool adapters for point-to-point integrations, but also proposing solutions for tool replacement in the toolchain, as well as including modifications of OSLC domain specifications and solutions for automated activities for tool integration.
{"title":"Model-Driven Integration and the OSLC Standard: a Mapping of Applied Studies","authors":"F. Basso, Bruno Marcelo Soares Ferreira, Rafael Torres, R. Z. Frantz, D. Kreutz, Maicon Bernardino, Elder de Macedo Rodrigues","doi":"10.1145/3555776.3577761","DOIUrl":"https://doi.org/10.1145/3555776.3577761","url":null,"abstract":"Open Services for Lifecycle Collaboration (OSLC) is an open standard for tool interoperability, which allows data federation throughout Software Engineering (SE) application lifecycles. The OSLC community has been active since 2008, and there is still an open question: \"What is the state-of-the-art and practice of OSLC for tool integration in Application Lifecycle Management (ALM) for Software Engineering environments?\". Objective: To answer this question, our main goal is to map the state-of-the-art and practice on the adoption of OSLC in SE lifecycles. Method: This paper presents a Systematic Mapping Study (SMS) by analyzing 59 primary studies and addressing integration issues such as building SE toolchains. Results: Our findings show that OSLC has been mostly implemented with the development of adapters and MDE. Conclusions: The main advantages of OSLC are related to linked data, involving not only tool adapters for point-to-point integrations, but also proposing solutions for tool replacement in the toolchain, as well as including modifications of OSLC domain specifications and solutions for automated activities for tool integration.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89285469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stock-index movement prediction is an important research topic in FinTech because the index indicates the economic status of a whole country. With a set of daily candlesticks of the stock-index, investors could gain a meaningful basis for the prediction of the next day's movement. This paper proposes a stock-index price-movement prediction model, Combined Time-View TabNet (CTV-TabNet), a novel approach that utilizes attributes of the candlesticks data with multi-time windows. Our model comprises three modules: TabNet encoder, gated recurrent unit with a sequence control, and multi-time combiner. They work together to forecast the movements based on the sequential attributes of the candlesticks. CTV-TabNet not only outperforms baseline models in prediction performance on 20 stock-indices of 14 different countries but also yields higher returns of index-futures trading simulations when compared to the baselines. Additionally, our model provides comprehensive interpretations of the stock-index related to its inherent properties in predictive performance.
{"title":"Exploring Candlesticks and Multi-Time Windows for Forecasting Stock-Index Movements","authors":"Kanghyeon Seo, Jihoon Yang","doi":"10.1145/3555776.3577604","DOIUrl":"https://doi.org/10.1145/3555776.3577604","url":null,"abstract":"Stock-index movement prediction is an important research topic in FinTech because the index indicates the economic status of a whole country. With a set of daily candlesticks of the stock-index, investors could gain a meaningful basis for the prediction of the next day's movement. This paper proposes a stock-index price-movement prediction model, Combined Time-View TabNet (CTV-TabNet), a novel approach that utilizes attributes of the candlesticks data with multi-time windows. Our model comprises three modules: TabNet encoder, gated recurrent unit with a sequence control, and multi-time combiner. They work together to forecast the movements based on the sequential attributes of the candlesticks. CTV-TabNet not only outperforms baseline models in prediction performance on 20 stock-indices of 14 different countries but also yields higher returns of index-futures trading simulations when compared to the baselines. Additionally, our model provides comprehensive interpretations of the stock-index related to its inherent properties in predictive performance.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86773524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shubham Malaviya, Manish Shukla, Pratik Korat, S. Lodha
Federated learning has emerged as a privacy-preserving technique to learn a machine learning model without requiring users to share their data. Our paper focuses on Federated Semi-Supervised Learning (FSSL) setting wherein users do not have domain expertise or incentives to label data on their device, and the server has access to some labeled data that is annotated by experts. The existing work in FSSL require data augmentation for model training. However, data augmentation is not well defined for prevalent domains like text and graphs. Moreover, non independent and identically distributed (non-i.i.d.) data across users is a significant challenge in federated learning. We propose a generalized framework based on model contrastive learning called FedFAME which does not require data augmentation, thus making it easy to adapt to different domains. Our experiments on image and text datasets show the robustness of FedFAME towards non-i.i.d. data. We have validated our approach by varying data imbalance across users and the number of labeled instances on the server.
