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An Ontological Model and Services for Capturing and Tracking Provenance in Decentralized Social Networks 在分散的社会网络中捕获和跟踪来源的本体模型和服务
Pub Date : 2021-09-27 DOI: 10.1145/3470482.3479637
Cíntia Souza, José Ronaldo Júnior, Cássio V. S. Prazeres
The rise of Decentralized Online Social Networks (DOSNs) and the increase in the number of active users on these networks offer an opportunity to develop solutions related to verifying origin, description paths and indicating the trajectory of the data that traffic in these networks. Provenance information is a key aspect of social networks because it is possible to evaluate the authenticity, reliability, and relevance of the information through its results. The speed of information generation and sharing, the decentralized storage strategy associated with the large volume of data represents a challenge for data provenance. Thus, this paper proposes DOSNPROV, a data provenance ontological model based on the W3C PROV-O specification. In addition, this paper proposes services based on DOSN-PROV model to support capture and tracking of provenance information in DOSNs. We evaluated DOSN-PROV model in two stages and demonstrated its quality and compliance with the proposed domain. The services underwent an evaluation of their performance and their results indicated acceptable response times.
分散式在线社交网络(dosn)的兴起以及这些网络上活跃用户数量的增加为开发与验证来源、描述路径和指示这些网络中流量数据轨迹相关的解决方案提供了机会。来源信息是社交网络的一个关键方面,因为它可以通过其结果评估信息的真实性、可靠性和相关性。信息生成和共享的速度以及与大量数据相关的分散存储策略对数据来源提出了挑战。为此,本文提出了基于W3C provo规范的数据来源本体模型dosnprove。此外,本文还提出了基于dosn - proof模型的服务,以支持dosn中来源信息的捕获和跟踪。我们分两个阶段对DOSN-PROV模型进行了评估,并证明了其质量和符合所提出的领域。对这些服务的性能进行了评估,评估结果表明了可接受的响应时间。
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引用次数: 0
Extracting Textual Features from Video Streaming Services Publications to Predict their Popularity 从视频流媒体服务出版物中提取文本特征以预测其受欢迎程度
Pub Date : 2021-09-27 DOI: 10.1145/3470482.3479624
Sidney Loyola de Sá, A. Paes, Antonio A. de A. Rocha
The Internet's popularization has increased the amount of content produced and consumed on the Web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged focusing on video streaming services. However, despite the large number and diversity of videos made available by these content providers, few of them attract most users' attention. For example, in the data explored in this paper, only 6% of the most popular videos are responsible for 85% of the total views. Finding out in advance which videos will be popular is not trivial, specially because of the large amount of influencing variables. Nevertheless, a tool with this ability would be of great value to help dimensioning network infrastructure and to properly recommend new content to users. In this work, we propose two approaches to obtaining features to classify the popularity of a video before it is published. The first one builds upon predictive attributes defined by feature engineering. The second leverages word embeddings from the descriptions and titles of the videos. We experiment with the proposed approaches on a set of videos from GloboPlay, the largest provider of video streaming services in Latin America. A combination of both engineered features and the embeddings using Random Forest machine learning algorithm reached the best result, with an accuracy of 87%.
互联网的普及增加了网络上生产和消费的内容数量。为了利用这个新市场,Netflix和亚马逊Prime等主要内容生产商纷纷涌现,专注于视频流媒体服务。然而,尽管这些内容提供商提供的视频数量众多,种类繁多,但很少有视频能吸引大多数用户的注意力。例如,在本文探索的数据中,只有6%的最受欢迎的视频占总观看量的85%。提前发现哪些视频会受欢迎是很重要的,特别是因为有大量的影响变量。尽管如此,具有这种能力的工具对于帮助划分网络基础设施的维度和向用户正确推荐新内容将非常有价值。在这项工作中,我们提出了两种方法来获取特征,以便在视频发布之前对其进行分类。第一种基于特征工程定义的预测属性。第二种方法利用视频描述和标题中的词嵌入。我们在拉丁美洲最大的视频流媒体服务提供商GloboPlay的一组视频上实验了所提出的方法。结合工程特征和使用随机森林机器学习算法的嵌入达到了最好的结果,准确率为87%。
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引用次数: 0
Pattern Identification of Bot Messages for Media Literacy 媒介素养中Bot信息的模式识别
Pub Date : 2021-09-27 DOI: 10.1145/3470482.3479452
Eric Ferreira dos Santos, Danilo S. Carvalho, Jonice Oliveira
The massive use of online social media is a reality nowadays. Such an increasing usage also raises growth in malicious activities in social media, one of which is the use of automated users (bots) that disseminate false information and can insert bias in analyses done on gathered social media data. Based on the concept of media literacy, this research presents a method to teach the human user to identify a pattern of a text produced by a bot, providing a tool (guide) to analyze social media text. Users who learned to identify a bot user with the guide had an average of 90% accuracy in the classification of new messages, against 57% of the participants who had no contact with the guide. The produced guide received a usefulness rating between 4 and 5 by the participants (scale from 1 to 5, with 5 being the highest value).
