Ahmed-Chawki Chaouche, J. Ilié, Assem Hebik, François Pêcheux
. We propose a software layered architecture for autonomous vehicles whose efficiency is driven by pull-based acquisition of sensor data. This multi-process software architecture, to be embedded into the control loop of these vehicles,
{"title":"Integration a Contextual Observation System in a Multi-Process Architecture for Autonomous Vehicles","authors":"Ahmed-Chawki Chaouche, J. Ilié, Assem Hebik, François Pêcheux","doi":"10.31577/cai_2023_3_716","DOIUrl":"https://doi.org/10.31577/cai_2023_3_716","url":null,"abstract":". We propose a software layered architecture for autonomous vehicles whose efficiency is driven by pull-based acquisition of sensor data. This multi-process software architecture, to be embedded into the control loop of these vehicles,","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"716-740"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70010020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. Multidimensional hierarchical (mTree) data are very common in daily life and scientific research. However, mTree data exploration is a laborious and time-consuming process due to its structural complexity and large dimension combination space. To address this problem, we present mTreeIllustrator, a mixed-initiative framework for exploratory analysis of multidimensional hierarchical data with faceted visualizations. First, we propose a recommendation pipeline for the automatic selection and visual representation of important subspaces of mTree data. Furthermore, we design a visual framework and an interaction schema to couple automatic recommendations with human specifications to facilitate progressive exploratory analysis. Comparative experiments and user studies demonstrate the usability and effectiveness of our framework.
{"title":"mTreeIllustrator: A Mixed-Initiative Framework for Visual Exploratory Analysis of Multidimensional Hierarchical Data","authors":"Guijuan Wang, Yu Zhao, Boyou Tan, Zhong Wang, Jiansong Wang, Hao Guo, Yadong Wu","doi":"10.31577/cai_2023_3_690","DOIUrl":"https://doi.org/10.31577/cai_2023_3_690","url":null,"abstract":". Multidimensional hierarchical (mTree) data are very common in daily life and scientific research. However, mTree data exploration is a laborious and time-consuming process due to its structural complexity and large dimension combination space. To address this problem, we present mTreeIllustrator, a mixed-initiative framework for exploratory analysis of multidimensional hierarchical data with faceted visualizations. First, we propose a recommendation pipeline for the automatic selection and visual representation of important subspaces of mTree data. Furthermore, we design a visual framework and an interaction schema to couple automatic recommendations with human specifications to facilitate progressive exploratory analysis. Comparative experiments and user studies demonstrate the usability and effectiveness of our framework.","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"690-715"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70009959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mantas Lukauskas, Tomas Rasymas, Matas Minelga, Domas Vaitmonas
. Natural language processing (NLP) involves the computer analysis and processing of human languages using a variety of techniques aimed at adapting various tasks or computer programs to linguistically process natural language. Currently, NLP is increasingly applied to a wide range of real-world problems. These tasks can vary from extracting meaningful information from unstructured data, analyzing sentiment, translating text between languages, to generating human-level text autonomously. The goal of this study is to employ transformer-based natural language models to generate high-quality business names. Specifically, this work investigates whether larger models, which require more training time, yield better results for generating relatively short texts, such as business names. To achieve
{"title":"Large Scale Fine-Tuned Transformers Models Application for Business Names Generation","authors":"Mantas Lukauskas, Tomas Rasymas, Matas Minelga, Domas Vaitmonas","doi":"10.31577/cai_2023_3_525","DOIUrl":"https://doi.org/10.31577/cai_2023_3_525","url":null,"abstract":". Natural language processing (NLP) involves the computer analysis and processing of human languages using a variety of techniques aimed at adapting various tasks or computer programs to linguistically process natural language. Currently, NLP is increasingly applied to a wide range of real-world problems. These tasks can vary from extracting meaningful information from unstructured data, analyzing sentiment, translating text between languages, to generating human-level text autonomously. The goal of this study is to employ transformer-based natural language models to generate high-quality business names. Specifically, this work investigates whether larger models, which require more training time, yield better results for generating relatively short texts, such as business names. To achieve","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"525-545"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70009995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting the patient’s brain and limbs, these interfaces contribute to the restoration of motor functions, ultimately leading to a significant improvement in the patient’s overall quality of life. However, the current BCI primarily relies on Electroencephalogram (EEG) motor imagery (MI), which has relatively coarse recognition granularity and struggles to accurately recognize specific hand movements. To address this limitation, this paper proposes a hybrid BCI framework based on Electroencephalogram and Electromyography (EEG-∗ Corresponding author
{"title":"EEG-EMG Analysis Method in Hybrid Brain Computer Interface for Hand Rehabilitation Training","authors":"Lubo Fu, Hao Li, Hongfei Ji, Jie Li","doi":"10.31577/cai_2023_3_741","DOIUrl":"https://doi.org/10.31577/cai_2023_3_741","url":null,"abstract":". Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting the patient’s brain and limbs, these interfaces contribute to the restoration of motor functions, ultimately leading to a significant improvement in the patient’s overall quality of life. However, the current BCI primarily relies on Electroencephalogram (EEG) motor imagery (MI), which has relatively coarse recognition granularity and struggles to accurately recognize specific hand movements. To address this limitation, this paper proposes a hybrid BCI framework based on Electroencephalogram and Electromyography (EEG-∗ Corresponding author","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"741-761"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70010077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Forgác, M. Očkay, Martin Javurek, Bianca Badidová
. This paper introduces a model for the authentication of large-scale images. The crucial element of the proposed model is the optimized Pulse Coupled Neural Network. This neural network generates position matrices based on which the embedding of authentication data into cover images is applied. Emphasis is placed on the minimalization of the stego image entropy change. Stego image entropy is consequently compared with the reference entropy of the cover image. The security of the suggested solution is granted by the neural network weights initialized with a steganographic key and by the encryption of accompanying steganographic data using the AES-256 algorithm. The integrity of the images is verified through the SHA-256 hash function. The integration of the accompanying and authentication data directly into the stego image and the authentication of the large images are the main contributions of the work.
