Inteligencia Artif.最新文献

{"title":"An Operational Semantics for Situated Ideological Systems, with a Case Study Concerning a Paradigmatic Religious Conflict","authors":"Antônio Costa","doi":"10.4114/intartif.vol25iss69pp159-182","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss69pp159-182","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000This paper introduces an operational semantics for what, in previous work, we have called situated \u0000ideological systems: sets of ways to envisage concrete social situations (normatively, valuationally etc.) that individuals and social groups may use to direct their choices and actions in such situations. The agent-based social model that we call Agent Society is used to computationally construe possible social situations where individual agents and groups of agents may find themselves, and to formally represent ideological systems in ways they can collectively share. As a case study of the proposed semantics, the paper formally presents the ideological dynamics underlying the temporal evolution of a paradigmatic situation of religious conflict (between catholics and devotees of African-Brazilian cults), as pictured in the classical Brazilian theatrical play The Keeper of Promises (O Pagador de Promessas). The ideology modeling language TinyIML is used for the computational presentation of the various stages of that ideological dynamics. The syntax and semantics of TinyIML is summarized in an appendix. \u0000 \u0000 \u0000 \u0000","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131040238","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":"Detection of Loanwords in Angolan Portuguese: A Text Mining Approach","authors":"Timóteo Muhongo, P. Brazdil, Fátima Silva","doi":"10.4114/intartif.vol25iss69pp87-106","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss69pp87-106","url":null,"abstract":"Angola is characterized by many different languages and social, cultural and political realities, which had a marked effect on Angolan Portuguese (AP). Consequently, AP is characterized by diatopic variation. One of the marked effects is in the loanwords imported from other Angolan languages. Our objective is to analyze different Angolan texts, analyze the lexical forms used and conduct a comparative study with European Portuguese, whose aim is to identify the possible loanwords in Angolan. This process was automated, as well as the identification of cotexts of all loanwords. In addition, we determine the lexical class of each loanword and the Angolan language of origin. Most lexical loanwords come from the Kimbundu, although AP includes loanwords from some other Angolan languages, too. Our study serves as a basis for preparation of an Angolan regionalism dictionary. We note that more than 700 loanwords identified do not figure in the existing dictionaries.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128666220","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":"Fault Tolerance for Composite Cloud Services: A Novel Approach Based MAS","authors":"O. Hioual, Ouided Hioual, S. Hemam, Rania Mordjane, N. Bouhlala","doi":"10.4114/intartif.vol25iss69pp183-200","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss69pp183-200","url":null,"abstract":"Several Cloud services may be composed in order to respond quickly to the needs of users. Unfortunately, when running such a service some faults may occur. The outcome of fault control is a big challenge. In this paper, the authors propose a new approach based back recovery and multi-agent planning methods. The proposed architecture based MAS (Multi-Agent System) is composed of two main types of Agents : a Composition Manager Agent (CMA) and a Supervisor Agent (SA). The role of the CMA is to create a set of plans as an oriented graph where the nodes are the Cloud services and the valued arcs represent the composition order of these services. This agent saves checkpoints (nodes) in a stable memory so that there are at least one possible path. However, the SA ensures that the running plan is working properly; otherwise, it informs the CMA to select another sub-plan. Experimental results show the performance and effectiveness of the proposed approach.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"316 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131053461","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":"Feature extractions and selection of bot detection on Twitter A systematic literature review: Feature extractions and selection of bot detection on Twitter A systematic literature review","authors":"Raad Al-azawi, S. Al-Mamory","doi":"10.4114/intartif.vol25iss69pp57-86","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss69pp57-86","url":null,"abstract":"Abstract Automated or semiautomated computer programs that imitate humans and/or human behavior in online social networks are known as social bots. Users can be attacked by social bots to achieve several hidden aims, such as spreading information or influencing targets. While researchers develop a variety of methods to detect social media bot accounts, attackers adapt their bots to avoid detection. This field necessitates ongoing growth, particularly in the areas of feature selection and extraction. The study's purpose is to provide an overview of bot attacks on Twitter, shedding light on issues in feature extraction and selection that have a significant impact on the accuracy of bot detection algorithms, and highlighting the weaknesses in training time and dimensionality reduction. To the best of our knowledge, this study is the first systematic literature review based