Pub Date : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00022
Nektarios Moumoutzis, Marios Christoulakis, C. Xanthaki, Yiannis Maragkoudakis, S. Christodoulakis, D. Paneva-Marinova, Lilia Pavlova
eShadow is a digital storytelling platform inspired by traditional Shadow Theatre. It enables the creation of digital stories within a project-based approach that may start from scenario development and include the creation of digital puppets and sceneries, the set-up and recording of story scenes and the final assembly of a digital story. This paper presents how eShadow can be enhanced to solve the problem of creating mixed reality installations to offer rich learning experiences in informal learning settings. This enhanced version is eShadow+ and it is evaluated via two installations which are described and compared. The evaluation results demonstrate the effectiveness of the approach thus offering new learning opportunities that are aligned with current trends in the use of mixed reality technologies.
{"title":"eShadow+: Mixed Reality Storytelling Inspired by Traditional Shadow Theatre","authors":"Nektarios Moumoutzis, Marios Christoulakis, C. Xanthaki, Yiannis Maragkoudakis, S. Christodoulakis, D. Paneva-Marinova, Lilia Pavlova","doi":"10.1109/COMPSAC54236.2022.00022","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00022","url":null,"abstract":"eShadow is a digital storytelling platform inspired by traditional Shadow Theatre. It enables the creation of digital stories within a project-based approach that may start from scenario development and include the creation of digital puppets and sceneries, the set-up and recording of story scenes and the final assembly of a digital story. This paper presents how eShadow can be enhanced to solve the problem of creating mixed reality installations to offer rich learning experiences in informal learning settings. This enhanced version is eShadow+ and it is evaluated via two installations which are described and compared. The evaluation results demonstrate the effectiveness of the approach thus offering new learning opportunities that are aligned with current trends in the use of mixed reality technologies.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131903490","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00114
Zhengxin Li, Sikai Tao, Ruixun Zhang, Hongpeng Wang
Medical segmentation of cochlear images aims to identify the area of the cochlea in a set of CT slices. The shape of cochlea will vary a quite in different CT slicing levels, and the relevant dataset has a higher labeling cost. This will lead to segmentation results with edge discontinuity when we implement supervised algorithm under few samples. In order to solve the problem of a small number of labeled images, this paper proposes a semi-supervised model called GSDNet which is based on GAN, which captures the features of the cochlear image without labels, so as to achieve high performance for processing fewer sampled data. To further improve the generalization of the model, we adopt a training method that allows the model to gradually distinguish between real images and fake images. In addition, in order to solve the problem of local noise interference and discontinuous segmentation results, we introduce a label discrimination network to force the distribution of generated results from segmentation network to align with the true label distribution, so that the edges of the segmentation results are continuous and the shape is more accurate. Finally, we conduct a segmentation experiment of the cochlear region containing 30 slices about cochlea data, and compare different cutting-edge methods. The method proposed in this paper achieves higher performance on the dice index.
{"title":"GSDNet: An Anti-interference Cochlea Segmentation Model Based on GAN","authors":"Zhengxin Li, Sikai Tao, Ruixun Zhang, Hongpeng Wang","doi":"10.1109/COMPSAC54236.2022.00114","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00114","url":null,"abstract":"Medical segmentation of cochlear images aims to identify the area of the cochlea in a set of CT slices. The shape of cochlea will vary a quite in different CT slicing levels, and the relevant dataset has a higher labeling cost. This will lead to segmentation results with edge discontinuity when we implement supervised algorithm under few samples. In order to solve the problem of a small number of labeled images, this paper proposes a semi-supervised model called GSDNet which is based on GAN, which captures the features of the cochlear image without labels, so as to achieve high performance for processing fewer sampled data. To further improve the generalization of the model, we adopt a training method that allows the model to gradually distinguish between real images and fake images. In addition, in order to solve the problem of local noise interference and discontinuous segmentation results, we introduce a label discrimination network to force the distribution of generated results from segmentation network to align with the true label distribution, so that the edges of the segmentation results are continuous and the shape is more accurate. Finally, we conduct a segmentation experiment of the cochlear region containing 30 slices about cochlea data, and compare different cutting-edge methods. The method proposed in this paper achieves higher performance on the dice index.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114418992","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00081
Md Jobair Hossain Faruk, Mohammad Masum, H. Shahriar, K. Qian, D. Lo
The primary goal of the authentic learning provides students with an engaging and motivating learning environment for students with hands-on experiences in solving real-world security problems. Each learning topic consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. With an authentic learning approach, we design and develop portable labware on Google CoLab for ML for ransomware detection and prevention so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem-solving skills.
