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

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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
Marinos Theodorakopoulos, Nikos Papageorgopoulos, A. Mourti, Angeliki Antoniou, Manolis Wallace, George Lepouras, C. Vassilakis, N. Platis
In this paper we examine the suitability of the Google Cardboard as a means for the delivery of personalized cultural experiences. Specifically, we develop the content and create the application required in order to provide highly personalized visits to the Archaeological Museum in Tripolis, Greece. We also examine the usability issues related to the use of Google Cardboards. Early results are promising, and based on them we also outline the next steps ahead.
在本文中,我们研究了谷歌纸板作为一种传递个性化文化体验的手段的适用性。具体来说,我们开发内容并创建所需的应用程序,以便提供高度个性化的希腊的黎波里考古博物馆参观。我们还研究了与使用谷歌纸板相关的可用性问题。早期的结果是有希望的,在此基础上,我们还概述了下一步的工作。
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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
Louis Deladiennée, Y. Naudet
Offering personalised recommendations to visitors of a museum is a complex problem inherent to physical spaces. When at the same time specific applicative or museum objectives have to be taken into account, this becomes even more complicated. We introduce here a graph-based semantic recommender approach relying on ontological formalisation of knowledge about manipulated entities to solve the multi-dimensional recommendation problem encountered in museums.
向博物馆的参观者提供个性化的推荐是物理空间固有的复杂问题。当同时考虑到特定的应用或博物馆目标时,这就变得更加复杂了。本文介绍了一种基于图的语义推荐方法,该方法依赖于被操纵实体知识的本体论形式化来解决博物馆中遇到的多维推荐问题。
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引用次数: 11
Detecting genuinely read parts of web documents 检测web文档的真正阅读部分
Patrik Hlavac, Marián Simko
In this paper, we propose a method for detecting genuinely read parts of documents based on gaze data from eye tracker. This work deals with the possibilities of identifying user interaction with (web-based) documents. Our algorithm takes into account user's eye fixation information and maps their coordinates onto word-level elements. These are then processed with respect to their relative word distance. Unlike studies that calculate distance in points that eyes moved around the screen, we consider the distance of words in the word sequence. We evaluate our approach by conducting a user study that shows promising results.
在本文中,我们提出了一种基于眼动仪的注视数据检测文档真实阅读部分的方法。这项工作涉及识别用户与(基于web的)文档交互的可能性。我们的算法考虑了用户的眼球注视信息,并将其坐标映射到词级元素上。然后根据它们的相对单词距离对它们进行处理。与计算眼睛在屏幕上移动的点的距离的研究不同,我们考虑的是单词序列中单词的距离。我们通过进行用户研究来评估我们的方法,该研究显示出有希望的结果。
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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?
N. Tsapatsoulis, Marilena Agathokleous
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.
在许多投票建议应用程序(VAAs)中,包含有关VAA用户投票意向的补充问题。通过这个问题收集的数据可以服务于各种目的,选举预测就是其中之一。然而,似乎大多数VAA用户在回答这个问题时都选择了安全的选项,比如“我不想说”和“我还没决定”。在这项研究中,我们研究了在多大程度上,我们可以借助机器学习技术,预测上述用户在VAA政策声明中使用他们的选择作为输入的投票意图。假设每个参与选举的政党都有足够的训练样本,从而对每个政党的用户进行建模,结果表明准确率高于60%。此外,选择“我不想说”和选择“我不确定”的用户的人均分配似乎也存在显著差异。根据这些研究结果,我们建议要有效预测选举,必须(a)以比传统民意调查方法更复杂和智能的方式分配在投票意向问题中选择上述选项的VAA用户,以及(b)应分别处理选择每一个选项的VAA用户。
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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
Eirini-Eleni Tsiropoulou, Athina Thanou, S. Paruchuri, S. Papavassiliou
This paper introduces a self-organizing museum visitor communities' formation exploiting their personal characteristics and social interactions, aiming at enhancing their visiting experience based on a participatory action research (PAR) process. Initially, visitors' (a) interest and social ties, (b) expertise and willingness for participation in communities and (c) physical ties, are captured towards formulating their communities, and selecting the facilitator of each community. The latter will lead the PAR process, the outcome of which will be adopted by the members of each community. A museum touring framework is proposed towards maximizing visitors' perceived Quality of Experience (QoE), while three different community formation alternatives are studied and evaluated.
本文基于参与式行动研究(PAR)过程,介绍了一种利用个人特征和社会互动的自组织博物馆游客社区的形成,旨在提升游客的参观体验。最初,访问者的(a)兴趣和社会关系,(b)参与社区的专业知识和意愿,以及(c)物质联系,都被用来制定他们的社区,并选择每个社区的调解人。后者将领导PAR进程,其结果将由每个社区的成员通过。提出了一个博物馆游览框架,以最大限度地提高游客的感知体验质量(QoE),同时研究和评估了三种不同的社区形成方案。
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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
Dimitrios Antonaras, C. Pavlidis, N. Vretos, P. Daras
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.
