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A method for analysis of human temperament in contrast to social network data 一种对比社会网络数据分析人类气质的方法
Pub Date : 2022-11-07 DOI: 10.1145/3539637.3556994
Lara Mondini Martins, Cássio De Alcantara, M. Barioni, Luiz Carlos De Oliveira Júnior, E. Faria
Currently, with the growth of the use of social networks, the possibilities of studies on social relationships and interactions have grown significantly. Understanding how users express their feelings and manifest their temperaments in social networks can be a step towards anticipating psychological disorders. Instagram has billions of users and is among the most used social networks today. However, it is still little explored as a source of study for human temperament. This work aims to analyze the relationships between users’ temperament and their data collected from the social network Instagram. For the analysis of textual data, two sentiment classification strategies are proposed. The sentiment classification results were satisfactory, with accuracy above 80% in three different databases. In order to analyze the relationship between the temperaments and social network data, statistical tests are used. Each user is represented by their positive and negative captions, the use of emojis in their posts, and the number of likes in their posts. Users of the same temperament are contrasted with users of other temperaments. The results indicate that depressed users post more captions with positive sentiment than hyperthymic, angry and worried users. Anxious users have more likes than depressed, hyperthymic, angry and worried users, and finally, anxious users use more emojis in Instagram captions than depressed and angry users.
目前,随着社交网络使用的增长,研究社会关系和互动的可能性显著增加。了解用户如何在社交网络中表达他们的情感和表现他们的气质,可以成为预测心理障碍的一步。Instagram拥有数十亿用户,是当今使用最多的社交网络之一。然而,作为人类气质的研究来源,它仍然很少被探索。这项工作旨在分析用户气质与社交网络Instagram上收集的数据之间的关系。对于文本数据的分析,提出了两种情感分类策略。在三个不同的数据库中,情感分类结果令人满意,准确率在80%以上。为了分析气质与社会网络数据之间的关系,使用了统计检验。每个用户都通过他们的积极和消极的标题,在他们的帖子中使用表情符号,以及在他们的帖子中喜欢的数量来代表。具有相同气质的用户与其他气质的用户形成对比。结果表明,抑郁的用户比情绪亢进、愤怒和担忧的用户发布了更多的积极情绪的标题。焦虑的用户比抑郁、情绪亢进、愤怒和担忧的用户点赞次数更多,最后,焦虑的用户比抑郁和愤怒的用户在Instagram配文中使用更多的表情符号。
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引用次数: 0
A Learning-Based Framework for Depth Perception using Dense Light Fields 基于学习的密集光场深度感知框架
Pub Date : 2022-11-07 DOI: 10.1145/3539637.3557062
A. P. Ferrugem, B. Zatt, L. Agostini
The rapid development of optical sensors technology has accompanied a growing demand for visual measurement systems in emerging areas that need to interpret the real three-dimensional physical world, such as self-driving cars, mobile robotics, Advanced Driver Assistance Systems (ADAS), and medical diagnostic in 3D imaging. In these systems, for modeling the physical world, it is necessary to unify visual information with depth measurements. Light Field cameras have the potential to be used in such systems as a versatile hypersensor. Since Light Fields represent the scene’s visual information from multiple viewpoints, it is possible to calculate the depth information through trigonometric operations. This paper proposes a learning-based framework that allows unifying scene depth with visual information obtained from Light Fields. The structure of the proposed framework is composed of four main modules. The deep learning modules consist of (i) a depth map estimation using a siamese convolutional neural network and (ii) an instance segmentation employing region-based convolutional neural network. The others two modules apply linear transformations: (iii) a module which applies the matrix transformations with camera intrinsic parameters to generated a new depth map of absolute distances and (iv) a module to return the distance of the selected objects. For the depth map estimation module this framework proposal a siamese neural network called EPINET-FAST, which allows for generating depth maps in less than half the time of the original EPINET. A case study is presented using Dense Light Fields captured by a Lytro Illum camera (plenotic 1.0). The case study seeks to exemplify the processing time of each module, allowing researchers to isolate critical points and propose changes in the future, seeking a processing that can be applied in real time.