{"title":"Authentic Learning of Machine Learning to Ransomware Detection and Prevention","authors":"Md Jobair Hossain Faruk, Mohammad Masum, H. Shahriar, K. Qian, D. Lo","doi":"10.1109/COMPSAC54236.2022.00081","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00081","url":null,"abstract":"The primary goal of the authentic learning provides students with an engaging and motivating learning environment for students with hands-on experiences in solving real-world security problems. Each learning topic consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. With an authentic learning approach, we design and develop portable labware on Google CoLab for ML for ransomware detection and prevention so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem-solving skills.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114818736","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00238
M. Menard, Tommy Nelson, Milan Shahi, Hugh Morton, Adam DeTavernier, Harvey P. Siy, Rui Zhao, Myoungkyu Song
Secure library reuse is critical for modern ap-plications to protect private information in software security engineering. Teaching secure programming is also more critical to tackle the challenges of new and evolving threats. However, novice students often make mistakes by API misuses due to a lack of understanding of secure libraries or a false sense of security. In this paper, we study the feasibility of applying code clone detection (CCD) for finding relevant examples to effectively teach secure programming to computer science students. CCD is an emerging new technology that extracts syntactically or semantically similar code fragments to support many software engineering tasks, such as program understanding, code quality analysis, software evolution analysis, and bug detection. We have developed a prototype implementation ExTUTOR that allows students to search for relevant examples as feedback when they want to fix their programming issues or vulnerabilities. In our evaluation, we applied ExTUTOR to open source subject applications in the security domain. Our approach should help novice students gain benefits from feedback and identify how to effectively make use of APIs, encouraging students to fix their own security violations in their own applications.
{"title":"A Feasibility Study of Using Code Clone Detection for Secure Programming Education","authors":"M. Menard, Tommy Nelson, Milan Shahi, Hugh Morton, Adam DeTavernier, Harvey P. Siy, Rui Zhao, Myoungkyu Song","doi":"10.1109/COMPSAC54236.2022.00238","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00238","url":null,"abstract":"Secure library reuse is critical for modern ap-plications to protect private information in software security engineering. Teaching secure programming is also more critical to tackle the challenges of new and evolving threats. However, novice students often make mistakes by API misuses due to a lack of understanding of secure libraries or a false sense of security. In this paper, we study the feasibility of applying code clone detection (CCD) for finding relevant examples to effectively teach secure programming to computer science students. CCD is an emerging new technology that extracts syntactically or semantically similar code fragments to support many software engineering tasks, such as program understanding, code quality analysis, software evolution analysis, and bug detection. We have developed a prototype implementation ExTUTOR that allows students to search for relevant examples as feedback when they want to fix their programming issues or vulnerabilities. In our evaluation, we applied ExTUTOR to open source subject applications in the security domain. Our approach should help novice students gain benefits from feedback and identify how to effectively make use of APIs, encouraging students to fix their own security violations in their own applications.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114834893","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00221
Zhipeng Cui, Qinyan Zhang, Jing-Wei Zhang, Xue Sun, Qing Wang, Yi Lei, Lin Zang, Li Zhao, Jijiang Yang
Gastroscopy is an important step in the diagnosis of early gastric cancer. However, because the morphological manifestations of early gastric cancer are not obvious, endoscopists need long-term specialized training and experience accumulation to correctly identify early cancer through magnification gastroscopy. In this paper, the data set of gastroscopy image is collected and enhanced, and target detection method is combined with gastroscopy image. The Mask R-CNN+BiFPN model was proposed to enhance the feature fusion and improve the detection effect of early gastric cancer lesions. Compared with Mask R-CNN, the improved Mask R-CNN model has better performance, with the sensitivity and specificity of 91.67% and 88.95% in accurately labeled gastroscopic datasets, respectively, showing a good segmentation effect for surface swelling lesions.
