Affective music composition systems are known to trigger emotions in humans. However, the design of such systems to stimulate users' emotions continues to be a challenge because, studies that aggregate existing literature in the domain to help advance research and knowledge is limited. This study presents a systematic literature review on affective algorithmic composition systems. Eighteen primary studies were selected from IEEE Xplore, ACM Digital Library, SpringerLink, PubMed, ScienceDirect, and Google Scholar databases following a systematic review protocol. The findings revealed that there is a lack of a unique definition that encapsulates the various types of affective algorithmic composition systems. Accordingly, a unique definition is provided. The findings also show that most affective algorithmic composition systems are designed for games to provide background music. The generative composition method was the most used compositional approach. Overall, there was rather a low amount of research in the domain. Possible reasons for these trends are the lack of a common definition for affective music composition systems and also the lack of detailed documentation of the design, implementation and evaluation of the existing systems.
{"title":"Affective algorithmic composition of music: A systematic review","authors":"Abigail Wiafe, P. Fränti","doi":"10.3934/aci.2023003","DOIUrl":"https://doi.org/10.3934/aci.2023003","url":null,"abstract":"\u0000 Affective music composition systems are known to trigger emotions in humans. However, the design of such systems to stimulate users' emotions continues to be a challenge because, studies that aggregate existing literature in the domain to help advance research and knowledge is limited. This study presents a systematic literature review on affective algorithmic composition systems. Eighteen primary studies were selected from IEEE Xplore, ACM Digital Library, SpringerLink, PubMed, ScienceDirect, and Google Scholar databases following a systematic review protocol. The findings revealed that there is a lack of a unique definition that encapsulates the various types of affective algorithmic composition systems. Accordingly, a unique definition is provided. The findings also show that most affective algorithmic composition systems are designed for games to provide background music. The generative composition method was the most used compositional approach. Overall, there was rather a low amount of research in the domain. Possible reasons for these trends are the lack of a common definition for affective music composition systems and also the lack of detailed documentation of the design, implementation and evaluation of the existing systems.\u0000","PeriodicalId":414924,"journal":{"name":"Applied Computing and Intelligence","volume":"28 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":"115719923","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}
We present a method to construct optimal clustering via a sequence of merge steps. We formulate the merge-based clustering as a minimum redundancy search tree, and then search the optimal clustering by a branch-and-bound technique. Optimal clustering is found regardless of the objective function used. We also consider two suboptimal polynomial time variants based on the proposed branch-and-bound technique. However, all variants are slow and has merely theoretical interest. We discuss the reasons for the results.
{"title":"Optimal clustering by merge-based branch-and-bound","authors":"P. Fränti, O. Virmajoki","doi":"10.3934/aci.2022004","DOIUrl":"https://doi.org/10.3934/aci.2022004","url":null,"abstract":"\u0000 We present a method to construct optimal clustering via a sequence of merge steps. We formulate the merge-based clustering as a minimum redundancy search tree, and then search the optimal clustering by a branch-and-bound technique. Optimal clustering is found regardless of the objective function used. We also consider two suboptimal polynomial time variants based on the proposed branch-and-bound technique. However, all variants are slow and has merely theoretical interest. We discuss the reasons for the results.\u0000","PeriodicalId":414924,"journal":{"name":"Applied Computing and Intelligence","volume":"38 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":"132353549","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}
Keywords are commonly used to summarize text documents. In this paper, we perform a systematic comparison of methods for automatic keyword extraction from web pages. The methods are based on three different types of features: statistical, structural and linguistic. Statistical features are the most common, but there are other clues in web documents that can also be used. Structural features utilize styling codes like header tags and links, but also the structure of the web page. Linguistic features can be based on detecting synonyms, semantic similarity of the words and part-of-speech tagging, but also concept hierarchy or a concept graph derived from Wikipedia. We compare different types of features to find out the importance of each of them. One of the key results is that stop word removal and other pre-processing steps are the most critical. The most successful linguistic feature was a pre-constructed list of words that had no synonyms in WordNet. A new method called ACI‑rank is also compiled from the best working combination.