随着光学传感器技术的快速发展,需要解释真实三维物理世界的新兴领域对视觉测量系统的需求不断增长,例如自动驾驶汽车、移动机器人、高级驾驶辅助系统(ADAS)和3D成像中的医疗诊断。在这些系统中,为了对物理世界进行建模,有必要将视觉信息与深度测量相统一。光场相机有潜力在这种系统中被用作多功能超传感器。由于光场代表了多个视点的场景视觉信息,因此可以通过三角运算来计算深度信息。本文提出了一种基于学习的框架,可以将场景深度与从光场获得的视觉信息统一起来。提出的框架结构由四个主要模块组成。深度学习模块包括(i)使用连体卷积神经网络的深度图估计和(ii)使用基于区域的卷积神经网络的实例分割。其他两个模块应用线性变换:(iii)一个模块应用相机内部参数的矩阵变换来生成绝对距离的新深度图,(iv)一个模块返回所选对象的距离。对于深度图估计模块,该框架提出了一种称为EPINET- fast的暹罗神经网络,它可以在不到原始EPINET一半的时间内生成深度图。使用Lytro Illum相机(plenotic 1.0)捕获的密集光场进行了案例研究。案例研究旨在举例说明每个模块的处理时间,使研究人员能够隔离关键点并提出未来的更改,寻求可以实时应用的处理。
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引用次数: 0
Correlating Historical Events and Cinematic Releases Using Web Information 使用网络信息关联历史事件和电影版本
Pub Date : 2022-11-07 DOI: 10.1145/3539637.3557059
Brenno Lemos Melquiades Santos, E. Albergaria, D. Dias, A. B. Pigozzo, Leonardo Rocha
Mimesis is a term created by Aristotle and Plato in which art imitates life. Mimesis has been studied since ancient Greece and governed the theatrical and sculptural creations of the time. In this context, our work aims to study the effect of mimesis in the current cinematographic scenario, correlating historical events of the 20th and 21st centuries to the great cinematographic productions that follow. The question that guides our work is “Is there an increase in the release of films with a certain theme after a historical event?”. To answer this, we propose a methodology that uses two distinct data sources: one related to descriptions of historical facts from the 20th and 21st centuries extracted from Wikipedia and another with descriptions of films extracted from TMDb. Using topic modeling strategies, we automatically find the main themes related to historical events, and later, we evaluate how the description of a film is associated with the themes found. Temporal analysis is done to assess the popularity of each of the themes over time. In the results obtained by our methodology, there was a significant increase in the popularity of films that addressed themes related to historical events that occurred in an immediately preceding moment in time, corroborating the concept of mimesis.