{"title":"Application of Improved Mask R-CNN Algorithm Based on Gastroscopic Image in Detection of Early Gastric Cancer","authors":"Zhipeng Cui, Qinyan Zhang, Jing-Wei Zhang, Xue Sun, Qing Wang, Yi Lei, Lin Zang, Li Zhao, Jijiang Yang","doi":"10.1109/COMPSAC54236.2022.00221","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00221","url":null,"abstract":"Gastroscopy is an important step in the diagnosis of early gastric cancer. However, because the morphological manifestations of early gastric cancer are not obvious, endoscopists need long-term specialized training and experience accumulation to correctly identify early cancer through magnification gastroscopy. In this paper, the data set of gastroscopy image is collected and enhanced, and target detection method is combined with gastroscopy image. The Mask R-CNN+BiFPN model was proposed to enhance the feature fusion and improve the detection effect of early gastric cancer lesions. Compared with Mask R-CNN, the improved Mask R-CNN model has better performance, with the sensitivity and specificity of 91.67% and 88.95% in accurately labeled gastroscopic datasets, respectively, showing a good segmentation effect for surface swelling lesions.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114847543","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00016
Carlo Vitucci, Daniel Sundmark, Marcus Jägemar, Jakob Danielsson, Alf Larsson, Thomas Nolte
Fault management is an important function that impacts the design of any digital system, from the simple kiosk in a shop to a complex 6G network. It is common to classify fault conditions into different taxonomies using terms like fault or error. Fault taxonomies are often suitable for managing fault detection, fault reporting, and fault localization but often neglect to support all different functions required by a fault management process. A correctly implemented fault management process must be able to distinguish between defects and faults, decide upon ap-propriate actions to recover the system to an ideal state, and avoid an error condition. Fault management is a multi-disciplinary process where recovery actions are deployed promptly by com-bined hardware, firmware, and software orchestration. The importance of fault management processes significantly increases with modern nanometer technologies, which suffer the risk of so-called soft errors, a corruption of a bit cells that can happen due to spurious disturbance, like cosmic radiation. Modern fault management implementations must support recovery actions for soft errors to ensure a steady system. This paper describes an extended fault classification model that emphasizes fault management and recovery actions. We aim to show how the reliability-based fault taxonomy definition is more suitable for the overall fault management process.
{"title":"A Reliability-oriented Faults Taxonomy and a Recovery-oriented Methodological Approach for Systems Resilience","authors":"Carlo Vitucci, Daniel Sundmark, Marcus Jägemar, Jakob Danielsson, Alf Larsson, Thomas Nolte","doi":"10.1109/COMPSAC54236.2022.00016","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00016","url":null,"abstract":"Fault management is an important function that impacts the design of any digital system, from the simple kiosk in a shop to a complex 6G network. It is common to classify fault conditions into different taxonomies using terms like fault or error. Fault taxonomies are often suitable for managing fault detection, fault reporting, and fault localization but often neglect to support all different functions required by a fault management process. A correctly implemented fault management process must be able to distinguish between defects and faults, decide upon ap-propriate actions to recover the system to an ideal state, and avoid an error condition. Fault management is a multi-disciplinary process where recovery actions are deployed promptly by com-bined hardware, firmware, and software orchestration. The importance of fault management processes significantly increases with modern nanometer technologies, which suffer the risk of so-called soft errors, a corruption of a bit cells that can happen due to spurious disturbance, like cosmic radiation. Modern fault management implementations must support recovery actions for soft errors to ensure a steady system. This paper describes an extended fault classification model that emphasizes fault management and recovery actions. We aim to show how the reliability-based fault taxonomy definition is more suitable for the overall fault management process.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117251144","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00132
Yan Huang, Xian Xu
Software defect prediction is an active topic in the field of software engineering. Cross-project defect prediction (CPDP) adopts the defect data set of the source project to predict the defects of the target project. However, the metrics of the source project and those of the target project are often different, and the traditional CPDP has certain limitations at this time. To address the inconsistency of source and target metrics, researchers propose heterogeneous cross-project defect prediction (HCPDP). To improve the performance of the HCPDP, we propose new Two-stage Cost-sensitive Local Models (TCLM). TCLM aims to improve on the problem of feature selection, linear inseparability of heterogeneous data, class imbalance and data adoption problems in HCPDP. Firstly, in the feature selection stage, we add cost information to improve the feature selection algorithm. Then, KCCA (Kernel Canonical Correlation Analysis) is used to project and map the heterogeneous data into a common feature space so as to mitigate the problem of inconsistent feature sets of the source and the target projects. Secondly, in the model training stage, we adopt local models to improve the performance, and introduce cost information to deal with the class imbalance problem. To verify the effectiveness of the TCLM method, we conduct large-scale empirical study on 24 projects in the AEEEM, PROMISE, NASA, and Relink datasets. Experimental results show that TCLM indeed outperforms the previous work. Therefore, we recommend using the TCLM method to build an HCPDP model.