{"title":"Combining statistical, structural, and linguistic features for keyword extraction from web pages","authors":"H. Shah, P. Fränti","doi":"10.3934/aci.2022007","DOIUrl":"https://doi.org/10.3934/aci.2022007","url":null,"abstract":"\u0000\u0000Keywords are commonly used to summarize text documents. In this paper, we perform a systematic comparison of methods for automatic keyword extraction from web pages. The methods are based on three different types of features: statistical, structural and linguistic. Statistical features are the most common, but there are other clues in web documents that can also be used. Structural features utilize styling codes like header tags and links, but also the structure of the web page. Linguistic features can be based on detecting synonyms, semantic similarity of the words and part-of-speech tagging, but also concept hierarchy or a concept graph derived from Wikipedia. We compare different types of features to find out the importance of each of them. One of the key results is that stop word removal and other pre-processing steps are the most critical. The most successful linguistic feature was a pre-constructed list of words that had no synonyms in WordNet. A new method called ACI‑rank is also compiled from the best working combination.\u0000\u0000","PeriodicalId":414924,"journal":{"name":"Applied Computing and Intelligence","volume":"73 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":"131602211","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}
Definition modeling, the task of generating a definition for a given term, is a relatively new area of research applied in evaluating word embeddings. Automatic generation of dictionary quality definitions has many applications in natural language processing, such as sentiment analysis, machine translation, and word sense disambiguation. Additionally, definition modeling is also helpful for evaluating the quality of word embeddings. As more research is done in this field, the need for a summary of different applications, approaches, and obstacles grows apparent. This review provides an overview of the current research in definition modeling and a list of future directions and trends.
{"title":"Definition modeling: literature review and dataset analysis","authors":"Noah Gardner, Hafiz Khan, Chih-Cheng Hung","doi":"10.3934/aci.2022005","DOIUrl":"https://doi.org/10.3934/aci.2022005","url":null,"abstract":"Definition modeling, the task of generating a definition for a given term, is a relatively new area of research applied in evaluating word embeddings. Automatic generation of dictionary quality definitions has many applications in natural language processing, such as sentiment analysis, machine translation, and word sense disambiguation. Additionally, definition modeling is also helpful for evaluating the quality of word embeddings. As more research is done in this field, the need for a summary of different applications, approaches, and obstacles grows apparent. This review provides an overview of the current research in definition modeling and a list of future directions and trends.","PeriodicalId":414924,"journal":{"name":"Applied Computing and Intelligence","volume":"799 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":"114874786","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}
Mohamed Wiem Mkaouer, T. Gaber, and Zaineb Chelly Dagdia
In late December 2019, the World Health Organization (WHO) announced the outbreak of a new type of coronavirus, named the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), also known as COVID-19. The deadliness of the virus has forced governments and countries to socially isolate their populations, causing a worldwide impact on the economy. Pandemic management has stressed health systems to work beyond their limits, adding more to the tragedy of losing millions of lives. As a natural response to such disasters, intelligent systems have been developed for various reasons related to virus detection, tracking and control. The social lockdown created a record level of online platforms and applications being used to resume professional and educational activities in a virtual environment. This has triggered an unprecedented growth in cybercrime. This paper presents the effects of the pandemic on computational intelligence and cybersecurity.
