Pub Date : 2022-11-03DOI: 10.1109/smap56125.2022.9942050
{"title":"SMAP 2022 Blank Page","authors":"","doi":"10.1109/smap56125.2022.9942050","DOIUrl":"https://doi.org/10.1109/smap56125.2022.9942050","url":null,"abstract":"","PeriodicalId":432172,"journal":{"name":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115721252","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 : 2022-11-03DOI: 10.1109/smap56125.2022.9942054
{"title":"SMAP 2022 Blank Page","authors":"","doi":"10.1109/smap56125.2022.9942054","DOIUrl":"https://doi.org/10.1109/smap56125.2022.9942054","url":null,"abstract":"","PeriodicalId":432172,"journal":{"name":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120955906","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 : 2022-11-03DOI: 10.1109/SMAP56125.2022.9942020
Antonios Koliarakis, Akrivi Krouska, C. Troussas, C. Sgouropoulou
Digital libraries constitute a considerable source of digital content providers, similar to video and music streaming services. Therefore, a solid, reliable and intelligent recommender system is essential to accommodate the plethora of different interests amongst its users. In view of this compelling need, this paper presents a modification to the classic collaborative filtering technique which incorporates the user’s actions into the recommendation production process. In this way, the user implicitly provides extra data to the collaborative filtering-based recommender system, resulting in higher quality recommendations and personalized search results, especially when combined with elements of content-based filtering. The results of the above-mentioned modification are presented by integrating the recommender system to a web-based digital lending library application. The evaluation of the application was made using the inspection method of cognitive walkthrough.
{"title":"Modified collaborative filtering for hybrid recommender systems and personalized search: The case of digital library","authors":"Antonios Koliarakis, Akrivi Krouska, C. Troussas, C. Sgouropoulou","doi":"10.1109/SMAP56125.2022.9942020","DOIUrl":"https://doi.org/10.1109/SMAP56125.2022.9942020","url":null,"abstract":"Digital libraries constitute a considerable source of digital content providers, similar to video and music streaming services. Therefore, a solid, reliable and intelligent recommender system is essential to accommodate the plethora of different interests amongst its users. In view of this compelling need, this paper presents a modification to the classic collaborative filtering technique which incorporates the user’s actions into the recommendation production process. In this way, the user implicitly provides extra data to the collaborative filtering-based recommender system, resulting in higher quality recommendations and personalized search results, especially when combined with elements of content-based filtering. The results of the above-mentioned modification are presented by integrating the recommender system to a web-based digital lending library application. The evaluation of the application was made using the inspection method of cognitive walkthrough.","PeriodicalId":432172,"journal":{"name":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132985676","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 : 2022-11-03DOI: 10.1109/SMAP56125.2022.9942197
Soumpenioti Vasiliki, Panagopoulos Apostolos
Effective logistics management focuses on information, but with technology, a promotional influence can be applied for competitive logistics strategy. Integrating information technology (IT) into business involves using information technology to introduce, enhance, complement and extend skills. Information technology has made it easier for managers to focus on tactical issues and core skills, while enhancing the use of logistics intermediate businesses, such as distribution, by automating many tedious logistics operations. This study aimed to evaluate/highlight through a literature review the impact of information technology on the performance of logistics companies.