{"title":"Two-stage cost-sensitive local models for heterogeneous cross-project defect prediction","authors":"Yan Huang, Xian Xu","doi":"10.1109/COMPSAC54236.2022.00132","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00132","url":null,"abstract":"Software defect prediction is an active topic in the field of software engineering. Cross-project defect prediction (CPDP) adopts the defect data set of the source project to predict the defects of the target project. However, the metrics of the source project and those of the target project are often different, and the traditional CPDP has certain limitations at this time. To address the inconsistency of source and target metrics, researchers propose heterogeneous cross-project defect prediction (HCPDP). To improve the performance of the HCPDP, we propose new Two-stage Cost-sensitive Local Models (TCLM). TCLM aims to improve on the problem of feature selection, linear inseparability of heterogeneous data, class imbalance and data adoption problems in HCPDP. Firstly, in the feature selection stage, we add cost information to improve the feature selection algorithm. Then, KCCA (Kernel Canonical Correlation Analysis) is used to project and map the heterogeneous data into a common feature space so as to mitigate the problem of inconsistent feature sets of the source and the target projects. Secondly, in the model training stage, we adopt local models to improve the performance, and introduce cost information to deal with the class imbalance problem. To verify the effectiveness of the TCLM method, we conduct large-scale empirical study on 24 projects in the AEEEM, PROMISE, NASA, and Relink datasets. Experimental results show that TCLM indeed outperforms the previous work. Therefore, we recommend using the TCLM method to build an HCPDP model.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117293783","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00146
B. Hnatkowska, Adrianna Kozierkiewicz-Hetmanska, Marcin Pietranik
The following paper is the next step of research on automatic processing of business rules expressed in natural language. Such rules are used to describe a selected universe of discourse - its properties and constraints. They are usually written with a text editor as a set of free-form sentences. The purpose of the paper is to propose a method for verifying the compatibility of business rules with a domain specification in the form of a UML class diagram. Such verification is performed at the syntax level. While our previous research has focused on processing only simple sentences, this paper presents a method for analyzing compound sentences. The usefulness of our ideas has been experimentally demonstrated.
{"title":"Compatibility Checking of Compound Business Rules Expressed in Natural Language Against Domain Specification","authors":"B. Hnatkowska, Adrianna Kozierkiewicz-Hetmanska, Marcin Pietranik","doi":"10.1109/COMPSAC54236.2022.00146","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00146","url":null,"abstract":"The following paper is the next step of research on automatic processing of business rules expressed in natural language. Such rules are used to describe a selected universe of discourse - its properties and constraints. They are usually written with a text editor as a set of free-form sentences. The purpose of the paper is to propose a method for verifying the compatibility of business rules with a domain specification in the form of a UML class diagram. Such verification is performed at the syntax level. While our previous research has focused on processing only simple sentences, this paper presents a method for analyzing compound sentences. The usefulness of our ideas has been experimentally demonstrated.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"387 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115852781","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00088
Jamalia Sultana, Mahmuda Naznin
Voice based interactive system has numerous ap-plications including patient care system, robotics, interactive learning tool etc. Speech Emotion Recognition (SER) is a vital part of any voice based interactive system. Providing an efficient SER framework in multi-lingual domain is highly challenging due to the difficulties in feature extraction from noisy voice signals, language barrier, issues due to gender dependency, domain generalization problem etc. Therefore, all of these challenges have made multi-domain SER interesting to the researchers. In our study, we provide a multi-domain SER framework where popular benchmark corpora have been integrated and used together for training and testing with the goal of removing language barriers and the corpus dependency. Moreover, we have utilized the role of gender on acoustic signal features to improve the performance in multi-domain. We design a hierarchical Convolutional Neural Network (CNN) based framework that finds the influence of genders while recognizing emotions in multi-domain cross-corpus system. We have used Unweighted Average Recall (UAR) for measuring performance in the multi-domain corpus to address data imbalance problem. We validate our proposed framework by conducting extensive experiments with benchmark datasets. The results show that using the proposed gender-based SER model with multi-lingual cross-corpus performs better than the conventional SER models. Our novel multi-domain cross-corpus SER will be very helpful for designing different multi-lingual voice- based interactive applications.