{"title":"FedFAME: A Data Augmentation Free Framework based on Model Contrastive Learning for Federated Semi-Supervised Learning","authors":"Shubham Malaviya, Manish Shukla, Pratik Korat, S. Lodha","doi":"10.1145/3555776.3577613","DOIUrl":"https://doi.org/10.1145/3555776.3577613","url":null,"abstract":"Federated learning has emerged as a privacy-preserving technique to learn a machine learning model without requiring users to share their data. Our paper focuses on Federated Semi-Supervised Learning (FSSL) setting wherein users do not have domain expertise or incentives to label data on their device, and the server has access to some labeled data that is annotated by experts. The existing work in FSSL require data augmentation for model training. However, data augmentation is not well defined for prevalent domains like text and graphs. Moreover, non independent and identically distributed (non-i.i.d.) data across users is a significant challenge in federated learning. We propose a generalized framework based on model contrastive learning called FedFAME which does not require data augmentation, thus making it easy to adapt to different domains. Our experiments on image and text datasets show the robustness of FedFAME towards non-i.i.d. data. We have validated our approach by varying data imbalance across users and the number of labeled instances on the server.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87588849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the days of AI, data-centric machine learning (ML) models are increasingly used in various complex systems. While many researchers are focusing on specifying ML-specific performance requirements, not enough guideline is provided to engineer the data requirements systematically involving diverse stakeholders. Lack of written agreement about the training data, collaboration bottlenecks, lack of data validation framework, etc. are posing new challenges to ensuring training data fitness for safety-critical ML components. To reduce these gaps, we propose a multi-layered framework that helps to perceive and elicit data requirements. We provide a template for verifiable data requirements specifications. Moreover, we show how such requirements can facilitate an evidence-driven assessment of the training data quality based on the experts' judgments about the satisfaction of the requirements. We use Dempster Shafer's theory to combine experts' subjective opinions in the process. A preliminary case study on the CityPersons dataset for the pedestrian detection feature of autonomous cars shows the usefulness of the proposed framework for data requirements understanding and the confidence assessment of the dataset.
{"title":"A Multi-layered Collaborative Framework for Evidence-driven Data Requirements Engineering for Machine Learning-based Safety-critical Systems","authors":"Sangeeta Dey, Seok-Won Lee","doi":"10.1145/3555776.3577647","DOIUrl":"https://doi.org/10.1145/3555776.3577647","url":null,"abstract":"In the days of AI, data-centric machine learning (ML) models are increasingly used in various complex systems. While many researchers are focusing on specifying ML-specific performance requirements, not enough guideline is provided to engineer the data requirements systematically involving diverse stakeholders. Lack of written agreement about the training data, collaboration bottlenecks, lack of data validation framework, etc. are posing new challenges to ensuring training data fitness for safety-critical ML components. To reduce these gaps, we propose a multi-layered framework that helps to perceive and elicit data requirements. We provide a template for verifiable data requirements specifications. Moreover, we show how such requirements can facilitate an evidence-driven assessment of the training data quality based on the experts' judgments about the satisfaction of the requirements. We use Dempster Shafer's theory to combine experts' subjective opinions in the process. A preliminary case study on the CityPersons dataset for the pedestrian detection feature of autonomous cars shows the usefulness of the proposed framework for data requirements understanding and the confidence assessment of the dataset.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89390161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Pereira, Carlos Gonçalves, Lara Lopes Nunes, P. Cortez, A. Pilastri
Nowadays, the general interest in Machine Learning (ML) based solutions is increasing. However, to develop and deploy a ML solution often requires experience and it involves developing large code scripts. In this paper, we propose AI4CITY, an automated technological platform that aims to reduce the complexity of designing ML solutions, with a particular focus on Smart Cities applications. We compare our solution with popular Automated ML (AutoML) tools (e.g., H2O, AutoGluon) and the results achieved by AI4CITY were quite interesting and competitive.