模仿是由亚里士多德和柏拉图创造的一个术语,其中艺术模仿生活。自古希腊以来,模仿就一直被研究,并支配着当时的戏剧和雕塑创作。在这种背景下,我们的工作旨在研究模仿在当前电影场景中的影响,将20世纪和21世纪的历史事件与随后的伟大电影制作联系起来。指导我们工作的问题是“某一历史事件之后,某类题材电影的上映是否会增加?”为了回答这个问题,我们提出了一种使用两种不同数据源的方法:一种是从维基百科中提取的20世纪和21世纪的历史事实描述,另一种是从TMDb中提取的电影描述。使用主题建模策略,我们自动找到与历史事件相关的主题,然后,我们评估电影的描述如何与找到的主题相关联。时间分析是为了评估每个主题随时间的受欢迎程度。通过我们的方法得出的结果显示,与发生在前一刻的历史事件相关的主题电影的受欢迎程度显著增加,这证实了模仿的概念。
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引用次数: 0
A Movement Analysis Application using Human Pose Estimation and Action Correction 基于人体姿态估计和动作校正的运动分析应用
Pub Date : 2022-11-07 DOI: 10.1145/3539637.3557931
Gisela Miranda Difini, M. G. Martins, J. Barbosa
Human pose estimation (HPE) is an important field of computer vision that aims to predict poses of individuals from videos and images. It has been used in many different areas including human-computer interaction, motion analysis, surveillance, action prediction, action correction, augmented reality, virtual reality, and healthcare. Executing movements correctly is crucial in all kinds of physical activities, both to increase performance and reduce risk of injury. HPE is poised to help athletes better analyse the quality of their movements. This work proposes a model for movement analysis, repetition count, and movement correction in physical exercises using HPE. For this purpose, a study is carried out in the field of HPE applied to sports and another study is focused on HPE for correction and postural analysis. From this, it is verified what is the state of the art in HPE for physical exercises and what is the best method for analyzing movements. This work implements an application with improvements in respect to other related work, focusing mainly on the feedback presented to the user when performing a certain movement. To validate the proposed model, a quantitative research was carried out using the Unified Theory of Acceptance and Use (UTAUT). For both people who exercise and professionals in the field of physical education, the results demonstrate that the application is able to analyze the biomechanics of movement, responding with speed and precision to execution errors. Among other results are: user satisfaction, interest in using the application in the future, and agreement in relation to good performance in helping and analyzing physical exercises.
人体姿态估计(HPE)是计算机视觉的一个重要领域,旨在从视频和图像中预测个体的姿态。它已被用于许多不同的领域,包括人机交互、运动分析、监视、动作预测、动作纠正、增强现实、虚拟现实和医疗保健。在各种体育活动中,正确地执行动作是至关重要的,既可以提高表现,又可以减少受伤的风险。惠普准备帮助运动员更好地分析他们的动作质量。这项工作提出了一个模型的运动分析,重复计数和运动纠正在体育锻炼中使用HPE。为此,一项研究是HPE在运动中的应用,另一项研究是HPE在矫正和姿势分析方面的应用。由此,验证了HPE体育锻炼的现状,以及分析运动的最佳方法。这项工作实现了一个应用程序,在其他相关工作的基础上进行了改进,主要关注在执行某个动作时呈现给用户的反馈。为了验证所提出的模型,使用统一接受和使用理论(UTAUT)进行了定量研究。对于锻炼者和体育教育领域的专业人士来说,结果表明该应用程序能够分析运动的生物力学,对执行错误做出快速和精确的反应。其他结果包括:用户满意度,未来使用应用程序的兴趣,以及在帮助和分析体育锻炼方面表现良好的一致性。
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引用次数: 1
Should We Translate? Evaluating Toxicity in Online Comments when Translating from Portuguese to English 我们应该翻译吗?评估从葡萄牙语翻译成英语时在线评论的毒性
Pub Date : 2022-11-07 DOI: 10.1145/3539637.3556892
Jordan K. Kobellarz, Thiago H. Silva
Social media and online discussion platforms suffer from the prevalence of uncivil behavior, such as harassment and abuse, seeking to curb toxic comments. There are several approaches to classifying toxic comments automatically. Some of them have more resources and are more advanced in English, thus, stimulating the task of translating the text from a specific language to English. While researchers have shown evidence that this practice is indicated for certain tasks, such as sentiment analysis, little is known in the context of toxicity identification. In this research, we assess the performance of a freely available model for toxic language detection in online comments called Perspective API, widely adopted by some famous news media sites to identify different toxicity classes in online comments. For that, we obtained comments in Portuguese from two Brazilian news media websites during a politically polarized situation as a use case. Then, this dataset was translated to English and compared to four baseline datasets, two composed of highly toxic comments, one in Portuguese and other in English, and two composed of neutral comments, also one in Portuguese and other in English – all of them in its original language, not translated. Finally, human-annotated comments from the news comments dataset were analyzed to assess the scores provided by the Perspective API for the original and the translated versions. Results indicate that keeping the texts in their original language is preferable, even in comparing different languages. Nevertheless, if the translated version is strictly necessary, ways of dealing with the situation were suggested to preserve as much information as possible from the original version.
