2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)最新文献_第2页

Personalized augmented reality experiences in museums using Google Cardboards 使用谷歌纸板在博物馆中进行个性化增强现实体验

2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2017-07-01 DOI: 10.1109/SMAP.2017.8022676

Marinos Theodorakopoulos, Nikos Papageorgopoulos, A. Mourti, Angeliki Antoniou, Manolis Wallace, George Lepouras, C. Vassilakis, N. Platis

引用次数: 9

A graph-based semantic recommender system for a reflective and personalised museum visit: Extended abstract 一个基于图形的语义推荐系统，用于反思和个性化的博物馆参观:扩展摘要

2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2017-07-01 DOI: 10.1109/SMAP.2017.8022674

Louis Deladiennée, Y. Naudet

引用次数: 11

Detecting genuinely read parts of web documents 检测web文档的真正阅读部分

2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2017-07-01 DOI: 10.1109/SMAP.2017.8022658

Patrik Hlavac, Marián Simko

引用次数: 1

Forecasting elections from VAA data: What the undecided would vote? 从VAA数据预测选举:尚未决定的选民会投什么票?

2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2017-07-01 DOI: 10.1109/SMAP.2017.8022666

N. Tsapatsoulis, Marilena Agathokleous

{"title":"Forecasting elections from VAA data: What the undecided would vote?","authors":"N. Tsapatsoulis, Marilena Agathokleous","doi":"10.1109/SMAP.2017.8022666","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022666","url":null,"abstract":"In many Voting Advice Applications (VAAs) a supplementary question concerning the voting intention of a VAA user is included. The data that are collected through this question can serve a variety of purposes, election forecast being one of them. However, it appears that the majority of VAA users who answer this question select safe choices such as “I prefer not to say” and “I am undecided”. In this study we investigate at what degree we can predict, with the aid of machine learning techniques, the voting intention of the above-mentioned users using as input their choices in the VAA policy statements. The results show an accuracy higher than 60%, supposed that sufficient training examples for each party that participates in the elections exist so as to model each party users. Also, it appears that there is significant difference on the distribution per party for the users who select “I prefer not to say” and those who select “I am undecided”. As a consequence of these findings one would suggest that for effective election forecast it is required to (a) distribute the VAA users who select the previously mentioned choices in the voting intention question in a more sophisticated and intelligent way than that followed in traditional poll methods, and (b) the VAA users who select each one of those choices should be handled separately.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127576101","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}

引用次数: 0

Self-organizing museum visitor communities: A participatory action research based approach 自组织博物馆游客社区:基于参与式行动研究的方法

2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2017-07-01 DOI: 10.1109/SMAP.2017.8022677

Eirini-Eleni Tsiropoulou, Athina Thanou, S. Paruchuri, S. Papavassiliou

引用次数: 6

Affect state recognition for adaptive human robot interaction in learning environments 学习环境中自适应人机交互的影响状态识别

2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2017-07-01 DOI: 10.1109/SMAP.2017.8022670

Dimitrios Antonaras, C. Pavlidis, N. Vretos, P. Daras

{"title":"Affect state recognition for adaptive human robot interaction in learning environments","authors":"Dimitrios Antonaras, C. Pavlidis, N. Vretos, P. Daras","doi":"10.1109/SMAP.2017.8022670","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022670","url":null,"abstract":"Previous studies of robots used in learning environments suggest that the interaction between learner and robot is able to enhance the learning procedure towards a better engagement of the learner. Moreover, intelligent robots can also adapt their behavior during a learning process according to certain criteria resulting in increasing cognitive learning gains. Motivated by these results, we propose a novel Human Robot Interaction framework where the robot adjusts its behavior to the affect state of the learner. Our framework uses the theory of flow to label different affect states (i.e., engagement, boredom and frustration) and adapt the robot's actions. Based on the automatic recognition of these states, through visual cues, our method adapt the learning actions taking place at this moment and performed by the robot. This results in keeping the learner at most times engaged in the learning process. In order to recognizing the affect state of the user a two step approach is followed. Initially we recognize the facial expressions of the learner and therefore we map these to an affect state. Our algorithm perform well even in situations where the environment is noisy due to the presence of more than one person and/or situations where the face is partially occluded.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"39 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132468640","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}