{"title":"The impact of information systems on the logistics industry","authors":"Soumpenioti Vasiliki, Panagopoulos Apostolos","doi":"10.1109/SMAP56125.2022.9942197","DOIUrl":"https://doi.org/10.1109/SMAP56125.2022.9942197","url":null,"abstract":"Effective logistics management focuses on information, but with technology, a promotional influence can be applied for competitive logistics strategy. Integrating information technology (IT) into business involves using information technology to introduce, enhance, complement and extend skills. Information technology has made it easier for managers to focus on tactical issues and core skills, while enhancing the use of logistics intermediate businesses, such as distribution, by automating many tedious logistics operations. This study aimed to evaluate/highlight through a literature review the impact of information technology on the performance of logistics companies.","PeriodicalId":432172,"journal":{"name":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128883350","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 : 2022-11-03DOI: 10.1109/smap56125.2022.9942093
{"title":"SMAP 2022 Blank Page","authors":"","doi":"10.1109/smap56125.2022.9942093","DOIUrl":"https://doi.org/10.1109/smap56125.2022.9942093","url":null,"abstract":"","PeriodicalId":432172,"journal":{"name":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126748769","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 : 2022-11-03DOI: 10.1109/SMAP56125.2022.9942121
Elias Dritsas, M. Trigka, Phivos Mylonas
Weather forecasting is vital as extreme weather events can cause damage and even death. The science of meteorology in recent decades has made spectacular progress resulting in more reliable forecasts. Although meteorologists now have adopted modern tools for accurate weather forecasting, extreme and sudden climate changes in the atmosphere have posed accurate weather forecasting even more valuable. In this research paper, we present a multi-class classification methodology from machine learning (ML) in order to predict the five classes of weather conditions. Specifically, the One-Against-One (OAO) and One-Against-All (OAA) strategies are evaluated under Support Vector Machine (SVM) and Logistic Regression (LR) assuming, for comparison, Random Forest (RF) and k-Nearest Neighbours (k-NN). The prevailing model is linear SVM under the OAO method achieving the average Accuracy, Precision, Recall, F-Measure and Area Under Curve (AUC) of 96.64%, 96.8%, 96.6%, 96.6% and 98.5%, respectively.
{"title":"A Multi-class Classification Approach for Weather Forecasting with Machine Learning Techniques","authors":"Elias Dritsas, M. Trigka, Phivos Mylonas","doi":"10.1109/SMAP56125.2022.9942121","DOIUrl":"https://doi.org/10.1109/SMAP56125.2022.9942121","url":null,"abstract":"Weather forecasting is vital as extreme weather events can cause damage and even death. The science of meteorology in recent decades has made spectacular progress resulting in more reliable forecasts. Although meteorologists now have adopted modern tools for accurate weather forecasting, extreme and sudden climate changes in the atmosphere have posed accurate weather forecasting even more valuable. In this research paper, we present a multi-class classification methodology from machine learning (ML) in order to predict the five classes of weather conditions. Specifically, the One-Against-One (OAO) and One-Against-All (OAA) strategies are evaluated under Support Vector Machine (SVM) and Logistic Regression (LR) assuming, for comparison, Random Forest (RF) and k-Nearest Neighbours (k-NN). The prevailing model is linear SVM under the OAO method achieving the average Accuracy, Precision, Recall, F-Measure and Area Under Curve (AUC) of 96.64%, 96.8%, 96.6%, 96.6% and 98.5%, respectively.","PeriodicalId":432172,"journal":{"name":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121884267","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 : 2022-11-03DOI: 10.1109/SMAP56125.2022.9942218
Vyankatesh Adke, Priti Bakshi, Muniza Askari
The number of active social media users as of 2022 is estimated at 4.62 billion, accounting for 58% of the global population and registering rapid growth. This growth can be attributed to various factors such as advances in smartphone technology, cloud, Artificial Intelligence (AI), and rapid progress in network access mediums. Furthermore, social media platforms provide rich, collaborative ways for consumers to stay connected and to express their experiences, feedback, and sentiment on products and services across both public and private forums. This has a direct impact on enterprise brand valuations, and as such, it is becoming increasingly important for enterprises to actively track such public expressions.This has led to the rapid proliferation of social media management platforms and their adoption by enterprises to monitor and analyse social media trends and is a key aspect of digital transformation. This includes various use cases, for example, the ability to run marketing campaigns to boost brand awareness and to analyse consumer trends and brand perception through social media analytics.However, given the impact social media can have on enterprise brand valuations, it is now becoming increasingly important for businesses to re-evaluate their Customer Experience (CX) management strategies to include social media channels for customer service, beyond marketing and social insights. This is in addition to contemporary customer service channels such as online, contact centres and branches. This study aims to explore the key factors impacting the adoption of social media channels for customer experience management. It does so by way of a literature review conducted on the evolution of social media channels, their usage by consumers and the corresponding impact on enterprise brands, and the application of social media management tools. The study notes that social media adoption by enterprises depends on various factors such as usability, response strategies, blending with other customer service channels, technology integration and corporate governance. These factors are formulated in the form of research questions which could form the basis for future studies and be validated empirically. The paper contributes by providing practical insights to enterprises on factors to consider for adopting social media channels for customer service management.
