2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)最新文献_第2页

On the Evaluation of Coarse Grained Parallelism in AV1 Video Coding AV1视频编码中粗粒度并行性的评价

2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2018-09-01 DOI: 10.1109/SMAP.2018.8501888

Panos K. Papadopoulos, M. Koziri, Nikos Tziritas, Thanasis Loukopoulos, Ioannis Anagnostopoulos, Petr Šaloun, David Andresic

{"title":"On the Evaluation of Coarse Grained Parallelism in AV1 Video Coding","authors":"Panos K. Papadopoulos, M. Koziri, Nikos Tziritas, Thanasis Loukopoulos, Ioannis Anagnostopoulos, Petr Šaloun, David Andresic","doi":"10.1109/SMAP.2018.8501888","DOIUrl":"https://doi.org/10.1109/SMAP.2018.8501888","url":null,"abstract":"Video compression is arguably one of the most time consuming tasks, especially at high resolutions. For this reason parallelization has been used at various levels: from coarse grained schemes that consider groups of blocks or frames as granules to fine grained approaches that parallelize the compression within a block of pels, e.g., at motion estimation. Given that the decade old H.264/AVC is showing its age in the 4K era, AV1 was recently launched by AOMedia as a royalty free standard promising significant compression gains (for the same quality) over the older H.264/AVC and VP9 protocols. Since AV1 is currently at its infancy, most initial research efforts concentrated on identifying possible strengths and shortcomings of the standard at various settings, focusing on video quality and compression efficiency. However, time performance and parallelism potential are equally important, particularly for media providers operating in the Cloud. In this paper we provide evaluation results of coarse grained parallelism methods implemented by the reference AV1 codec.","PeriodicalId":291905,"journal":{"name":"2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123984843","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

SMAP 2018 Keynotes

2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2018-09-01 DOI: 10.1109/smap.2018.8501863

引用次数: 0

Deep Guessing: Generating Meaningful Personalized Quizzes on Historical Topics by Introducing Wikicategories in Doc2Vec 深度猜测:通过在Doc2Vec中引入维基分类来生成有意义的历史主题个性化测验

2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2018-09-01 DOI: 10.1109/SMAP.2018.8501891

Borja Varela-Brea, Martín López Nores, Y. Blanco-Fernández, J. Pazos-Arias, A. Gil-Solla, M. Cabrer

引用次数: 1

Arm Gesture Recognition using a Convolutional Neural Network 使用卷积神经网络的手臂手势识别

2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2018-09-01 DOI: 10.1109/SMAP.2018.8501886

Eirini Mathe, Alexandros Mitsou, E. Spyrou, Phivos Mylonas

引用次数: 9

Improving Social Vote Recommendation in VAAs: The Effects of Political Profile Augmentation and Classification Method 改进VAAs的社会投票推荐:政治形象增强和分类方法的效果

2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2018-09-01 DOI: 10.1109/SMAP.2018.8501885

Constantinos Djouvas, Antri Antoniou, N. Tsapatsoulis

{"title":"Improving Social Vote Recommendation in VAAs: The Effects of Political Profile Augmentation and Classification Method","authors":"Constantinos Djouvas, Antri Antoniou, N. Tsapatsoulis","doi":"10.1109/SMAP.2018.8501885","DOIUrl":"https://doi.org/10.1109/SMAP.2018.8501885","url":null,"abstract":"Voting Advice Applications (VAAs) are online tools used by voters in order to identify their political stance in relation to parties / candidates running for elections. Traditional approaches are based on some standard vector space distance metrics (e.g. Euclidean Distance), that measure the distance between the political profile of a voter - expressed by her/his answers on a series of policy statements - against those (answers) of parties / candidates. A new paradigm, the so-called Social Vote Recommendation (SVR), extends traditional VAAs with a peer (i.e., voter to voter) opinion matching based on the principles of collaborative filtering. The problem of vote recommendation in that case is equivalent to the problem of matching a multidimensional vector (profile of the current voter) to a set of vectors (profiles of voters that support a particular political party). Previously, this functionality was offered using the Mahalanobis distance; a model that represents the ‘average’ voter of each party is created, and then, the distance between the active user and the ‘average’ voter of each party is calculated. In this paper we explore ways in which current best practices can be evaluated and compared to potentially better performing machine learning approaches for use in the domain of VAAs. In addition, we investigate the effects of political profile augmentation with the so-called supplementary questions and we show that users’ education level and demographics, such as gender and age, along with the reason of vote choice consistently improve SVR.","PeriodicalId":291905,"journal":{"name":"2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131544698","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: 2

Topic modelling on Instagram hashtags: An alternative way to Automatic Image Annotation? Instagram标签上的主题建模:自动图像注释的另一种方法?