{"title":"Breaking the Barrier with a Multi-Domain SER","authors":"Jamalia Sultana, Mahmuda Naznin","doi":"10.1109/COMPSAC54236.2022.00088","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00088","url":null,"abstract":"Voice based interactive system has numerous ap-plications including patient care system, robotics, interactive learning tool etc. Speech Emotion Recognition (SER) is a vital part of any voice based interactive system. Providing an efficient SER framework in multi-lingual domain is highly challenging due to the difficulties in feature extraction from noisy voice signals, language barrier, issues due to gender dependency, domain generalization problem etc. Therefore, all of these challenges have made multi-domain SER interesting to the researchers. In our study, we provide a multi-domain SER framework where popular benchmark corpora have been integrated and used together for training and testing with the goal of removing language barriers and the corpus dependency. Moreover, we have utilized the role of gender on acoustic signal features to improve the performance in multi-domain. We design a hierarchical Convolutional Neural Network (CNN) based framework that finds the influence of genders while recognizing emotions in multi-domain cross-corpus system. We have used Unweighted Average Recall (UAR) for measuring performance in the multi-domain corpus to address data imbalance problem. We validate our proposed framework by conducting extensive experiments with benchmark datasets. The results show that using the proposed gender-based SER model with multi-lingual cross-corpus performs better than the conventional SER models. Our novel multi-domain cross-corpus SER will be very helpful for designing different multi-lingual voice- based interactive applications.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115480580","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00134
Nasos Grigoropoulos, S. Lalis
Next-generation drone applications will be distributed, including tasks that need to run at the edge or in the cloud and interact with the drone in a smooth way. In this paper, we propose Fractus, an orchestration framework for the automated deployment of such applications in the drone-edge-cloud continuum. Fractus provides users with abstractions for describing the application's placement and communication requirements, allocates resources in a mission-aware fashion by considering the drone operation area, establishes and maintains connectivity between components by transparently leveraging different networking capabilities, and tackles safety and privacy issues via policy-based access to mobility and sensor resources. We present the design of Fractus and discuss an implementation based on mature software deployment technology. Further, we evaluate the resource requirements of our implementation, showing that it introduces an acceptable overhead, and illustrate its functionality via real field tests and a simulation setup.
{"title":"Fractus: Orchestration of Distributed Applications in the Drone-Edge-Cloud Continuum","authors":"Nasos Grigoropoulos, S. Lalis","doi":"10.1109/COMPSAC54236.2022.00134","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00134","url":null,"abstract":"Next-generation drone applications will be distributed, including tasks that need to run at the edge or in the cloud and interact with the drone in a smooth way. In this paper, we propose Fractus, an orchestration framework for the automated deployment of such applications in the drone-edge-cloud continuum. Fractus provides users with abstractions for describing the application's placement and communication requirements, allocates resources in a mission-aware fashion by considering the drone operation area, establishes and maintains connectivity between components by transparently leveraging different networking capabilities, and tackles safety and privacy issues via policy-based access to mobility and sensor resources. We present the design of Fractus and discuss an implementation based on mature software deployment technology. Further, we evaluate the resource requirements of our implementation, showing that it introduces an acceptable overhead, and illustrate its functionality via real field tests and a simulation setup.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123731803","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：