社交媒体和在线讨论平台普遍存在不文明行为,例如骚扰和辱骂,试图遏制有毒评论。有几种方法可以自动对有毒评论进行分类。他们中的一些人资源更丰富,英语水平更高,因此,激发了将文本从特定语言翻译成英语的任务。虽然研究人员已经证明这种做法适用于某些任务,如情绪分析,但在毒性鉴定方面却知之甚少。在这项研究中,我们评估了一个免费的在线评论有毒语言检测模型的性能,该模型被称为Perspective API,被一些著名的新闻媒体网站广泛采用,用于识别在线评论中的不同毒性类别。为此,我们从两家巴西新闻媒体网站获得了葡萄牙语的评论,这是在政治两极分化的情况下作为用例。然后,该数据集被翻译成英语,并与四个基线数据集进行比较,其中两个由高度有害的评论组成,一个用葡萄牙语,另一个用英语,还有两个由中立的评论组成,一个用葡萄牙语,另一个用英语——所有这些都是原始语言,没有翻译。最后,分析来自新闻评论数据集的人工注释评论,以评估Perspective API为原始版本和翻译版本提供的分数。结果表明,即使在比较不同的语言时,保持文本的原始语言也是可取的。然而,如果翻译本是绝对必要的,建议的处理办法是尽可能多地保留原文的资料。
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引用次数: 3
Visualization of brainwaves using EEG to map emotions with eye tracking to identify attention in audiovisual workpieces 利用脑电图对情绪进行可视化,并结合眼动追踪来识别视听工件中的注意力
Pub Date : 2022-11-07 DOI: 10.1145/3539637.3557055
Thiago Silva, M. Cavalcanti, Felipe Melo Feliciano De Sá, Isaac Nóbrega Marinho, Daniel De Queiroz Cavalcanti, Valdecir Becker
Abstract: This article describes a brainwave visualization system using EEG and Eye Tracking in order to map the emotional relationship of individuals with audiovisual workpieces, especially attention and taste. Using the Design Science Research method, the artifact was specified, implemented and tested with 10 subjects, using a horror movie trailer. A preliminary and a post-test questionnaire was presented to the participants. The results indicate patterns of emotional identification with the film, which can be interpreted as an inclination to watch the film in movie theaters or a repulsion to the theme/genre of the film. In conclusion, this research points to an advance in evaluation of audiovisual workpieces, contemplating unconscious emotional elements of subjective perceptions about the watched content.
摘要:本文介绍了一种基于脑电图和眼动追踪技术的脑波可视化系统,以绘制个体与视听工件之间的情感关系,特别是注意力和品味关系。使用设计科学研究方法,使用恐怖电影预告片对10个受试者进行指定、实施和测试。向参与者提供了一份初步和后测试问卷。结果显示了对电影的情感认同模式,可以解释为倾向于在电影院观看电影或排斥电影的主题/类型。总之,本研究指出了视听作品评价的一个进步,考虑了对观看内容的主观感知的无意识情感因素。
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引用次数: 0
Introducing Contextual Information in an Ensemble Recommendation System for Fashion Domains 在时尚领域集成推荐系统中引入上下文信息
Pub Date : 2022-11-07 DOI: 10.1145/3539637.3557058
Heitor Werneck, N. Silva, Carlos Mito, A. Pereira, D. Dias, E. Albergaria, Leonardo Rocha
In online marketing environments, we have seen strong growth in the fashion domain, allowing consumers to access a worldwide network of brands. Despite the significant advances of the so-called Recommender Systems in more traditional scenarios, they still fail to offer a personalized and reliable fashion shopping experience that allows customers to discover products that suit their style and products that complement their choices or challenge them with new ideas. In this work, we propose a new ensemble recommendation system that combines different context information (customer-product interaction, item characteristics and user behaviour) with the predictions (recommendations) of different state-of-the-art traditional Recommender Systems to recognize new patterns in user-item interaction and to ensure a desirable level of personalization for fashion domains. Specifically, in the present work, we present a first instantiation that combines a collaborative filtering neural network method, a non-customized classical method and domain context information. In our experimental evaluation, considering two Amazon data collections, the instantiation of our proposal presented significant gains of up to 80% of MRR, 70% of NDCG and 108% of Hits compared with the methods considered state-of-the-art for the fashion recommendation scenario.
