Pub Date : 2021-12-05DOI: 10.1109/SSCI50451.2021.9660152
A. J, E. Cambria, T. Trueman
Social media influences internet users to share their sentiments, feelings, or emotions about entities. In particular, sentiment analysis classifies a text into positive, negative, or neutral. It does not capture the state of mind of an individual like happiness, anger, and fear. Therefore, emotion detection plays an important role in user-generated content for capturing the state of mind. Moreover, researchers adopted traditional machine learning and deep learning models to capture emotions from the text. Recently, transformers-based architectures achieve better results in various natural language processing tasks. Therefore, we propose a transformer-based emotion detection system, which uses context-dependent features and a one-cycle learning rate policy for a better understanding of emotions from the text. We evaluate the proposed emotion detection model using error matrix, learning curve, precision, recall, F1-score, and their micro and macro averages. Our results indicate that the system achieves a 6 % accuracy over existing models.
{"title":"Transformer-Based Bidirectional Encoder Representations for Emotion Detection from Text","authors":"A. J, E. Cambria, T. Trueman","doi":"10.1109/SSCI50451.2021.9660152","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660152","url":null,"abstract":"Social media influences internet users to share their sentiments, feelings, or emotions about entities. In particular, sentiment analysis classifies a text into positive, negative, or neutral. It does not capture the state of mind of an individual like happiness, anger, and fear. Therefore, emotion detection plays an important role in user-generated content for capturing the state of mind. Moreover, researchers adopted traditional machine learning and deep learning models to capture emotions from the text. Recently, transformers-based architectures achieve better results in various natural language processing tasks. Therefore, we propose a transformer-based emotion detection system, which uses context-dependent features and a one-cycle learning rate policy for a better understanding of emotions from the text. We evaluate the proposed emotion detection model using error matrix, learning curve, precision, recall, F1-score, and their micro and macro averages. Our results indicate that the system achieves a 6 % accuracy over existing models.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127930673","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}
Pub Date : 2021-12-05DOI: 10.1109/SSCI50451.2021.9660056
Danilo Cavaliere, S. Senatore
Precision Agriculture (PA) and Forest Management (FM) applications require sensor-based environment monitoring to assess the vegetation status of monitored areas. Vegetation Indices (VIs), assessed from satellite-taken spectral images, depict some features (e.g., vegetation vigour, coverage, etc.) but they are not enough to describe vegetation status, hence they need to be contextualized according to the area phenology, latitude and weather for correct vegetation status interpretations. Moreover, heterogeneous data collection can cause data integration and interoperability issues. Additionally, human operators, who have to monitor multiple vast environments in time critical contexts, require brief meaningful reports about occurred situations. In this paper a knowledge-based multi-agent approach is presented to deal with environment monitoring of user-specified Regions of Interest (ROIs) and assess their vegetation status. The approach employs different types of agents to carry out various tasks, including data acquisition and knowledge storing, end-user interaction and vegetation analysis accomplishment. The end-user can request different types of analysis and pass data to the system through an agent-managed GUI, hence vegetation analysis is carried out by using a decision tree-based method to properly query the KB built on VIs and contextual data to consequently build a report about the vegetation status of the ROI. The built report includes a description of other features (soil, weather) that helps depicting the detected vegetation status. Several case studies demonstrate the functioning and efficacy of the approach.