{"title":"Steganography Approach to Image Authentication Using Pulse Coupled Neural Network","authors":"R. Forgác, M. Očkay, Martin Javurek, Bianca Badidová","doi":"10.31577/cai_2023_3_591","DOIUrl":"https://doi.org/10.31577/cai_2023_3_591","url":null,"abstract":". This paper introduces a model for the authentication of large-scale images. The crucial element of the proposed model is the optimized Pulse Coupled Neural Network. This neural network generates position matrices based on which the embedding of authentication data into cover images is applied. Emphasis is placed on the minimalization of the stego image entropy change. Stego image entropy is consequently compared with the reference entropy of the cover image. The security of the suggested solution is granted by the neural network weights initialized with a steganographic key and by the encryption of accompanying steganographic data using the AES-256 algorithm. The integrity of the images is verified through the SHA-256 hash function. The integration of the accompanying and authentication data directly into the stego image and the authentication of the large images are the main contributions of the work.","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"591-614"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70010206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Classification of Sentiment Using Optimized Hybrid Deep Learning Model","authors":"Chaima Ahle Touate, R. Ayachi, M. Biniz","doi":"10.31577/cai_2023_3_651","DOIUrl":"https://doi.org/10.31577/cai_2023_3_651","url":null,"abstract":"","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"651-666"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70009764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nabila Sid, M. Djezzar, Mohammed El Habib Souidi, M. Hemam
. Pursuit-Evasion Game (PEG) can be defined as a set of agents known as pursuers, which cooperate with the aim forming dynamic coalitions to capture dynamic evader agents, while the evaders try to avoid this capture by moving in the environment according to specific velocities. The factor of capturing time was treated by various studies before, but remain the powerful tools used to satisfy this factor object of research. To improve the capturing time factor we proposed in this work a novel online decentralized coalition formation algorithm equipped with Convolutional Neural Network (CNN) and based on the Iterated Elimination of Dominated Strategies (IEDS). The coalition is formed such that the pursuer should learn at each iteration the approximator formation achieving the capture in the shortest time. The pursuer’s learning process depends on the features extracted by CNN at each iteration. The proposed supervised technique is compared through simulation, with the IEDS algorithm, AGR algorithm. Simulation results show that the proposed learning technique outperform the IEDS algorithm and the AGR algorithm with respect to the learning time which represents an important factor in a chasing game.
{"title":"New Game-Theoretic Convolutional Neural Network Applied for the Multi-Pursuer Multi-Evader Game","authors":"Nabila Sid, M. Djezzar, Mohammed El Habib Souidi, M. Hemam","doi":"10.31577/cai_2023_3_546","DOIUrl":"https://doi.org/10.31577/cai_2023_3_546","url":null,"abstract":". Pursuit-Evasion Game (PEG) can be defined as a set of agents known as pursuers, which cooperate with the aim forming dynamic coalitions to capture dynamic evader agents, while the evaders try to avoid this capture by moving in the environment according to specific velocities. The factor of capturing time was treated by various studies before, but remain the powerful tools used to satisfy this factor object of research. To improve the capturing time factor we proposed in this work a novel online decentralized coalition formation algorithm equipped with Convolutional Neural Network (CNN) and based on the Iterated Elimination of Dominated Strategies (IEDS). The coalition is formed such that the pursuer should learn at each iteration the approximator formation achieving the capture in the shortest time. The pursuer’s learning process depends on the features extracted by CNN at each iteration. The proposed supervised technique is compared through simulation, with the IEDS algorithm, AGR algorithm. Simulation results show that the proposed learning technique outperform the IEDS algorithm and the AGR algorithm with respect to the learning time which represents an important factor in a chasing game.","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"546-567"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70010097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. The domains of a protein provide an insight on the functions that the protein can perform. Delineation of proteins using high-throughput experimental methods is difficult and a time-consuming task. Template-free and sequence-based computational methods that mainly rely on machine learning techniques can be used. However, some of the drawbacks of computational methods are low accuracy and their limitation in predicting different types of multi-domain proteins. Biological language modeling and deep learning techniques can be useful in such situations. In this study, we propose BERTDom for segmenting protein sequences. BERTDOM uses BERT for feature representation and stacked bi-directional long short term memory for classification. We pre-train BERT from scratch on a corpus of protein sequences obtained from UniProt knowledge base with reference clusters. For comparison, we also used two other deep learning architectures: LSTM and feed-forward neural networks. We also experimented with protein-to-vector (Pro2Vec) feature representation that uses word2vec to encode protein bio-words. For testing, three other bench-marked datasets were used. The experimental re-sults on benchmarks datasets show that BERTDom produces the best F-score as compared to other template-based and template-free protein domain boundary prediction methods. Employing deep learning architectures can significantly improve domain boundary prediction. Furthermore, BERT used extensively in NLP for feature representation, has shown promising results when used for encoding bio-words. The code is available at https://github.com/maryam988/BERTDom-Code .