on a preset search-strategy that encompasses literature published between 2018 and 2021 which are concerned with Twitter features (attributes). The key findings of this research are threefold. First, the paper provides an improved taxonomy of feature extraction and selection approaches. Second, it includes a comprehensive overview of approaches for detecting bots in the Twitter platform, particularly machine learning techniques. The percentage was calculated using the proposed taxonomy, with metadata, tweet text, and merging (meta and tweet text) accounting for 37%, 31%, and 32%, respectively. Third, some gaps are also highlighted for further research. The first is that public datasets are not precise or suitable in size. Second, the use of integrated systems and real-time detection is uncommon. Third, detecting each bots category identified separately is needed, rather than detecting all categories of bots using one generic model and the same features' values. Finally, extracting influential features that assist machine learning algorithms in detecting Twitter bots with high accuracy is critical, especially if the type of bot is pre-determined.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124106597","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":"Rough-Fuzzy Support Vector Clustering with OWA Operators","authors":"Ramiro Saltos Atiencia, R. Weber","doi":"10.4114/intartif.vol25iss69pp42-56","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss69pp42-56","url":null,"abstract":"Rough-Fuzzy Support Vector Clustering (RFSVC) is a novel soft computing derivative of the classical Support Vector Clustering (SVC) algorithm, which has been used already in many real-world applications. RFSVC’s strengths are its ability to handle arbitrary cluster shapes, identify the number of clusters, and e?ectively detect outliers by the means of membership degrees. However, its current version uses only the closest support vector of each cluster to calculate outliers’ membership degrees, neglecting important information that remaining support vectors can contribute. We present a novel approach based on the ordered weighted average (OWA) operator that aggregates information from all cluster representatives when computing ?nal membership degrees and at the same time allows a better interpretation of the cluster structures found. Particularly, we propose the induced OWA using weights determined by the employed kernel function. The computational experiments show that our approach outperforms the current version of RFSVC as well as alternative techniques ?xing the weights of the OWA operator while maintaining the level of interpretability of membership degrees for detecting outliers.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129180287","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":"Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series","authors":"M. Sol'is, L. Calvo-Valverde","doi":"10.48550/arXiv.2203.11196","DOIUrl":"https://doi.org/10.48550/arXiv.2203.11196","url":null,"abstract":"Deep Learning and transfer learning models are being used to generate time series forecasts; however, there is scarce evidence about their performance prediction that it is more evident for monthly time series. The purpose of this paper is to compare Deep Learning models with transfer learning and without transfer learning and other traditional methods used for monthly forecasts to answer three questions about the suitability of Deep Learning and Transfer Learning to generate predictions of time series. Time series of M4 and M3 competitions were used for the experiments. The results suggest that deep learning models based on TCN, LSTM, and CNN with transfer learning tend to surpass the performance prediction of other traditional methods. On the other hand, TCN and LSTM, trained directly on the target time series, got similar or better performance than traditional methods for some forecast horizons.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131345064","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":"A Gene Expression Clustering Method to Extraction of Cell-to-Cell Biological Communication","authors":"Hui Wang, Yan Sha, Dan Wang, Hamed Nazari","doi":"10.4114/intartif.vol25iss69pp1-12","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss69pp1-12","url":null,"abstract":"Graph-based clustering identification is a practical method to detect the communication between nodes in complex networks that has obtained considerable comments. Since identifying different communities in large-scale data is a challenging task, by understanding the communication between the behaviors of the elements in a community (a cluster), the general characteristics of clusters can be predicted. Graph-based clustering methods have played an important role in clustering gene expression data because of their ability to show the relations between the data. In order to be able to identify genes that lead to the development of diseases, the communication between the cells must be established. The communication between different cells can be indicated by the expression of different genes within them. In this study, the problem of cell-to-cell communication is expressed as a graph and the communication are extracted by recognizing the communities. The FANTOM5 dataset is used to simulate and calculate the similarity between cells. After preprocessing and normalizing the data, to convert this data into graphs, the expression of genes in different cells was examined and by considering a threshold and Wilcoxon test, the communication between them were identified through using clustering.