{"title":"A multi-agent knowledge-enhanced model for decision-supporting agroforestry systems","authors":"Danilo Cavaliere, S. Senatore","doi":"10.1109/SSCI50451.2021.9660056","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660056","url":null,"abstract":"Precision Agriculture (PA) and Forest Management (FM) applications require sensor-based environment monitoring to assess the vegetation status of monitored areas. Vegetation Indices (VIs), assessed from satellite-taken spectral images, depict some features (e.g., vegetation vigour, coverage, etc.) but they are not enough to describe vegetation status, hence they need to be contextualized according to the area phenology, latitude and weather for correct vegetation status interpretations. Moreover, heterogeneous data collection can cause data integration and interoperability issues. Additionally, human operators, who have to monitor multiple vast environments in time critical contexts, require brief meaningful reports about occurred situations. In this paper a knowledge-based multi-agent approach is presented to deal with environment monitoring of user-specified Regions of Interest (ROIs) and assess their vegetation status. The approach employs different types of agents to carry out various tasks, including data acquisition and knowledge storing, end-user interaction and vegetation analysis accomplishment. The end-user can request different types of analysis and pass data to the system through an agent-managed GUI, hence vegetation analysis is carried out by using a decision tree-based method to properly query the KB built on VIs and contextual data to consequently build a report about the vegetation status of the ROI. The built report includes a description of other features (soil, weather) that helps depicting the detected vegetation status. Several case studies demonstrate the functioning and efficacy of the approach.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133895599","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}
Pub Date : 2021-12-05DOI: 10.1109/SSCI50451.2021.9660001
Stephen Friess, P. Tiňo, S. Menzel, B. Sendhoff, Xin Yao
Domain-dependent expertise knowledge and high-level abstractions to arbitrate between different problem domains can be considered to be essential components of how human problem-solvers build experience and reuse it over the course of their lifetime. However, replicating it from an algorithmic point of view is a less trivial endeavor. Existing knowledge transfer methods in optimization largely fail to provide more specific guidance on specifying the similarity of different optimization problems and the nature of complementary experiences formed on them. A more rigorously grounded approach can be found alternatively in metalearning. This notion neglects any hurdles on characterizing problem similarity in favor of focusing instead on methodology to form domain-dependent inductive biases and mechanisms to arbitrate between them. In principle, we proposed within our previous research methods for constructing inductive biases and predict these from procedural optimization data. However, while we obtained effective methodology, it does not allow the joint construction of predictive components and biases in a cohesive manner. We therefore show in our following study, that improved configurations can be derived for the CMA-ES algorithm which can serve as inductive biases, and that predictors can be trained to recall them. Particularly noteworthy, this scenario allows the construction of predictive component and bias iteratively in a joint manner. We demonstrate the efficacy of this approach in a shape optimization scenario, in which the inductive bias is predicted through an operator configuration in a problem-specific manner during run-time.
{"title":"Predicting CMA-ES Operators as Inductive Biases for Shape Optimization Problems","authors":"Stephen Friess, P. Tiňo, S. Menzel, B. Sendhoff, Xin Yao","doi":"10.1109/SSCI50451.2021.9660001","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660001","url":null,"abstract":"Domain-dependent expertise knowledge and high-level abstractions to arbitrate between different problem domains can be considered to be essential components of how human problem-solvers build experience and reuse it over the course of their lifetime. However, replicating it from an algorithmic point of view is a less trivial endeavor. Existing knowledge transfer methods in optimization largely fail to provide more specific guidance on specifying the similarity of different optimization problems and the nature of complementary experiences formed on them. A more rigorously grounded approach can be found alternatively in metalearning. This notion neglects any hurdles on characterizing problem similarity in favor of focusing instead on methodology to form domain-dependent inductive biases and mechanisms to arbitrate between them. In principle, we proposed within our previous research methods for constructing inductive biases and predict these from procedural optimization data. However, while we obtained effective methodology, it does not allow the joint construction of predictive components and biases in a cohesive manner. We therefore show in our following study, that improved configurations can be derived for the CMA-ES algorithm which can serve as inductive biases, and that predictors can be trained to recall them. Particularly noteworthy, this scenario allows the construction of predictive component and bias iteratively in a joint manner. We demonstrate the efficacy of this approach in a shape optimization scenario, in which the inductive bias is predicted through an operator configuration in a problem-specific manner during run-time.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131841804","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}
Pub Date : 2021-12-05DOI: 10.1109/SSCI50451.2021.9659920
Kanwal Jahan, Jeethesh Pai Umesh, Michael Roth
This paper introduces a novel application of anomaly detection on the rail lines using deep learning methods on camera data. We propose a two-fold approach for identifying irregularities like coal, dirt, and obstacles on the rail tracks. In the first stage, a binary semantic segmentation is performed to extract only the rails from the background. In the second stage, we deploy our proposed autoencoder utilizing the self-supervised learning techniques to address the unavailability of labelled anomalies. The extracted rails from stage one are divided into multiple patches and are fed to the autoencoder, which is trained to reconstruct the non-anomalous data only. Hence, during the inference, the regeneration of images with any abnormalities produces a larger reconstruction error. Applying a predefined threshold to the reconstruction errors can detect an anomaly on a rail track. Stage one, rail extracting network achieves a high value of 52.78% mean Intersection over Union (mIoU). The second stage autoencoder network converges well on the training data. Finally, we evaluate our two-fold approach on real scenario test images, no false positives or false negatives were found in the the detected anomalies on the rail tracks.