{"title":"Factors Impacting Adoption of Social Media Channels for Customer Service Management: A Review","authors":"Vyankatesh Adke, Priti Bakshi, Muniza Askari","doi":"10.1109/SMAP56125.2022.9942218","DOIUrl":"https://doi.org/10.1109/SMAP56125.2022.9942218","url":null,"abstract":"The number of active social media users as of 2022 is estimated at 4.62 billion, accounting for 58% of the global population and registering rapid growth. This growth can be attributed to various factors such as advances in smartphone technology, cloud, Artificial Intelligence (AI), and rapid progress in network access mediums. Furthermore, social media platforms provide rich, collaborative ways for consumers to stay connected and to express their experiences, feedback, and sentiment on products and services across both public and private forums. This has a direct impact on enterprise brand valuations, and as such, it is becoming increasingly important for enterprises to actively track such public expressions.This has led to the rapid proliferation of social media management platforms and their adoption by enterprises to monitor and analyse social media trends and is a key aspect of digital transformation. This includes various use cases, for example, the ability to run marketing campaigns to boost brand awareness and to analyse consumer trends and brand perception through social media analytics.However, given the impact social media can have on enterprise brand valuations, it is now becoming increasingly important for businesses to re-evaluate their Customer Experience (CX) management strategies to include social media channels for customer service, beyond marketing and social insights. This is in addition to contemporary customer service channels such as online, contact centres and branches. This study aims to explore the key factors impacting the adoption of social media channels for customer experience management. It does so by way of a literature review conducted on the evolution of social media channels, their usage by consumers and the corresponding impact on enterprise brands, and the application of social media management tools. The study notes that social media adoption by enterprises depends on various factors such as usability, response strategies, blending with other customer service channels, technology integration and corporate governance. These factors are formulated in the form of research questions which could form the basis for future studies and be validated empirically. The paper contributes by providing practical insights to enterprises on factors to consider for adopting social media channels for customer service management.","PeriodicalId":432172,"journal":{"name":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132186738","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 : 2022-11-03DOI: 10.1109/SMAP56125.2022.9941824
G. P. Zarri
We propose here to consider the digitalized image of a CH entity as the association of two components, the first describing the physical properties of the entity, and the second its immaterial/symbolic aspects under the form of a “Digital Cultural Heritage Twin”. NKRL, the Narrative Knowledge Representation Language, has been chosen for the implementation of the Digital CH Twins components; some detailed examples of its use are given in the paper.
{"title":"Digital Cultural Heritage Twins, a Proposal and Some Examples","authors":"G. P. Zarri","doi":"10.1109/SMAP56125.2022.9941824","DOIUrl":"https://doi.org/10.1109/SMAP56125.2022.9941824","url":null,"abstract":"We propose here to consider the digitalized image of a CH entity as the association of two components, the first describing the physical properties of the entity, and the second its immaterial/symbolic aspects under the form of a “Digital Cultural Heritage Twin”. NKRL, the Narrative Knowledge Representation Language, has been chosen for the implementation of the Digital CH Twins components; some detailed examples of its use are given in the paper.","PeriodicalId":432172,"journal":{"name":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127680207","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 : 2022-11-03DOI: 10.1109/SMAP56125.2022.9941896
T. T. Kulatilake, P. L. R. S. Liyanage, G. H. K. Deemud, U. S. C. D. Silva, Disni Sriyaratna, Archchana Kugathasan
Procrastination refers to the voluntary delay of urgent tasks and can have several negative consequences such as stress, health issues and academic underachievement [47]. It is viewed within physiological research as a self-regulation failure [48]. Similar to procrastination, another severe problem which comes up within lots of people including students and teenagers is “Depression”. Depression is a massively widespread problem among people around the world as well as in Sri Lanka [49]. As a result of procrastination and depression, students has to face academic underachievement. One of the main cause of these widespread problems are Social media over-usage [50]. Therefore this paper presents a new tracker which presented as a mobile application with four main components. This research study is about identifying and tracking users’ facial emotions and eye-aspect ratio to analyze real emotions of the user via device inbuilt webcam to identify user fatigueness and procrastination. This study also analyzes user behavior in two selected social media platforms which are Facebook and Twitter and identifies the negativity and depressiveness of “Sinhala” content using Machine learning based Sentiment analysis approaches. Also as a companion, this paper introduces a chat-bot which communicates with the user in “Singlish” language. Our final products will be a complete mobile application which generates reports to the user based on the analysis done in the four components. As future work we will introduce AutoML approaches instead of traditional machine learning based approaches.