在网络营销环境中,我们看到了时尚领域的强劲增长,消费者可以接触到全球的品牌网络。尽管所谓的“推荐系统”在更传统的场景中取得了重大进展,但它们仍然无法提供个性化和可靠的时尚购物体验,无法让顾客发现适合自己风格的产品,无法补充他们的选择,也无法用新的想法挑战他们。在这项工作中,我们提出了一个新的集成推荐系统,该系统将不同的上下文信息(客户-产品交互、商品特征和用户行为)与不同的最先进的传统推荐系统的预测(推荐)相结合,以识别用户-商品交互中的新模式,并确保时尚领域的理想个性化水平。具体而言,在本工作中,我们提出了结合协同过滤神经网络方法、非自定义经典方法和领域上下文信息的第一个实例。在我们的实验评估中,考虑到两个亚马逊的数据集合,与被认为是最先进的时尚推荐场景的方法相比,我们建议的实例化呈现出高达80%的MRR, 70%的NDCG和108%的点击率的显著提高。
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引用次数: 0
Context injection in expert finding 专家查找中的上下文注入
Pub Date : 2022-11-07 DOI: 10.1145/3539637.3556995
Rodrigo Gonçalves, C. Dorneles
Expert finding is a subject of research in information retrieval and, often, is taken to mean expertise retrieval within a specific organization. The task involves finding an expert on a given topic of interest. Even though there are several proposals in the literature, they do not consider the context in which the given expertise is bound. This paper introduces an approach to inject context into existing expertise evidence based on data extracted from the evidence. Our motivation is to provide context when describing the expertise associated with a candidate expert, allowing a user to understand the results better and choose the best candidate for the task.
专家发现是信息检索中的一个研究课题,通常被认为是指特定组织内的专业知识检索。这项任务包括找到一个对特定话题感兴趣的专家。尽管文献中有几个建议,但它们没有考虑到给定的专业知识所处的背景。本文介绍了一种基于从证据中提取的数据为现有专家证据注入背景的方法。我们的动机是在描述与候选专家相关的专业知识时提供上下文,允许用户更好地理解结果并为任务选择最佳候选人。
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引用次数: 0
“Click Here to Join”: A Large-Scale Analysis of Topics Discussed by Brazilian Public Groups on WhatsApp “点击这里加入”:对WhatsApp上巴西公共团体讨论的话题进行大规模分析
Pub Date : 2022-11-07 DOI: 10.1145/3539637.3557056
Daniel Kansaon, P. Melo, Fabrício Benevenuto
WhatsApp has many similarities with online social networks, as it allows connections between multiple people and massive communication by sharing content with your contacts and public groups, which brings people together to discuss a topic. Even though it is one of the most popular social media in the world, there is a lack of a systematic understanding of the Whatsapp ecosystem, especially when it comes to knowing the subjects discussed in public groups and how other users find/join those groups. In this direction, our goal is to investigate how public groups are shared on the Web and also map the main topics existing within this ecosystem. For this, we perform a large-scale collection, spanning four main sources on the Web for sharing groups, with more than 270k WhatsApp public groups, categorizing and analyzing this environment. Our results shed light on a large existence of groups focused on topics such as friendship, pop culture, stickers, sales, jobs, education, and even adult content suggesting the many uses of the WhatsApp tool. We also found key differences in groups according to the source where it was posted. Moreover, we discovered how group links work to persuade users from other platforms into the underground environment of WhatsApp. Malicious groups abuse its closed architecture and low moderation for illicit practices such as selling fake money and cloned cards. Furthermore, our analysis also found evidence of automated behavior in malicious group sharing. Finally, we discuss implications and measures that can be taken to address these issues.