{"title":"Anomaly Detection on the Rail Lines Using Semantic Segmentation and Self-supervised Learning","authors":"Kanwal Jahan, Jeethesh Pai Umesh, Michael Roth","doi":"10.1109/SSCI50451.2021.9659920","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659920","url":null,"abstract":"This paper introduces a novel application of anomaly detection on the rail lines using deep learning methods on camera data. We propose a two-fold approach for identifying irregularities like coal, dirt, and obstacles on the rail tracks. In the first stage, a binary semantic segmentation is performed to extract only the rails from the background. In the second stage, we deploy our proposed autoencoder utilizing the self-supervised learning techniques to address the unavailability of labelled anomalies. The extracted rails from stage one are divided into multiple patches and are fed to the autoencoder, which is trained to reconstruct the non-anomalous data only. Hence, during the inference, the regeneration of images with any abnormalities produces a larger reconstruction error. Applying a predefined threshold to the reconstruction errors can detect an anomaly on a rail track. Stage one, rail extracting network achieves a high value of 52.78% mean Intersection over Union (mIoU). The second stage autoencoder network converges well on the training data. Finally, we evaluate our two-fold approach on real scenario test images, no false positives or false negatives were found in the the detected anomalies on the rail tracks.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131889272","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}
Pub Date : 2021-12-05DOI: 10.1109/SSCI50451.2021.9659992
A. Thavaneswaran, R. Thulasiram, Md. Erfanul Hoque, S. S. Appadoo
Uncertainty in supply chain leads to what is known as bullwhip effect (BE), which causes multiple inefficiencies such as higher costs of production (of more than what is needed), wastage and logistics. Though there are many studies reported in the literature, the impact of the quality of dynamic forecasts on the BE has not received sufficient coverage. In this paper, a fuzzy data-driven weighted moving average (DDWMA) forecasts of the future demand strategy is proposed for supply chain. Also, data-driven random weighted volatility forecasting model is used to study the fuzzy extended Bollinger bands forecasts of the demand. The main reason of using the fuzzy approach is to provide α-cuts for DDWMA demand forecasts as well as extended Bollinger bands forecasts. The proposed fuzzy extended Bollinger bands forecast is a two steps procedure as it uses optimal weights for both the demand forecasts as well as the volatility forecasts of the demand process. In particular, a novel dynamic fuzzy forecasting algorithm of the demand is proposed which bypasses complexities associated with traditional forecasting steps of fitting any time series model. The proposed data-driven fuzzy forecasting approach focuses on defining a dynamic fuzzy forecasting intervals of the demand as well as the volatility of the demand in supply chain. The performance of proposed approaches is evaluated through numerical experiments using simulated data and weekly demand data. The results show that the proposed methods perform well in terms of narrower fuzzy forecasting bands for demand as well as the volatility of the demand.