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124618266","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":"Artificial Intelligence techniques based on the integration of symbolic logic and deep neural networks: A systematic review of the literature","authors":"P. Negro, Claudia Pons","doi":"10.4114/intartif.vol25iss69pp13-41","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss69pp13-41","url":null,"abstract":"The need for neural-symbolic integration becomes apparent as more complex problems are tackled, and they go beyond limited domain tasks such as classification. In this sense, understanding the state of the art of hybrid technologies based on Deep Learning and augmented with logic based systems, is of utmost importance. As a consequence, we seek to understand and represent the current state of these technologies that are highly used in intelligent systems engineering.This work aims to provide a comprehensive view of the solutions available in the literature, within the field of applied Artificial Intelligence (AI), using technologies based on AI techniques that integrate symbolic and non-symbolic logic (in particular artificial neural networks), making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from both perspectives: symbolic and non-symbolic AI.In this work, we use the PICOC & Limits method to define the research questions and analyze the results.Out of a total of 65 candidate studies found, 24 articles (37%) relevant to this study were selected. Each study also focuses on different application domains. Conclusion: Through the analysis of the selected works throughout this review, we have seen different combinations of logical systems with some form of neural network and, although we have not found a clear architectural pattern, efforts to find a model of general purpose combining both worlds drive trends and research efforts.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124400330","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":"Symmetry-Based Brain Abnormality Detection Using Machine Learning","authors":"Mohammad A. N. Al-Azawi","doi":"10.4114/intartif.vol24iss68pp138-150","DOIUrl":"https://doi.org/10.4114/intartif.vol24iss68pp138-150","url":null,"abstract":"Medical image processing, which includes many applications such as magnetic resonance image (MRI) processing, is one of the most significant fields of computer-aided diagnostic (CAD) systems. the detection and identification of abnormalities in the magnetic resonance imaging of the brain is one of the important applications that uses magnetic resonance imaging and digital image processing techniques. In this study, we present a method that relies on the symmetry and similarity between the two lobes of the brain to determine if there are any abnormalities in the brain because tumours cause deformations in the shape of one of the lobes, which affects this symmetry. The proposed approach overcomes the challenge arising from different shapes of brain images of different people, which poses an obstacle to some approaches that rely on comparing one person’s brain image with other people's brain images. In the proposed method the image of the brain is divided into two parts, one for the left lobe and the other for the right lobe. Some measures are extracted from the features of the image of each lobe separately and the distance between the corresponding metrics are calculated. These distances are used as the independent variables of the classification algorithm which determines the class to which the brain belongs. Metrics extracted from various features, such as colour and texture, were studied, discussed and used in the classification process. The proposed algorithm was applied to 366 images from standard datasets and four classifiers were tested namely Naïve Bayes (NB), random forest (RF), logistic regression (LR), and support vector machine (SVM). The obtained results from these classifiers have been discussed thoroughly and it was found that the best results were obtained from RF classifiers where the accuracy was 98.2%. Finally, The results obtained and the limitations were discussed and benchmarked with state-of-the-art approaches.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133794713","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":"Greedy Genetic Algorithm for the Data Aggregator Positioning Problem in Smart Grids","authors":"Sami Nasser Lauar, Mário Mestria","doi":"10.4114/intartif.vol24iss68pp123-137","DOIUrl":"https://doi.org/10.4114/intartif.vol24iss68pp123-137","url":null,"abstract":"In this work, we present a metaheuristic based on the genetic and greedy algorithms to solve an application of the set covering problem (SCP), the data aggregator positioning in smart grids. The GGH (Greedy Genetic Hybrid) is structured as a genetic algorithm, but it has many modifications compared to the classic version. At the mutation step, only columns included in the solution can suffer mutation and be removed. At the recombination step, only columns from the parent’s solutions are available to generate the offspring. Moreover, the greedy algorithm generates the initial population, reconstructs solutions after mutation, and generates new solutions from the recombination step. Computational results using OR-Library problems showed that the GGH reached optimal solutions for 40 instances in a total of 75 and, in the other instances, obtained good and promising values, presenting a medium gap of 1,761%.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127928250","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}