{"title":"Effects of COVID-19 pandemic on computational intelligence and cybersecurity: survey","authors":"Mohamed Wiem Mkaouer, T. Gaber, and Zaineb Chelly Dagdia","doi":"10.3934/aci.2022010","DOIUrl":"https://doi.org/10.3934/aci.2022010","url":null,"abstract":"In late December 2019, the World Health Organization (WHO) announced the outbreak of a new type of coronavirus, named the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), also known as COVID-19. The deadliness of the virus has forced governments and countries to socially isolate their populations, causing a worldwide impact on the economy. Pandemic management has stressed health systems to work beyond their limits, adding more to the tragedy of losing millions of lives. As a natural response to such disasters, intelligent systems have been developed for various reasons related to virus detection, tracking and control. The social lockdown created a record level of online platforms and applications being used to resume professional and educational activities in a virtual environment. This has triggered an unprecedented growth in cybercrime. This paper presents the effects of the pandemic on computational intelligence and cybersecurity.","PeriodicalId":414924,"journal":{"name":"Applied Computing and Intelligence","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":"115551915","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}
From the theory of algorithms, we know that the time complexity of finding the optimal solution for a traveling salesman problem (TSP) grows exponentially with the number of targets. However, the size of the problem instance is not the only factor that affects its difficulty. In this paper, we review existing measures to estimate the difficulty of a problem instance. We also introduce MST branches and two other measures called greedy path and greedy gap. The idea of MST branches is to generate minimum spanning tree (MST) and then calculate the number of branches in the tree. A branch is a target, which is connected to at least two other targets. We perform an extensive comparison of 11 measures to see how well they correlate to human and computer performance. We evaluate the measures based on time complexity, prediction capability, suitability, and practicality. The results show that while the MST branches measure is simple, fast to compute, and does not need to have the optimal solution as a reference unlike many other measures. It correlates equally good or even better than the best of the previous measures ‑ the number of targets, and the number of targets on the convex hull.
{"title":"Comparison of eleven measures for estimating difficulty of open-loop TSP instances","authors":"Lahari Sengupta, P. Fränti","doi":"10.3934/aci.2021001","DOIUrl":"https://doi.org/10.3934/aci.2021001","url":null,"abstract":"\u0000 From the theory of algorithms, we know that the time complexity of finding the optimal solution for a traveling salesman problem (TSP) grows exponentially with the number of targets. However, the size of the problem instance is not the only factor that affects its difficulty. In this paper, we review existing measures to estimate the difficulty of a problem instance. We also introduce MST branches and two other measures called greedy path and greedy gap. The idea of MST branches is to generate minimum spanning tree (MST) and then calculate the number of branches in the tree. A branch is a target, which is connected to at least two other targets. We perform an extensive comparison of 11 measures to see how well they correlate to human and computer performance. We evaluate the measures based on time complexity, prediction capability, suitability, and practicality. The results show that while the MST branches measure is simple, fast to compute, and does not need to have the optimal solution as a reference unlike many other measures. It correlates equally good or even better than the best of the previous measures ‑ the number of targets, and the number of targets on the convex hull.\u0000","PeriodicalId":414924,"journal":{"name":"Applied Computing and Intelligence","volume":"372 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":"120880876","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}
Anomaly detection strategies in industrial control systems mainly investigate the transmitting network traffic called network intrusion detection system. However, The measurement intrusion detection system inspects the sensors data integrated into the supervisory control and data acquisition center to find any abnormal behavior. An approach to detect anomalies in the measurement data is training supervised learning models that can learn to classify normal and abnormal data. But, a labeled dataset consisting of abnormal behavior, such as attacks, or malfunctions is extremely hard to achieve. Therefore, the unsupervised learning strategy that does not require labeled data for being trained can be helpful to tackle this problem. This study evaluates the performance of unsupervised learning strategies in anomaly detection using measurement data in control systems. The most accurate algorithms are selected to train unsupervised learning models, and the results show an accuracy of 98% in stealthy attack detection.
{"title":"Measurement data intrusion detection in industrial control systems based on unsupervised learning","authors":"S. Mokhtari, K. Yen","doi":"10.3934/aci.2021004","DOIUrl":"https://doi.org/10.3934/aci.2021004","url":null,"abstract":"Anomaly detection strategies in industrial control systems mainly investigate the transmitting network traffic called network intrusion detection system. However, The measurement intrusion detection system inspects the sensors data integrated into the supervisory control and data acquisition center to find any abnormal behavior. An approach to detect anomalies in the measurement data is training supervised learning models that can learn to classify normal and abnormal data. But, a labeled dataset consisting of abnormal behavior, such as attacks, or malfunctions is extremely hard to achieve. Therefore, the unsupervised learning strategy that does not require labeled data for being trained can be helpful to tackle this problem. This study evaluates the performance of unsupervised learning strategies in anomaly detection using measurement data in control systems. The most accurate algorithms are selected to train unsupervised learning models, and the results show an accuracy of 98% in stealthy attack detection.","PeriodicalId":414924,"journal":{"name":"Applied Computing and Intelligence","volume":"46 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":"127358278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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