{"title":"AI4CITY - An Automated Machine Learning Platform for Smart Cities","authors":"P. Pereira, Carlos Gonçalves, Lara Lopes Nunes, P. Cortez, A. Pilastri","doi":"10.1145/3555776.3578740","DOIUrl":"https://doi.org/10.1145/3555776.3578740","url":null,"abstract":"Nowadays, the general interest in Machine Learning (ML) based solutions is increasing. However, to develop and deploy a ML solution often requires experience and it involves developing large code scripts. In this paper, we propose AI4CITY, an automated technological platform that aims to reduce the complexity of designing ML solutions, with a particular focus on Smart Cities applications. We compare our solution with popular Automated ML (AutoML) tools (e.g., H2O, AutoGluon) and the results achieved by AI4CITY were quite interesting and competitive.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83469896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Franceschetti, Roberto Posenato, Carlo Combi, Johann Eder
A Conditional Simple Temporal Network with Uncertainty (CSTNU) models temporal constraint satisfaction problems in which the environment sets uncontrollable timepoints and conditions. The executor observes and reacts to such uncontrollable assignments as time advances with the CSTNU execution. However, there exist scenarios in which the occurrence of some future timepoints must be fixed as soon as the execution starts. We call these timepoints parameters. For a correct execution, parameters must assume values that guarantee the possibility of satisfying all temporal constraints, whatever the environment decides the execution time for uncontrollable timepoints and the truth value of conditions, i.e., dynamic controllability (DC). Here, we formalize the extension of the CSTNU with parameters. Furthermore, we define a set of rules to check the DC of such extended CSTNU. These rules additionally solve the problem inverse to checking DC: computing restrictions on parameter values that yield DC guarantees. The proposed rules can be composed into a sound and complete procedure.
{"title":"Dynamic Controllability of Parameterized CSTNUs","authors":"M. Franceschetti, Roberto Posenato, Carlo Combi, Johann Eder","doi":"10.1145/3555776.3577618","DOIUrl":"https://doi.org/10.1145/3555776.3577618","url":null,"abstract":"A Conditional Simple Temporal Network with Uncertainty (CSTNU) models temporal constraint satisfaction problems in which the environment sets uncontrollable timepoints and conditions. The executor observes and reacts to such uncontrollable assignments as time advances with the CSTNU execution. However, there exist scenarios in which the occurrence of some future timepoints must be fixed as soon as the execution starts. We call these timepoints parameters. For a correct execution, parameters must assume values that guarantee the possibility of satisfying all temporal constraints, whatever the environment decides the execution time for uncontrollable timepoints and the truth value of conditions, i.e., dynamic controllability (DC). Here, we formalize the extension of the CSTNU with parameters. Furthermore, we define a set of rules to check the DC of such extended CSTNU. These rules additionally solve the problem inverse to checking DC: computing restrictions on parameter values that yield DC guarantees. The proposed rules can be composed into a sound and complete procedure.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76157391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The adoption of semantic technologies for the representation of crime events can help law enforcement agencies (LEAs) in crime prevention and investigation. Moreover, online newspapers and social networks are valuable sources for crime intelligence gathering. In this paper, we propose a new lightweight ontology to model crime events as they are usually described in online news articles. The Crime Event Model (CEM) can integrate specific data about crimes, i.e., where and when they occurred, who is involved (author, victim, and other subjects involved), which is the reason for the occurrence, and details about the source of information (e.g., the news article). Extracting structured data from multiple online sources and interconnecting them in a Knowledge Graph using CEM allow events relationships extraction, patterns and trends identification, and event recommendation. The CEM ontology is available at https://w3id.org/CEMontology.