2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2018-09-01 DOI: 10.1109/SMAP.2018.8501887

A. Argyrou, Stamatios Giannoulakis, N. Tsapatsoulis

{"title":"Topic modelling on Instagram hashtags: An alternative way to Automatic Image Annotation?","authors":"A. Argyrou, Stamatios Giannoulakis, N. Tsapatsoulis","doi":"10.1109/SMAP.2018.8501887","DOIUrl":"https://doi.org/10.1109/SMAP.2018.8501887","url":null,"abstract":"Automatic Image Annotation (AIA) is the process of assigning tags to digital images without the intervention of humans. Most of the modern automatic image annotation methods are based on the learning by example paradigm. In those methods building the training examples, that is, pairs of images and related tags, is the first critical step. We have shown in our previous studies that hashtags accompanying images in social media and especially the Instagram provide a reach source for creating training sets for AIA. However, we concluded that only 20% of the Instagram hashtags describe the actual content of the image they accompany, thus, a series of filtering steps need to apply in order to identify the appropriate hashtags. In this paper we apply topic modelling with Latent Dirichlet Allocation (LDA) on Instagram hashtags in order to predict the subject of the related images. Since a topic is composed by a set of related terms, the identification of the visual topic of an Instagram image, through the proposed method, provides a plausible set of tags to be used in the context of training AIA methods.","PeriodicalId":291905,"journal":{"name":"2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133179108","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: 29

Developing a scientific knowledge graph through conceptual linking of academic classifications 通过学术分类的概念链接，构建科学知识图谱

2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2018-09-01 DOI: 10.1109/SMAP.2018.8501869

Charalampos Bratsas, Panagiotis-Marios Filippidis, Sotirios Karampatakis, Lazaros Ioannidis

{"title":"Developing a scientific knowledge graph through conceptual linking of academic classifications","authors":"Charalampos Bratsas, Panagiotis-Marios Filippidis, Sotirios Karampatakis, Lazaros Ioannidis","doi":"10.1109/SMAP.2018.8501869","DOIUrl":"https://doi.org/10.1109/SMAP.2018.8501869","url":null,"abstract":"This paper describes the process of the construction of a scientific knowledge graph via semantic annotation and linking of academic research fields. The method aims to combine the broad scope of generic classifications of research areas with the specialized knowledge and domain fields of specific scientific classifications in order to build a unified scientific knowledge graph including all the research fields of the respective scientific areas in a common hierarchy. First, a survey of scientific classifications has been conducted in order to identify the ones that are the most common and complete for the knowledge graph to build upon. Classifications from different research domains have been used to semantically annotate their thematic topics and fields. The various scientific fields are connected based on their similarity, enlightening and creating, in this way, cross-domain research fields. A core scientific graph containing main fields of science with their relations to domain specific vocabularies and classifications has been created. This knowledge graph can be used to retrieve specific scientific fields in a related, broader or narrower, research area. Finally, a use case leveraging the semantic features of the graph is presented, indicating its usefulness for research activities and PhD web services.","PeriodicalId":291905,"journal":{"name":"2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131388192","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: 2

SMAP 2018 Proceedings

2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2018-09-01 DOI: 10.1109/smap.2018.8501861

Sergio Ilarri, Luis Alías Ilarri

引用次数: 0

Computing Query Sets for Better Exploring Raw Data Collections 计算查询集以更好地探索原始数据集合

2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2018-09-01 DOI: 10.1109/SMAP.2018.8501871

Genoveva Vargas-Solar, J. Zechinelli-Martini, Javier-Alfonso Espinosa-Oviedo

引用次数: 3

SMAP 2018 Index SMAP 2018指数

2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) Pub Date : 2018-09-01 DOI: 10.1109/smap.2018.8501873

引用次数: 0