This paper presents a new web application implementing and automizing a tool called Search Trajectory Networks. This web application is potentially very useful for researchers from the field of stochastic optimization algorithms such as metaheuristics because it allows the visual comparison of such algorithms. Moreover, it helps in gaining an improved understanding of optimization algorithm behaviour. Our web application facilitates the use of the Search Trajectory Networks tool because the previously manual process is automized and important improvements are added. These improvements include a simplified input format, a generalization to more than three algorithms and an integrated search space partitioning scheme.
{"title":"Search Trajectory Networks Meet the Web: A Web Application for the Visual Comparison of Optimization Algorithms","authors":"Camilo Chacon-Sartori, C. Blum, G. Ochoa","doi":"10.1145/3587828.3587843","DOIUrl":"https://doi.org/10.1145/3587828.3587843","url":null,"abstract":"This paper presents a new web application implementing and automizing a tool called Search Trajectory Networks. This web application is potentially very useful for researchers from the field of stochastic optimization algorithms such as metaheuristics because it allows the visual comparison of such algorithms. Moreover, it helps in gaining an improved understanding of optimization algorithm behaviour. Our web application facilitates the use of the Search Trajectory Networks tool because the previously manual process is automized and important improvements are added. These improvements include a simplified input format, a generalization to more than three algorithms and an integrated search space partitioning scheme.","PeriodicalId":340917,"journal":{"name":"Proceedings of the 2023 12th International Conference on Software and Computer Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126429149","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}
Nur Shuhadah Ab Razak, Hafizah Mansor, Shaila Sharmin
Automotive technology is soaring and has reached an advanced phase. Despite their benefits, these advancements may expose vehicles to additional threats, particularly in terms of security and data management. This paper focuses on the application of vehicle maintenance services, specifically on maintenance history records. Currently, handling maintenance services history is a manual process which may give rise to inaccuracy and unavailability. Moreover, consumers rarely have access to these records unless they keep hardcopy-based records for the maintenance services performed on their cars. Those issues bring another concern that there are no reliable platforms or legitimate sources to retrieve the history of maintenance service records. Thus, the objectives of this paper are to identify the relevant stakeholders and determine their roles in the vehicle maintenance services industry. Hence, the list and details of stakeholder is considered and applied accordingly in the design of a secure framework that provides the necessary security goals for the maintenance services records. The proposed framework implements blockchain technology based on two use cases which are the Framework for Scheduled Maintenance Service and the Framework of Repair Maintenance Service. The use of this framework by the stakeholders in the automotive ecosystem would ensure the implementation of the required security goals in the maintenance services application. The method that is being used in this research is literature reviews which is to find out the best approach to be applied in the framework as well as listing out the stakeholder involved and their roles. Next, a secure framework has been designed to produce secure vehicle maintenance records that covers the security requirements needed. As a result, the contributions of this work are the list of stakeholders and their roles, security requirements that need to be taken into consideration and a secure framework for vehicle maintenance records involving all stakeholders.