{"title":"CEM: an Ontology for Crime Events in Newspaper Articles","authors":"Federica Rollo, Laura Po, Alessandro Castellucci","doi":"10.1145/3555776.3577862","DOIUrl":"https://doi.org/10.1145/3555776.3577862","url":null,"abstract":"The adoption of semantic technologies for the representation of crime events can help law enforcement agencies (LEAs) in crime prevention and investigation. Moreover, online newspapers and social networks are valuable sources for crime intelligence gathering. In this paper, we propose a new lightweight ontology to model crime events as they are usually described in online news articles. The Crime Event Model (CEM) can integrate specific data about crimes, i.e., where and when they occurred, who is involved (author, victim, and other subjects involved), which is the reason for the occurrence, and details about the source of information (e.g., the news article). Extracting structured data from multiple online sources and interconnecting them in a Knowledge Graph using CEM allow events relationships extraction, patterns and trends identification, and event recommendation. The CEM ontology is available at https://w3id.org/CEMontology.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78904472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Embedded Systems (ES) are present in several domains like automotive, smart homes, smart cities, industry, and healthcare, to name but a few. ES brings new challenges to designing embedded software that requires a high level of abstraction and being aware of resource consumption, mainly on resource-constrained devices. Modern programming languages like JavaScript (JS) can help solve these issues. However, JS is an interpreted language that demands attention to develop applications considering the balance between performance and resource consumption. In this scenario, this paper introduces an architecture design that proposes to model software for embedded systems as event-driven applications. Our design combines traditional architectures traits of Time-triggered (TT) and Event-triggered (ET) into a framework named JSEVAsync, promoting a hybrid system that explores JavaScript's non-blocking concept as a development interface to structure the algorithms into asynchronous units. As a result, we aid the development of applications with high abstraction levels and better resource consumption. To validate it, we compare C- and JavaScript-based applications, analyze the source code (static code analysis) to extract software quality metrics, and explore the results from the energy consumption perspective. We found that writing code through JSEVAsync can be up to 21% more energy efficient than the traditional method and can improve design-time metrics.
{"title":"Student Research Abstract: A Hybrid Approach to Design Embedded Software Using JavaScript's Non-blocking Principle","authors":"Fernando L. Oliveira","doi":"10.1145/3555776.3577210","DOIUrl":"https://doi.org/10.1145/3555776.3577210","url":null,"abstract":"Embedded Systems (ES) are present in several domains like automotive, smart homes, smart cities, industry, and healthcare, to name but a few. ES brings new challenges to designing embedded software that requires a high level of abstraction and being aware of resource consumption, mainly on resource-constrained devices. Modern programming languages like JavaScript (JS) can help solve these issues. However, JS is an interpreted language that demands attention to develop applications considering the balance between performance and resource consumption. In this scenario, this paper introduces an architecture design that proposes to model software for embedded systems as event-driven applications. Our design combines traditional architectures traits of Time-triggered (TT) and Event-triggered (ET) into a framework named JSEVAsync, promoting a hybrid system that explores JavaScript's non-blocking concept as a development interface to structure the algorithms into asynchronous units. As a result, we aid the development of applications with high abstraction levels and better resource consumption. To validate it, we compare C- and JavaScript-based applications, analyze the source code (static code analysis) to extract software quality metrics, and explore the results from the energy consumption perspective. We found that writing code through JSEVAsync can be up to 21% more energy efficient than the traditional method and can improve design-time metrics.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79933869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unsupervised side-channel attacks allow extracting secret keys manipulated by cryptographic primitives through leakages of their physical implementations. As opposed to supervised attacks, they do not require a preliminary profiling of the target, constituting a broader threat since they imply weaker assumptions on the adversary model. Their downside is their requirement for some a priori knowledge on the leakage model of the device. On one hand, stochastic attacks such as the Linear Regression Analysis (LRA) allow for a flexible a priori, but are mostly limited to a univariate treatment of the traces. On the other hand, model-based attacks require an explicit formulation of the leakage model but have recently been extended to multidimensional versions allowing to benefit from the potential of Deep Learning (DL) techniques. The EVIL Machine Attack (EMA), introduced in this paper, aims at taking the best of both worlds. Inspired by generative adversarial networks, its architecture is able to recover a representation of the leakage model, which is then turned into a key distinguisher allowing flexible a priori. In addition, state-of-the-art DL techniques require 256 network trainings to conduct the attack. EMA requires only one, scaling down the time complexity of such attacks by a considerable factor. Simulations and real experiments show that EMA is applicable in cases where the adversary has very low knowledge on the leakage model, while significantly reducing the required number of traces compared to a classical LRA. Eventually, a generalization of EMA, able to deal with masked implementation is introduced.