{"title":"BERTDom: Protein Domain Boundary Prediction Using BERT","authors":"Ahmad Haseeb, Maryam Bashir, Aamir Wali","doi":"10.31577/cai_2023_3_667","DOIUrl":"https://doi.org/10.31577/cai_2023_3_667","url":null,"abstract":". The domains of a protein provide an insight on the functions that the protein can perform. Delineation of proteins using high-throughput experimental methods is difficult and a time-consuming task. Template-free and sequence-based computational methods that mainly rely on machine learning techniques can be used. However, some of the drawbacks of computational methods are low accuracy and their limitation in predicting different types of multi-domain proteins. Biological language modeling and deep learning techniques can be useful in such situations. In this study, we propose BERTDom for segmenting protein sequences. BERTDOM uses BERT for feature representation and stacked bi-directional long short term memory for classification. We pre-train BERT from scratch on a corpus of protein sequences obtained from UniProt knowledge base with reference clusters. For comparison, we also used two other deep learning architectures: LSTM and feed-forward neural networks. We also experimented with protein-to-vector (Pro2Vec) feature representation that uses word2vec to encode protein bio-words. For testing, three other bench-marked datasets were used. The experimental re-sults on benchmarks datasets show that BERTDom produces the best F-score as compared to other template-based and template-free protein domain boundary prediction methods. Employing deep learning architectures can significantly improve domain boundary prediction. Furthermore, BERT used extensively in NLP for feature representation, has shown promising results when used for encoding bio-words. The code is available at https://github.com/maryam988/BERTDom-Code .","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"667-689"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70009806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. Attribute-based access control (ABAC) has higher flexibility and better scalability than traditional access control and can be used for fine-grained access control of large-scale information systems. Although ABAC can depict a dynamic, complex access control policy, it is costly, tedious, and error-prone to manually define. Therefore, it is worth studying how to construct an ABAC policy efficiently and accurately. This paper proposes an ABAC policy generation approach based on the CatBoost algorithm to automatically learn policies from historical access logs. First, we perform a weighted reconstruction of the attributes for the policy to be mined. Second, we provide an ABAC rule extraction algorithm, rule pruning algorithm, and rule optimization algorithm, among which the rule pruning and rule optimization algorithms are used to improve the accuracy of the generated policies. In addition, we present a new policy quality indicator to measure the accuracy and simplicity of the generated policies. Finally, the results of an experiment conducted to validate the approach verify its feasibility and effectiveness.
{"title":"Attribute-Based Access Control Policy Generation Approach from Access Logs Based on the CatBoost","authors":"Shan Quan, Yongdan Zhao, N. Helil","doi":"10.31577/cai_2023_3_615","DOIUrl":"https://doi.org/10.31577/cai_2023_3_615","url":null,"abstract":". Attribute-based access control (ABAC) has higher flexibility and better scalability than traditional access control and can be used for fine-grained access control of large-scale information systems. Although ABAC can depict a dynamic, complex access control policy, it is costly, tedious, and error-prone to manually define. Therefore, it is worth studying how to construct an ABAC policy efficiently and accurately. This paper proposes an ABAC policy generation approach based on the CatBoost algorithm to automatically learn policies from historical access logs. First, we perform a weighted reconstruction of the attributes for the policy to be mined. Second, we provide an ABAC rule extraction algorithm, rule pruning algorithm, and rule optimization algorithm, among which the rule pruning and rule optimization algorithms are used to improve the accuracy of the generated policies. In addition, we present a new policy quality indicator to measure the accuracy and simplicity of the generated policies. Finally, the results of an experiment conducted to validate the approach verify its feasibility and effectiveness.","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"615-650"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70009751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Model for Spatiotemporal Crime Prediction with Improved Deep Learning","authors":"Ature Angbera, H. Chan","doi":"10.31577/cai_2023_3_568","DOIUrl":"https://doi.org/10.31577/cai_2023_3_568","url":null,"abstract":"","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"8 1","pages":"568-590"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70010113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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