{"title":"Data-Driven Fuzzy Demand Forecasting Models for Resilient Supply Chains","authors":"A. Thavaneswaran, R. Thulasiram, Md. Erfanul Hoque, S. S. Appadoo","doi":"10.1109/SSCI50451.2021.9659992","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659992","url":null,"abstract":"Uncertainty in supply chain leads to what is known as bullwhip effect (BE), which causes multiple inefficiencies such as higher costs of production (of more than what is needed), wastage and logistics. Though there are many studies reported in the literature, the impact of the quality of dynamic forecasts on the BE has not received sufficient coverage. In this paper, a fuzzy data-driven weighted moving average (DDWMA) forecasts of the future demand strategy is proposed for supply chain. Also, data-driven random weighted volatility forecasting model is used to study the fuzzy extended Bollinger bands forecasts of the demand. The main reason of using the fuzzy approach is to provide α-cuts for DDWMA demand forecasts as well as extended Bollinger bands forecasts. The proposed fuzzy extended Bollinger bands forecast is a two steps procedure as it uses optimal weights for both the demand forecasts as well as the volatility forecasts of the demand process. In particular, a novel dynamic fuzzy forecasting algorithm of the demand is proposed which bypasses complexities associated with traditional forecasting steps of fitting any time series model. The proposed data-driven fuzzy forecasting approach focuses on defining a dynamic fuzzy forecasting intervals of the demand as well as the volatility of the demand in supply chain. The performance of proposed approaches is evaluated through numerical experiments using simulated data and weekly demand data. The results show that the proposed methods perform well in terms of narrower fuzzy forecasting bands for demand as well as the volatility of the demand.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132197570","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}
Pub Date : 2021-12-05DOI: 10.1109/SSCI50451.2021.9660133
Iron Tessaro, R. Z. Freire, V. Mariani, L. Coelho
One of the applications of data-driven methods in the industry is the creation of real-time, embedded measurements, whether to monitor or replace sensor signals. As the number of embedded systems in products raises over time, the energy efficiency of such systems must be considered in the design. The time (processor) efficiency of the embedded software is directly related to the energy efficiency of the embedded system. Therefore, when considering some embedded software solutions, such as data-driven methods, time efficiency must be taken into account to improve energy efficiency. In this work, the energy efficiency of three data-driven methods: the Sparse Identification of Nonlinear Dynamics (SINDy), the Extreme Learning Machine (ELM), and the Random-Vector Functional Link (RVFL) network were assessed by using the creation of a real-time in-cylinder pressure sensor for diesel engines as a task. The three methods were kept with equivalent performances, whereas their relative execution time was tested and classified by their statistical rankings. Additionally, the space (memory) efficiency of the methods was assessed. The contribution of this work is to provide a guide to choose the best data-driven method to be used in an embedded system in terms of efficiency.
{"title":"Space and Time Efficiency Analysis of Data-Driven Methods Applied to Embedded Systems","authors":"Iron Tessaro, R. Z. Freire, V. Mariani, L. Coelho","doi":"10.1109/SSCI50451.2021.9660133","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660133","url":null,"abstract":"One of the applications of data-driven methods in the industry is the creation of real-time, embedded measurements, whether to monitor or replace sensor signals. As the number of embedded systems in products raises over time, the energy efficiency of such systems must be considered in the design. The time (processor) efficiency of the embedded software is directly related to the energy efficiency of the embedded system. Therefore, when considering some embedded software solutions, such as data-driven methods, time efficiency must be taken into account to improve energy efficiency. In this work, the energy efficiency of three data-driven methods: the Sparse Identification of Nonlinear Dynamics (SINDy), the Extreme Learning Machine (ELM), and the Random-Vector Functional Link (RVFL) network were assessed by using the creation of a real-time in-cylinder pressure sensor for diesel engines as a task. The three methods were kept with equivalent performances, whereas their relative execution time was tested and classified by their statistical rankings. Additionally, the space (memory) efficiency of the methods was assessed. The contribution of this work is to provide a guide to choose the best data-driven method to be used in an embedded system in terms of efficiency.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133801415","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}
Pub Date : 2021-12-05DOI: 10.1109/SSCI50451.2021.9659853
U. Johansson, Henrik Boström, Tuwe Löfström
Conformal prediction can be applied on top of any machine learning predictive regression model, thus turning it into a conformal regressor. Given a significance level $epsilon$, conformal regressors output valid prediction intervals, i.e., the probability that the interval covers the true value is exactly $1-epsilon$. To obtain validity, a calibration set that is not used for training the model must be set aside. In standard inductive conformal regression, the size of the prediction intervals is then determined by the absolute error made by the predictive model on a specific instance in the calibration set, where different significance levels correspond to different instances. In this setting, all prediction intervals will have the same size, making the resulting models very unspecific. When adding a technique called normalization, however, the difficulty of each instance is estimated, and the interval sizes are adjusted accordingly. An integral part of normalized conformal regressors is a parameter called $beta$, which determines the relative importance of the difficulty estimation and the error of the model. In this study, the effects of different underlying models, difficulty estimation functions and $beta$ -values are investigated. The results from a large empirical study, using twenty publicly available data sets, show that better difficulty estimation functions will lead to both tighter and more specific prediction intervals. Furthermore, it is found that the $beta$ -values used strongly affect the conformal regressor. While there is no specific $beta$ -value that will always minimize the interval sizes, lower $beta$ -values lead to more variation in the interval sizes, i.e., more specific models. In addition, the analysis also identifies that the normalization procedure introduces a small but unfortunate bias in the models. More specifically, normalization using low $beta$ -values means that smaller intervals are more likely to be erroneous, while the opposite is true for higher $beta$ -values.
