Pub Date : 2022-09-20DOI: 10.1142/s1793351x22500076
G. Luger
{"title":"A Brief History and Foundations for Modern Artificial Intelligence","authors":"G. Luger","doi":"10.1142/s1793351x22500076","DOIUrl":"https://doi.org/10.1142/s1793351x22500076","url":null,"abstract":"","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126728705","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 : 2022-09-20DOI: 10.1142/s1793351x22400153
Mobin Akhtar, D. Ahamad, A. Shatat, A. Shatat
{"title":"Big Data Classification in IOT Healthcare Application Using Optimal Deep Learning","authors":"Mobin Akhtar, D. Ahamad, A. Shatat, A. Shatat","doi":"10.1142/s1793351x22400153","DOIUrl":"https://doi.org/10.1142/s1793351x22400153","url":null,"abstract":"","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121923513","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 : 2022-07-08DOI: 10.1142/s1793351x22500064
Haiping Xu, Ran Wei, Richard de Groof, Joshua Carberry
To address the uncertainty about the quality of online merchandise, e-commerce sites often provide product review ranking services to help customers make purchasing decisions. Such services can be very useful, but they are not necessarily reliable when the ranking results are based on ratings without considering their reliability. In this paper, we propose a reliable evidence-based approach to online product evaluation by using text mining to analyze product reviews while taking into account the reliability of each review. We parse the product reviews and classify the opinion orientations for each recognized product feature as positive or negative. Then, we weight the classified opinion orientations by their reliability and use them as independent evidence to calculate the belief values of the product using Dempster-Shafer (D-S) theory. Based on the belief values of a list of similar products, we can calculate their product effectiveness and cost-effectiveness values for product ranking. The case studies show that our approach can greatly help customers make better decisions when choosing the right online products.
{"title":"Evaluating Online Products Using Text Mining: A Reliable Evidence-Based Approach","authors":"Haiping Xu, Ran Wei, Richard de Groof, Joshua Carberry","doi":"10.1142/s1793351x22500064","DOIUrl":"https://doi.org/10.1142/s1793351x22500064","url":null,"abstract":"To address the uncertainty about the quality of online merchandise, e-commerce sites often provide product review ranking services to help customers make purchasing decisions. Such services can be very useful, but they are not necessarily reliable when the ranking results are based on ratings without considering their reliability. In this paper, we propose a reliable evidence-based approach to online product evaluation by using text mining to analyze product reviews while taking into account the reliability of each review. We parse the product reviews and classify the opinion orientations for each recognized product feature as positive or negative. Then, we weight the classified opinion orientations by their reliability and use them as independent evidence to calculate the belief values of the product using Dempster-Shafer (D-S) theory. Based on the belief values of a list of similar products, we can calculate their product effectiveness and cost-effectiveness values for product ranking. The case studies show that our approach can greatly help customers make better decisions when choosing the right online products.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125551254","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 : 2022-07-08DOI: 10.1142/s1793351x22400128
Md. Enamul Haque, Suzann Pershing
{"title":"Predicting Acute Endophthalmitis for Patients with Cataract Surgery Using Hierarchical and Probabilistic Representation of Clinical Codes","authors":"Md. Enamul Haque, Suzann Pershing","doi":"10.1142/s1793351x22400128","DOIUrl":"https://doi.org/10.1142/s1793351x22400128","url":null,"abstract":"","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124323737","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 : 2022-07-08DOI: 10.1142/s1793351x22500040
Sudhashree Sayenju, Ramazan S. Aygun, Jonathan W. Boardman, Duleep Prasanna Rathgamage Don, Yifan Zhang, Bill Franks, Sereres Johnston, George Lee, D. Sullivan, Girish Modgil
{"title":"Quantification and Mitigation of Directional Pairwise Class Confusion Bias in a Chatbot Intent Classification Model","authors":"Sudhashree Sayenju, Ramazan S. Aygun, Jonathan W. Boardman, Duleep Prasanna Rathgamage Don, Yifan Zhang, Bill Franks, Sereres Johnston, George Lee, D. Sullivan, Girish Modgil","doi":"10.1142/s1793351x22500040","DOIUrl":"https://doi.org/10.1142/s1793351x22500040","url":null,"abstract":"","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116469841","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 : 2022-07-08DOI: 10.1142/s1793351x2240013x
Magnus Bender, Felix Kuhr, Tanya Braun
,
，
{"title":"To Extend or Not to Extend? Enriching a Corpus with Complementary and Related Documents","authors":"Magnus Bender, Felix Kuhr, Tanya Braun","doi":"10.1142/s1793351x2240013x","DOIUrl":"https://doi.org/10.1142/s1793351x2240013x","url":null,"abstract":",","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129144095","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 : 2022-05-28DOI: 10.1142/s1793351x22400086
Adan Häfliger, Shuichi Kurabayashi
Despite modern game systems adopting motion matching to retrieve an appropriate short motion clip from a database in real-time, existing methods struggle to support complex gaming scenes due to their inability to adapt live the motion retrieval based on the context. This paper presents the design and implementation of a context-aware character animation system, synthesizing realistic animations suitable for complex game scenes from a large-scale motion database. This system, called dynamic motion matching (DyMM), enables geometry and objects aware motion synthesis by introducing a two-phase context computation: an offline subspace decomposition of motion clips for creating a set of retrieval sub-spaces tailored to specific contexts and a subspace ensemble matching to compare relevant sub-features to determine the most appropriate motion clip. We also show the system architecture and implementation details applicable to a production-grade game engine. We verified the effectiveness of our method with industry-level motion data captured by professional game artists for multiple configurations and character controllers. The results of this study show that, by finding motion clips that comply well with the scene context, one can leverage large motion capture datasets to create practical systems that generate believable and controllable animations for games.