{"title":"The EVIL Machine: Encode, Visualize and Interpret the Leakage","authors":"Valence Cristiani, Maxime Lecomte, P. Maurine","doi":"10.1145/3555776.3577688","DOIUrl":"https://doi.org/10.1145/3555776.3577688","url":null,"abstract":"Unsupervised side-channel attacks allow extracting secret keys manipulated by cryptographic primitives through leakages of their physical implementations. As opposed to supervised attacks, they do not require a preliminary profiling of the target, constituting a broader threat since they imply weaker assumptions on the adversary model. Their downside is their requirement for some a priori knowledge on the leakage model of the device. On one hand, stochastic attacks such as the Linear Regression Analysis (LRA) allow for a flexible a priori, but are mostly limited to a univariate treatment of the traces. On the other hand, model-based attacks require an explicit formulation of the leakage model but have recently been extended to multidimensional versions allowing to benefit from the potential of Deep Learning (DL) techniques. The EVIL Machine Attack (EMA), introduced in this paper, aims at taking the best of both worlds. Inspired by generative adversarial networks, its architecture is able to recover a representation of the leakage model, which is then turned into a key distinguisher allowing flexible a priori. In addition, state-of-the-art DL techniques require 256 network trainings to conduct the attack. EMA requires only one, scaling down the time complexity of such attacks by a considerable factor. Simulations and real experiments show that EMA is applicable in cases where the adversary has very low knowledge on the leakage model, while significantly reducing the required number of traces compared to a classical LRA. Eventually, a generalization of EMA, able to deal with masked implementation is introduced.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77580079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xinwei Ji, Tianming Zhao, Wei Li, Albert Y. Zomaya
Automatic pain assessment systems can help patients get timely and effective pain relief treatment whenever needed. Such a system aims to provide the service with pain identification and pain intensity rating functions. Among the physiological signals, the electrodermal activity (EDA) signal emerges as a promising feature to support both functions in pain assessment. In this work, we propose a machine learning framework to implement pain identification and pain intensity rating using only EDA and its derived features. Our solution also explores the feasibility of using ultra-short EDA segmentation of about 5 seconds to meet real-time requirements. We evaluate our system on two datasets: Biovid, a publicly available dataset, and Apon, the one we build. Experimental results demonstrate that using just the ultra-short EDA signal as input, our algorithm outperforms state-of-the-art baselines and achieves a low regression error of 0.90.
{"title":"Automatic Pain Assessment with Ultra-short Electrodermal Activity Signal","authors":"Xinwei Ji, Tianming Zhao, Wei Li, Albert Y. Zomaya","doi":"10.1145/3555776.3577721","DOIUrl":"https://doi.org/10.1145/3555776.3577721","url":null,"abstract":"Automatic pain assessment systems can help patients get timely and effective pain relief treatment whenever needed. Such a system aims to provide the service with pain identification and pain intensity rating functions. Among the physiological signals, the electrodermal activity (EDA) signal emerges as a promising feature to support both functions in pain assessment. In this work, we propose a machine learning framework to implement pain identification and pain intensity rating using only EDA and its derived features. Our solution also explores the feasibility of using ultra-short EDA segmentation of about 5 seconds to meet real-time requirements. We evaluate our system on two datasets: Biovid, a publicly available dataset, and Apon, the one we build. Experimental results demonstrate that using just the ultra-short EDA signal as input, our algorithm outperforms state-of-the-art baselines and achieves a low regression error of 0.90.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91301899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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