{"title":"Investigating Normalized Conformal Regressors","authors":"U. Johansson, Henrik Boström, Tuwe Löfström","doi":"10.1109/SSCI50451.2021.9659853","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659853","url":null,"abstract":"Conformal prediction can be applied on top of any machine learning predictive regression model, thus turning it into a conformal regressor. Given a significance level $epsilon$, conformal regressors output valid prediction intervals, i.e., the probability that the interval covers the true value is exactly $1-epsilon$. To obtain validity, a calibration set that is not used for training the model must be set aside. In standard inductive conformal regression, the size of the prediction intervals is then determined by the absolute error made by the predictive model on a specific instance in the calibration set, where different significance levels correspond to different instances. In this setting, all prediction intervals will have the same size, making the resulting models very unspecific. When adding a technique called normalization, however, the difficulty of each instance is estimated, and the interval sizes are adjusted accordingly. An integral part of normalized conformal regressors is a parameter called $beta$, which determines the relative importance of the difficulty estimation and the error of the model. In this study, the effects of different underlying models, difficulty estimation functions and $beta$ -values are investigated. The results from a large empirical study, using twenty publicly available data sets, show that better difficulty estimation functions will lead to both tighter and more specific prediction intervals. Furthermore, it is found that the $beta$ -values used strongly affect the conformal regressor. While there is no specific $beta$ -value that will always minimize the interval sizes, lower $beta$ -values lead to more variation in the interval sizes, i.e., more specific models. In addition, the analysis also identifies that the normalization procedure introduces a small but unfortunate bias in the models. More specifically, normalization using low $beta$ -values means that smaller intervals are more likely to be erroneous, while the opposite is true for higher $beta$ -values.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132136875","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}
Pub Date : 2021-12-05DOI: 10.1109/SSCI50451.2021.9659990
Ander Gray, S. Ferson, O. Kosheleva, V. Kreinovich
In general, many general mathematical formulations of uncertainty quantification problems are NP-hard, meaning that (unless it turned out that P = NP) no feasible algorithm is possible that would always solve these problems. In this paper, we argue that if we restrict ourselves to practical problems, then the correspondingly restricted problems become feasible - namely, they can be solved by using linear programming techniques.
{"title":"While, In General, Uncertainty Quantification (UQ) Is NP-Hard, Many Practical UQ Problems Can Be Made Feasible","authors":"Ander Gray, S. Ferson, O. Kosheleva, V. Kreinovich","doi":"10.1109/SSCI50451.2021.9659990","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659990","url":null,"abstract":"In general, many general mathematical formulations of uncertainty quantification problems are NP-hard, meaning that (unless it turned out that P = NP) no feasible algorithm is possible that would always solve these problems. In this paper, we argue that if we restrict ourselves to practical problems, then the correspondingly restricted problems become feasible - namely, they can be solved by using linear programming techniques.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"360 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132173783","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}
Pub Date : 2021-12-05DOI: 10.1109/SSCI50451.2021.9659850
M. Alghamdi, P. Angelov, Bryan M. Williams
Handimages are of paramount importance within critical domains like security and criminal investigation. They can sometimes be the only available evidence of an offender's identity at a crime scene. Approaches to person identification that consider the human hand as a complex object composed of many components are rare. The approach proposed in this paper fills this gap, making use of knuckle creases and fingernail information. It introduces a framework for automatic person identification that includes localisation of the regions of interest within hand images, recognition of the detected components, segmentation of the region of interest using bounding boxes, and similarity matching between a query image and a library of available images. The following hand components are considered: i) the metacarpohalangeal, commonly known as base knuckle; ii) the proximal interphalangeal joint commonly known as major knuckle; iii) distal interphalangeal joint, commonly known as minor knuckle; iv) the interphalangeal joint, commonly known as thumb's knuckle, and v) the fingernails. A key element of the proposed framework is the similarity matching and an important role for it is played by the feature extraction. In this paper, we exploit end-to-end deep convolutional neural networks to extract discriminative high-level abstract features. We further use Bray-Curtis (BC) similarity for the matching process. We validated the proposed approach on well-known benchmarks, the ‘11k Hands' dataset and the Hong Kong Polytechnic University Contactless Hand Dorsal Images known as ‘PolyU HD’. We found that the results indicate that the knuckle patterns and fingernails play a significant role in the person identification. The results from the 11K dataset indicate that the results for the left hand are better than the results for the right hand. In both datasets, the fingernails produced consistently higher identification results than other hand components, with a rank-1 score of 93.65% on the ring finger of the left hand for the ‘11k Hands' dataset and rank-l score of 93.81% for the thumb from the ‘PolyU HD’ dataset.