引用次数: 1

A density based algorithm for community detection in hyper-networks 一种基于密度的超网络社区检测算法

2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2017-07-01 DOI: 10.1109/SMAP.2017.8022668

D. Vogiatzis, A. Keros

引用次数: 2

Formalising and evaluating Cultural User Experience 形式化和评估文化用户体验

2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2017-07-01 DOI: 10.1109/SMAP.2017.8022675

M. Konstantakis, Konstantinos Michalakis, John Aliprantis, Eirini Kalatha, G. Caridakis

引用次数: 22

Extracting emotions from speech using a bag-of-visual-words approach 使用视觉词袋方法从语音中提取情感

2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2017-07-01 DOI: 10.1109/SMAP.2017.8022672

E. Spyrou, Theodoros Giannakopoulos, Dimitris Sgouropoulos, Michalis Papakostas

{"title":"Extracting emotions from speech using a bag-of-visual-words approach","authors":"E. Spyrou, Theodoros Giannakopoulos, Dimitris Sgouropoulos, Michalis Papakostas","doi":"10.1109/SMAP.2017.8022672","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022672","url":null,"abstract":"Recognition of humans' emotions may be crucial in certain applications involving e.g., human-computer interaction, monitoring of elderly, understanding the affective state of learners during a course etc. To this goal and depending on the application and the environment, one may use physiological parameters (e.g., heart rate, brain activity etc.) which are typically obtrusive, or analyze other modalities that may be extracted by simply observing a human, such as visual (e.g., her/his facial expressions, gestures, skeletal motion etc.) or audio (e.g., speech). In many applications the only available modality is the latter one, i.e., the human's voice. In this work we aim to analyze a speaker's emotions by relying only on paralinguistic information, extracted by her/his voice, thus discarding the linguistic aspect of speech (i.e., the spoken words). To this goal, we propose a novel emotion classification approach that has been inspired by computer vision tasks. We use a spectrogram, which is a visual representation of the spectrum of an audio segment. We then extract features and code them using a visual vocabulary and represent a spectrogram as a “bag-of-visual words.” This representation is used for classifying an audio segment to an emotion class. We evaluate our approach on 3 datasets that contain speech from different languages and compare it to baseline methods.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129032658","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}

引用次数: 7

Personalized query expansion utilizing multi-relational social data 利用多关系社会数据进行个性化查询扩展

2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2017-07-01 DOI: 10.1109/SMAP.2017.8022669

Xuan Wu, Dong Zhou, Yu Xu, S. Lawless

{"title":"Personalized query expansion utilizing multi-relational social data","authors":"Xuan Wu, Dong Zhou, Yu Xu, S. Lawless","doi":"10.1109/SMAP.2017.8022669","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022669","url":null,"abstract":"Social tagging systems have been widely used as a way to annotate and categorize Web resources. However, users often use unrestricted vocabulary to tag and describe resources. On the contrast, annotators of Web documents may use very different words to describe the same concept. In the past few years, numerous personalized query expansion methods have been proposed to tackle the vocabulary mismatch problem. Many of them are based on the probabilistic-based techniques or graph-based techniques, but they ignored the multi-relational characteristics existed in the social data. In this paper, we explore multiple semantic relationships from social tagging systems, including relationships between tags, between words and between tags and words. Three affinity graphs are built based on the features derived from tags and words. In addition, we incorporate pseudo-relevance feedback information obtained from top-ranked documents to regularize the smoothness of multiple associations over the three affinity graphs. The key of this paper is considering above three affinity graphs into a novel query expansion model and aim to produce better personalized search results. Experiments conducted on a real-world dataset validate the effectiveness of the proposed approach.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116039974","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}

引用次数: 3