{"title":"A Secure Framework for Vehicle Maintenance Service using Blockchain","authors":"Nur Shuhadah Ab Razak, Hafizah Mansor, Shaila Sharmin","doi":"10.1145/3587828.3587868","DOIUrl":"https://doi.org/10.1145/3587828.3587868","url":null,"abstract":"Automotive technology is soaring and has reached an advanced phase. Despite their benefits, these advancements may expose vehicles to additional threats, particularly in terms of security and data management. This paper focuses on the application of vehicle maintenance services, specifically on maintenance history records. Currently, handling maintenance services history is a manual process which may give rise to inaccuracy and unavailability. Moreover, consumers rarely have access to these records unless they keep hardcopy-based records for the maintenance services performed on their cars. Those issues bring another concern that there are no reliable platforms or legitimate sources to retrieve the history of maintenance service records. Thus, the objectives of this paper are to identify the relevant stakeholders and determine their roles in the vehicle maintenance services industry. Hence, the list and details of stakeholder is considered and applied accordingly in the design of a secure framework that provides the necessary security goals for the maintenance services records. The proposed framework implements blockchain technology based on two use cases which are the Framework for Scheduled Maintenance Service and the Framework of Repair Maintenance Service. The use of this framework by the stakeholders in the automotive ecosystem would ensure the implementation of the required security goals in the maintenance services application. The method that is being used in this research is literature reviews which is to find out the best approach to be applied in the framework as well as listing out the stakeholder involved and their roles. Next, a secure framework has been designed to produce secure vehicle maintenance records that covers the security requirements needed. As a result, the contributions of this work are the list of stakeholders and their roles, security requirements that need to be taken into consideration and a secure framework for vehicle maintenance records involving all stakeholders.","PeriodicalId":340917,"journal":{"name":"Proceedings of the 2023 12th International Conference on Software and Computer Applications","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126669206","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}
A knowledge graph (KG) is used to store knowledge in the form of connected facts. Facts in KG are represented in the form of a triple (subject, predicate, object) or (head, relation, tail). KG is widely used in question answering, information retrieval, classification, recommender systems, and so on. However, a common problem with KG is incomplete KG. A KG is called incomplete if there is a missing relationship between two entities. An incomplete KG can have an impact on decreasing the accuracy of a task that uses the KG. One solution to the incomplete KG is to use link prediction. Link prediction aims to predict the missing relationship between two entities in a KG. Another problem is that the size of KG is large, consisting of hundreds or millions of entities and relationships. Handling large KG also needs to be considered. Therefore, link prediction on large KG also needs to be considered so that the link prediction process is more efficient. This paper discusses link prediction using embedding to overcome the incomplete KG problem. In addition, it is proposed to use clustering to increase the efficiency of the link prediction process. Clustering is used to group the embedding results. After the embedding results are grouped, scoring and loss function calculations to predict missing links are carried out in groups that are considered appropriate. It is expected that with this grouping, the time of link prediction process can be more efficient because there is no need to check all the vectors in the embedding space.
{"title":"Improving the Efficiency of Link Prediction on Handling Incomplete Knowledge Graph Using Clustering","authors":"Fitri Susanti, N. Maulidevi, K. Surendro","doi":"10.1145/3587828.3587830","DOIUrl":"https://doi.org/10.1145/3587828.3587830","url":null,"abstract":"A knowledge graph (KG) is used to store knowledge in the form of connected facts. Facts in KG are represented in the form of a triple (subject, predicate, object) or (head, relation, tail). KG is widely used in question answering, information retrieval, classification, recommender systems, and so on. However, a common problem with KG is incomplete KG. A KG is called incomplete if there is a missing relationship between two entities. An incomplete KG can have an impact on decreasing the accuracy of a task that uses the KG. One solution to the incomplete KG is to use link prediction. Link prediction aims to predict the missing relationship between two entities in a KG. Another problem is that the size of KG is large, consisting of hundreds or millions of entities and relationships. Handling large KG also needs to be considered. Therefore, link prediction on large KG also needs to be considered so that the link prediction process is more efficient. This paper discusses link prediction using embedding to overcome the incomplete KG problem. In addition, it is proposed to use clustering to increase the efficiency of the link prediction process. Clustering is used to group the embedding results. After the embedding results are grouped, scoring and loss function calculations to predict missing links are carried out in groups that are considered appropriate. It is expected that with this grouping, the time of link prediction process can be more efficient because there is no need to check all the vectors in the embedding space.","PeriodicalId":340917,"journal":{"name":"Proceedings of the 2023 12th International Conference on Software and Computer Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121629005","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}
Vhydie G. Christianto, Erna Hikmawati, K. Surendro
Global warming and other environmental problems are becoming a worrying aspect. In the current digitalization era, various organizations are focusing on increasing the quantity of Information Technology (IT) assets to support business processes, including organizations engaged in the education sector. However, the impact arising from IT equipment has not been well thought out and managed. The same thing also happened to the Bandung Institute of Technology, which had no concerns regarding IT equipment emissions. In reality, the management of IT equipment has not been carried out properly due to the absence of an equipment management system based on environmental aspects, such as greenhouse gas emissions and end-of-life period. Therefore, this research focuses on developing a method with a design thinking approach to develop an IT equipment management system. Design thinking is used as a method of development approach so that it can be adapted to the needs of users and organizations. This method is expected to provide insight to ITB stakeholders so that they are able to design an environment-based IT equipment management system.