{"title":"Automated Person Identification Framework Based on Fingernails and Dorsal Knuckle Patterns","authors":"M. Alghamdi, P. Angelov, Bryan M. Williams","doi":"10.1109/SSCI50451.2021.9659850","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659850","url":null,"abstract":"Handimages are of paramount importance within critical domains like security and criminal investigation. They can sometimes be the only available evidence of an offender's identity at a crime scene. Approaches to person identification that consider the human hand as a complex object composed of many components are rare. The approach proposed in this paper fills this gap, making use of knuckle creases and fingernail information. It introduces a framework for automatic person identification that includes localisation of the regions of interest within hand images, recognition of the detected components, segmentation of the region of interest using bounding boxes, and similarity matching between a query image and a library of available images. The following hand components are considered: i) the metacarpohalangeal, commonly known as base knuckle; ii) the proximal interphalangeal joint commonly known as major knuckle; iii) distal interphalangeal joint, commonly known as minor knuckle; iv) the interphalangeal joint, commonly known as thumb's knuckle, and v) the fingernails. A key element of the proposed framework is the similarity matching and an important role for it is played by the feature extraction. In this paper, we exploit end-to-end deep convolutional neural networks to extract discriminative high-level abstract features. We further use Bray-Curtis (BC) similarity for the matching process. We validated the proposed approach on well-known benchmarks, the ‘11k Hands' dataset and the Hong Kong Polytechnic University Contactless Hand Dorsal Images known as ‘PolyU HD’. We found that the results indicate that the knuckle patterns and fingernails play a significant role in the person identification. The results from the 11K dataset indicate that the results for the left hand are better than the results for the right hand. In both datasets, the fingernails produced consistently higher identification results than other hand components, with a rank-1 score of 93.65% on the ring finger of the left hand for the ‘11k Hands' dataset and rank-l score of 93.81% for the thumb from the ‘PolyU HD’ dataset.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129200345","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}
Pub Date : 2021-12-05DOI: 10.1109/SSCI50451.2021.9659911
Carlos Quintero Gull, J. Aguilar, M. Rodríguez-Moreno
In this work, we use the semi-supervised LAMDA-HSCC algorithm for characterizing the energy consumption in smart buildings, which can work with labeled and unlabeled data. Particularly, it uses the LAMDA-RD approach for the clustering problem and the LAMDA-HAD approach for the classification problem. Additionally, this algorithm uses three submodels for merging, partition groups (classes/cluster) and migrating individuals from a group to another. For the performance evaluation, several datasets of energetic consumption are used, with different percent of labeled data, showing very encouraging results according to two metrics in the semi-supervised context.
{"title":"A semi-supervised learning approach to study the energy consumption in smart buildings","authors":"Carlos Quintero Gull, J. Aguilar, M. Rodríguez-Moreno","doi":"10.1109/SSCI50451.2021.9659911","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659911","url":null,"abstract":"In this work, we use the semi-supervised LAMDA-HSCC algorithm for characterizing the energy consumption in smart buildings, which can work with labeled and unlabeled data. Particularly, it uses the LAMDA-RD approach for the clustering problem and the LAMDA-HAD approach for the classification problem. Additionally, this algorithm uses three submodels for merging, partition groups (classes/cluster) and migrating individuals from a group to another. For the performance evaluation, several datasets of energetic consumption are used, with different percent of labeled data, showing very encouraging results according to two metrics in the semi-supervised context.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132298127","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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