{"title":"Dynamic Motion Matching: Design and Implementation of a Context-Aware Animation System for Games","authors":"Adan Häfliger, Shuichi Kurabayashi","doi":"10.1142/s1793351x22400086","DOIUrl":"https://doi.org/10.1142/s1793351x22400086","url":null,"abstract":"Despite modern game systems adopting motion matching to retrieve an appropriate short motion clip from a database in real-time, existing methods struggle to support complex gaming scenes due to their inability to adapt live the motion retrieval based on the context. This paper presents the design and implementation of a context-aware character animation system, synthesizing realistic animations suitable for complex game scenes from a large-scale motion database. This system, called dynamic motion matching (DyMM), enables geometry and objects aware motion synthesis by introducing a two-phase context computation: an offline subspace decomposition of motion clips for creating a set of retrieval sub-spaces tailored to specific contexts and a subspace ensemble matching to compare relevant sub-features to determine the most appropriate motion clip. We also show the system architecture and implementation details applicable to a production-grade game engine. We verified the effectiveness of our method with industry-level motion data captured by professional game artists for multiple configurations and character controllers. The results of this study show that, by finding motion clips that comply well with the scene context, one can leverage large motion capture datasets to create practical systems that generate believable and controllable animations for games.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133769693","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 : 2022-05-18DOI: 10.1142/s1793351x22400098
A. Elwood, Alberto Gasparin, A. Rozza
With the rise in use of social media to promote branded products, the demand for effective influencer marketing has increased. Brands are looking for improved ways to identify valuable influencers among a vast catalogue; this is even more challenging with micro-influencers, which are more affordable than mainstream ones but difficult to discover. In this paper, we propose a novel multi-task learning framework to improve the state of the art in micro-influencer ranking based on multimedia content. Moreover, since the visual congruence between a brand and influencer has been shown to be a good measure of compatibility, we provide an effective visual method for interpreting our model’s decisions, which can also be used to inform brands’ media strategies. We compare with the current state of the art on a recently constructed public dataset and we show significant improvement both in terms of accuracy and model complexity. We also introduce a methodology for tuning the image and text contribution to the final ranking score. The techniques for ranking and interpretation presented in this work can be generalized to arbitrary multimedia ranking tasks that have datasets with a similar structure.
{"title":"Ranking Micro-Influencers: A Multimedia Framework with Multi-Task and Interpretable Architectures","authors":"A. Elwood, Alberto Gasparin, A. Rozza","doi":"10.1142/s1793351x22400098","DOIUrl":"https://doi.org/10.1142/s1793351x22400098","url":null,"abstract":"With the rise in use of social media to promote branded products, the demand for effective influencer marketing has increased. Brands are looking for improved ways to identify valuable influencers among a vast catalogue; this is even more challenging with micro-influencers, which are more affordable than mainstream ones but difficult to discover. In this paper, we propose a novel multi-task learning framework to improve the state of the art in micro-influencer ranking based on multimedia content. Moreover, since the visual congruence between a brand and influencer has been shown to be a good measure of compatibility, we provide an effective visual method for interpreting our model’s decisions, which can also be used to inform brands’ media strategies. We compare with the current state of the art on a recently constructed public dataset and we show significant improvement both in terms of accuracy and model complexity. We also introduce a methodology for tuning the image and text contribution to the final ranking score. The techniques for ranking and interpretation presented in this work can be generalized to arbitrary multimedia ranking tasks that have datasets with a similar structure.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133547332","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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