{"title":"PRODEP: Smart Social Media Procrastination and Depression Tracker","authors":"T. T. Kulatilake, P. L. R. S. Liyanage, G. H. K. Deemud, U. S. C. D. Silva, Disni Sriyaratna, Archchana Kugathasan","doi":"10.1109/SMAP56125.2022.9941896","DOIUrl":"https://doi.org/10.1109/SMAP56125.2022.9941896","url":null,"abstract":"Procrastination refers to the voluntary delay of urgent tasks and can have several negative consequences such as stress, health issues and academic underachievement [47]. It is viewed within physiological research as a self-regulation failure [48]. Similar to procrastination, another severe problem which comes up within lots of people including students and teenagers is “Depression”. Depression is a massively widespread problem among people around the world as well as in Sri Lanka [49]. As a result of procrastination and depression, students has to face academic underachievement. One of the main cause of these widespread problems are Social media over-usage [50]. Therefore this paper presents a new tracker which presented as a mobile application with four main components. This research study is about identifying and tracking users’ facial emotions and eye-aspect ratio to analyze real emotions of the user via device inbuilt webcam to identify user fatigueness and procrastination. This study also analyzes user behavior in two selected social media platforms which are Facebook and Twitter and identifies the negativity and depressiveness of “Sinhala” content using Machine learning based Sentiment analysis approaches. Also as a companion, this paper introduces a chat-bot which communicates with the user in “Singlish” language. Our final products will be a complete mobile application which generates reports to the user based on the analysis done in the four components. As future work we will introduce AutoML approaches instead of traditional machine learning based approaches.","PeriodicalId":432172,"journal":{"name":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","volume":"23 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128438809","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 : 2022-11-03DOI: 10.1109/SMAP56125.2022.9941803
Maria Nefeli Nikiforos, Konstantina Deliveri, Katia Lida Kermanidis, Adamantia G. Pateli
Despite their educational level and professional qualifications, an important percentage of highly-skilled migrants and refugees find employment in low-skill vocations throughout the world. Typical vocational domains include agriculture, cooking, crafting, construction, and hospitality. As a first step towards developing an educational tool for helping such underprivileged communities become acquainted with the sublanguage of their vocational domain in their host country, automatic domain identification among the aforementioned domains was attempted in this paper, using domain-specific textual data. Wikis and social networks provide a valuable data source for data mining, Natural Language Processing and machine learning tasks. Wikipedia articles, in regard to these domains, were collected and processed in order to create a novel text data set. Extracted linguistic features were used in the experiments with Random Forest combined with Adaboost, and Gradient Boosted Trees. The machine learning models achieved high performance in vocational domain identification (up to 99.93% accuracy).
{"title":"Machine Learning on Wikipedia Text for the Automatic Identification of Vocational Domains of Significance for Displaced Communities","authors":"Maria Nefeli Nikiforos, Konstantina Deliveri, Katia Lida Kermanidis, Adamantia G. Pateli","doi":"10.1109/SMAP56125.2022.9941803","DOIUrl":"https://doi.org/10.1109/SMAP56125.2022.9941803","url":null,"abstract":"Despite their educational level and professional qualifications, an important percentage of highly-skilled migrants and refugees find employment in low-skill vocations throughout the world. Typical vocational domains include agriculture, cooking, crafting, construction, and hospitality. As a first step towards developing an educational tool for helping such underprivileged communities become acquainted with the sublanguage of their vocational domain in their host country, automatic domain identification among the aforementioned domains was attempted in this paper, using domain-specific textual data. Wikis and social networks provide a valuable data source for data mining, Natural Language Processing and machine learning tasks. Wikipedia articles, in regard to these domains, were collected and processed in order to create a novel text data set. Extracted linguistic features were used in the experiments with Random Forest combined with Adaboost, and Gradient Boosted Trees. The machine learning models achieved high performance in vocational domain identification (up to 99.93% accuracy).","PeriodicalId":432172,"journal":{"name":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131100775","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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