先前对学习环境中使用的机器人的研究表明,学习者和机器人之间的互动能够增强学习过程,使学习者更好地参与。此外,智能机器人还可以在学习过程中根据一定的标准调整自己的行为,从而增加认知学习收益。基于这些结果,我们提出了一种新的人机交互框架,其中机器人根据学习者的影响状态调整其行为。我们的框架使用心流理论来标记不同的影响状态(即投入,无聊和沮丧)并调整机器人的行动。基于对这些状态的自动识别,我们的方法通过视觉线索来适应机器人在这一时刻发生的学习动作。这样做的结果是让学习者在大多数时候都沉浸在学习过程中。为了识别用户的情感状态,采用了两步方法。首先,我们识别学习者的面部表情,因此我们将这些表情映射到情感状态。我们的算法即使在由于多人存在而导致环境嘈杂和/或面部部分遮挡的情况下也表现良好。
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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
D. Vogiatzis, A. Keros
We propose an efficient community detection algorithm for networks that comprise more than one entities, such as users, tags and items, with ternary or higher relations between them. Such networks are also known as multi-partite and can be used for representing social tagging systems but also the activity in streaming media. Detecting communities in multi-paritite networks entails different challenges than in simple networks. The proposed algorithm is able to detect crisp or overlapping communities, and is applied on four data sets from social tagging systems and Twitter, and is compared with other multi-partite community detection algorithms.
我们提出了一种有效的社区检测算法,用于包含多个实体的网络,如用户、标签和项目,它们之间具有三元或更高的关系。这种网络也被称为多方网络,可用于表示社会标签系统,也可用于表示流媒体中的活动。在多方网络中检测社区与在简单网络中检测社区面临不同的挑战。该算法能够检测出清晰或重叠的社区,并将其应用于来自社交标签系统和Twitter的四个数据集上,并与其他多方社区检测算法进行了比较。
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引用次数: 2
Formalising and evaluating Cultural User Experience 形式化和评估文化用户体验
M. Konstantakis, Konstantinos Michalakis, John Aliprantis, Eirini Kalatha, G. Caridakis
User Experience (UX) is considered a subjective and universal concept which contributes to the success of any Information and Communications Technology (ICT) framework. However, in both Information Systems and Cultural Technology research, little attention has been paid to the evaluation of UX with technologies in cultural heritage environments. Since Cultural User Experience (CUX) is an important factor, a formal classification of how to design for and evaluate CUX is necessary. This paper attempts to analyze and evaluate the aspects of CUX methodologies that are currently available and to specify future designing improvements for UX evaluation methods.
用户体验(UX)被认为是一个主观和普遍的概念,它有助于任何信息和通信技术(ICT)框架的成功。然而,无论是信息系统研究还是文化技术研究,都很少关注文化遗产环境下技术对用户体验的评价。由于文化用户体验(CUX)是一个重要因素,因此有必要对如何设计和评估CUX进行正式分类。本文试图分析和评估当前可用的用户体验方法论的各个方面,并为用户体验评估方法指明未来的设计改进。
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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
E. Spyrou, Theodoros Giannakopoulos, Dimitris Sgouropoulos, Michalis Papakostas
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.
人类情绪的识别在某些应用中可能是至关重要的,例如,人机交互,老年人的监测,在课程中理解学习者的情感状态等。为了实现这一目标,根据应用程序和环境,人们可以使用生理参数(例如,心率,大脑活动等),这些参数通常是突出的,或者分析通过简单观察人类可以提取的其他模式,例如视觉(例如,她/他的面部表情,手势,骨骼运动等)或音频(例如,语音)。在许多应用中,唯一可用的情态是后者,即人的声音。在这项工作中,我们的目标是通过只依赖从她/他的声音中提取的副语言信息来分析说话人的情绪,从而抛弃语言的语言方面(即口语)。为了实现这一目标,我们提出了一种受计算机视觉任务启发的新的情感分类方法。我们使用频谱图,它是音频片段频谱的可视化表示。然后,我们提取特征并使用视觉词汇表对其进行编码,并将谱图表示为“视觉词汇袋”。这种表示用于将音频片段分类为情感类。我们在包含不同语言语音的3个数据集上评估了我们的方法,并将其与基线方法进行了比较。
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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
Xuan Wu, Dong Zhou, Yu Xu, S. Lawless
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.
社会标签系统作为一种对Web资源进行注释和分类的方法已经被广泛使用。然而,用户经常使用不受限制的词汇表来标记和描述资源。相反,Web文档的注释者可能使用非常不同的单词来描述相同的概念。在过去的几年里,人们提出了许多个性化的查询扩展方法来解决词汇不匹配问题。许多方法都是基于概率技术或基于图的技术,但忽略了社会数据中存在的多关系特征。在本文中,我们从社会标签系统中探索了多种语义关系,包括标签之间、词与词之间以及标签与词之间的关系。基于标签和词的特征构建了三个关联图。此外,我们结合了从排名靠前的文档中获得的伪相关反馈信息,以正则化三个亲和图上多个关联的平滑度。本文的关键是将以上三种关联图考虑到一个新的查询扩展模型中,以产生更好的个性化搜索结果。在真实数据集上进行的实验验证了所提出方法的有效性。
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引用次数: 3
期刊
2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)
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