WhatsApp与在线社交网络有很多相似之处,因为它允许多人之间建立联系,通过与你的联系人和公共群组分享内容来进行大规模通信,这将人们聚集在一起讨论一个话题。尽管它是世界上最受欢迎的社交媒体之一,但人们对Whatsapp的生态系统缺乏系统的了解,尤其是在了解公共群中讨论的主题以及其他用户如何找到/加入这些群时。在这个方向上,我们的目标是研究公共群组是如何在Web上共享的,并绘制出这个生态系统中存在的主要主题。为此,我们进行了大规模的收集,跨越网络上四个主要来源的共享组,超过27万个WhatsApp公共组,对这个环境进行分类和分析。我们的研究结果揭示了大量群组的存在,这些群组关注的主题包括友谊、流行文化、贴纸、销售、工作、教育,甚至成人内容,这表明WhatsApp工具的用途很多。我们还发现,根据发布消息的来源,不同群体之间存在关键差异。此外,我们还发现了群链接如何说服其他平台的用户进入WhatsApp的地下环境。恶意组织利用其封闭的结构和低节制进行非法活动,如出售假币和克隆卡。此外,我们的分析还发现了恶意群组共享中自动行为的证据。最后,我们讨论了解决这些问题的影响和措施。
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引用次数: 0
A conceptual model for autonomic relationships in the Social Internet of Things 社交物联网中自主关系的概念模型
Pub Date : 2022-11-07 DOI: 10.1145/3539637.3557928
Leandro Camargo, A. Pernas, A. Yamin
Miniaturization of computing devices allows everyday physical objects to communicate on a network through various ubiquitous media. In this scenario, smart things can socially interact autonomously with each other. In order to make this scenario a reality, this work presents the conception of a Conceptual Model for the dynamic management of the relationships established between intelligent objects, thus contributing to the consolidation of the Social Internet of Things. We designed a friendship recommendation and social circle composition approach that monitors interaction and highlights the most relevant objects for fulfilling requests. Additional contributions are: (i) simplification of the interaction process between objects; (ii) timely indication of friendship; and (iii) a structuring proposal for the social circle. The Link Analysis technique is the basis of the model implemented to identify connections between individuals and qualify interactions. In addition, the composition of the social circle is guided by the theory of social networks of the Organizational Network Analysis methodology, aiming to simplify indications of new friendships. Results of partial evaluations demonstrated the potential of the implemented model to correctly classify the interactions and roles of objects defined in the relationships. This validation leads to planned future work and the improvement of the implemented model.
计算设备的小型化使得日常的物理对象可以通过各种无处不在的媒介在网络上进行通信。在这种情况下,智能设备可以相互自主地进行社交互动。为了使这一场景成为现实,这项工作提出了一个概念模型的概念,用于动态管理智能对象之间建立的关系,从而有助于巩固社会物联网。我们设计了一个朋友推荐和社交圈组成的方法来监控互动,并突出显示最相关的对象来满足请求。其他贡献有:(i)简化了对象之间的交互过程;(二)及时表示友好;(三)社交圈构建方案。链接分析技术是实现模型的基础,用于识别个体之间的连接并限定交互。此外,社交圈的构成以组织网络分析方法论的社会网络理论为指导,旨在简化新友谊的迹象。部分评估的结果证明了实现模型正确分类关系中定义的对象的交互和角色的潜力。此验证将导致计划的未来工作和已实现模型的改进。
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引用次数: 2
期刊
Proceedings of the Brazilian Symposium on Multimedia and the Web
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