{"title":"Development of IT Equipment Management Methodology based on Carbon Emission and End-of-Life Period with A Design Thinking Approach: Case Study: Bandung Institute of Technology","authors":"Vhydie G. Christianto, Erna Hikmawati, K. Surendro","doi":"10.1145/3587828.3587882","DOIUrl":"https://doi.org/10.1145/3587828.3587882","url":null,"abstract":"Global warming and other environmental problems are becoming a worrying aspect. In the current digitalization era, various organizations are focusing on increasing the quantity of Information Technology (IT) assets to support business processes, including organizations engaged in the education sector. However, the impact arising from IT equipment has not been well thought out and managed. The same thing also happened to the Bandung Institute of Technology, which had no concerns regarding IT equipment emissions. In reality, the management of IT equipment has not been carried out properly due to the absence of an equipment management system based on environmental aspects, such as greenhouse gas emissions and end-of-life period. Therefore, this research focuses on developing a method with a design thinking approach to develop an IT equipment management system. Design thinking is used as a method of development approach so that it can be adapted to the needs of users and organizations. This method is expected to provide insight to ITB stakeholders so that they are able to design an environment-based IT equipment management system.","PeriodicalId":340917,"journal":{"name":"Proceedings of the 2023 12th International Conference on Software and Computer Applications","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115056214","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}
Mohammed Rakib, Md. Ismail Hossain, Nabeel Mohammed, F. Rahman
Although over 300M around the world speak Bangla, scant work has been done in improving Bangla voice-to-text transcription due to Bangla being a low-resource language. However, with the introduction of the Bengali Common Voice 9.0 speech dataset, Automatic Speech Recognition (ASR) models can now be significantly improved. With 399hrs of speech recordings, Bengali Common Voice is the largest and most diversified open-source Bengali speech corpus in the world. In this paper, we outperform the State-of-the-Art (SOTA) pretrained Bengali ASR models by finetuning a pretrained wav2vec2 model on the common voice dataset. We also demonstrate how to significantly improve the performance of an ASR model by adding an n-gram language model as a post-processor. Finally, we do some experiments and hyperparameter tuning to generate a robust Bangla ASR model that is better than the existing ASR models.
{"title":"Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models","authors":"Mohammed Rakib, Md. Ismail Hossain, Nabeel Mohammed, F. Rahman","doi":"10.1145/3587828.3587872","DOIUrl":"https://doi.org/10.1145/3587828.3587872","url":null,"abstract":"Although over 300M around the world speak Bangla, scant work has been done in improving Bangla voice-to-text transcription due to Bangla being a low-resource language. However, with the introduction of the Bengali Common Voice 9.0 speech dataset, Automatic Speech Recognition (ASR) models can now be significantly improved. With 399hrs of speech recordings, Bengali Common Voice is the largest and most diversified open-source Bengali speech corpus in the world. In this paper, we outperform the State-of-the-Art (SOTA) pretrained Bengali ASR models by finetuning a pretrained wav2vec2 model on the common voice dataset. We also demonstrate how to significantly improve the performance of an ASR model by adding an n-gram language model as a post-processor. Finally, we do some experiments and hyperparameter tuning to generate a robust Bangla ASR model that is better than the existing ASR models.","PeriodicalId":340917,"journal":{"name":"Proceedings of the 2023 12th International Conference on Software and Computer Applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122274216","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}
{"title":"Proceedings of the 2023 12th International Conference on Software and Computer Applications","authors":"","doi":"10.1145/3587828","DOIUrl":"https://doi.org/10.1145/3587828","url":null,"abstract":"","PeriodicalId":340917,"journal":{"name":"Proceedings of the 2023 12th International Conference on Software and Computer Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122278899","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：