如今,在线社交媒体的大量使用已成为现实。这种不断增加的使用也增加了社交媒体中恶意活动的增长,其中之一是使用自动用户(机器人)传播虚假信息,并可以在对收集的社交媒体数据进行的分析中插入偏见。基于媒介素养的概念,本研究提出了一种教人类用户识别机器人生成的文本模式的方法,为分析社交媒体文本提供了一种工具(指南)。学会用指南识别机器人用户的用户在新信息分类方面的平均准确率为90%,而没有与指南接触的参与者的准确率为57%。制作的指南得到了参与者4到5之间的有用性评级(从1到5,5是最高的值)。
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引用次数: 0
An Approach for Automatic Description of Characters for Blind People 一种面向盲人的汉字自动描述方法
Pub Date : 2021-09-27 DOI: 10.1145/3470482.3479617
Itamar Rocha Filho, Felipe Honorato, J. W. Lucena, J. P. Teixeira, T. Araújo
Audio Description (AD) or Video Description is a vital accessibility concept in blind and visually impaired people's life. Automating this task is not easy and involves many problems, such as describing the scenario, actions, emotions, and characters. This paper presents an approach to automatically describe characters --- in a video or image --- combining Deep Learning (DL), Face detection, Facial Expression detection techniques, and audio synthesizers. Our proposal uses the detection tools, applies some DL models to the analyzed data, and generates an audio description. To evaluate the feasibility of our proposal, we have developed a proof of concept of the solution and performed some computational experiments to evaluate it.
音频描述(AD)或视频描述是盲人和视障人士生活中重要的可及性概念。自动化这项任务并不容易,涉及许多问题,例如描述场景、动作、情感和角色。本文提出了一种自动描述视频或图像中的字符的方法,该方法结合了深度学习(DL)、人脸检测、面部表情检测技术和音频合成器。我们的建议使用检测工具,将一些深度学习模型应用于分析的数据,并生成音频描述。为了评估我们的建议的可行性,我们开发了一个解决方案的概念证明,并进行了一些计算实验来评估它。
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引用次数: 2
@grogest_Ambiental: A Web-based Decision Support System for agribusiness 基于web的农业综合企业决策支持系统
Pub Date : 2021-09-27 DOI: 10.1145/3470482.3479612
Thiago Feijó, J. M. David, Regina M. M. Braga, M. Otenio, V. R. Paula, Gabriele Medeiros Santos, Fernanda Campos, Victor Ströele
In agribusiness, the treatment and sustainable management of waste in intensive production systems must consider productivity and economic gains in the short term and the sustainability of agricultural production. The acquisition of technical information, and the knowledge about the characteristics of the rural property, are fundamental for stakeholders, especially considering the adequacy of sustainable environmental practices. It is necessary to provide a web-based system to support stakeholders in discovering and understanding their productive context. This paper proposes a solution to support stakeholders' decision-making to improve sustainable practices in agribusiness. The proposed solution is applied in the agricultural domain, focusing on sustainability, by implementing a web system supported by an ontology to organize and provide strategic information through a mobile app. Technical and regulatory environmental practices documents are recommended for a relevant context, considering a feasibility study with experts in the field. As a result, the approach provided decision-making support offering technical information efficiently and agile for stakeholders.
在农业综合企业中,集约化生产系统中废物的处理和可持续管理必须考虑到短期内的生产力和经济收益以及农业生产的可持续性。获取技术信息和对农村财产特征的了解对利益相关者来说是至关重要的,特别是考虑到可持续环境实践的充分性。有必要提供一个基于网络的系统来支持利益相关者发现和理解他们的生产环境。本文提出了一个解决方案,以支持利益相关者的决策,以改善农业综合企业的可持续实践。提出的解决方案应用于农业领域,重点是可持续性,通过实施一个由本体支持的web系统,通过移动应用程序组织和提供战略信息。考虑到与该领域专家的可行性研究,建议在相关背景下使用技术和监管环境实践文件。因此,该方法提供了决策支持,为利益相关者提供高效和敏捷的技术信息。
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引用次数: 1
A Cluster-Based Method for Action Segmentation Using Spatio-Temporal and Positional Encoded Embeddings 基于聚类的时空和位置编码嵌入动作分割方法
Pub Date : 2021-09-27 DOI: 10.1145/3470482.3479632
Guilherme de A. P. Marques, A. Busson, Alan Livio Vasconcelos Guedes, S. Colcher
A crucial task to overall video understanding is the recognition and localisation in time of different actions or events that are present along the scenes. To address this problem, action segmentation must be achieved. Action segmentation consists of temporally segmenting a video by labeling each frame with a specific action. In this work, we propose a novel action segmentation method that requires no prior video analysis and no annotated data. Our method involves extracting spatio-temporal features from videos in samples of 0.5s using a pre-trained deep network. Data is then transformed using a positional encoder and finally a clustering algorithm is applied with the use of a silhouette score to find the optimal number of clusters where each cluster presumably corresponds to a different single and distinguishable action. In experiments, we show that our method produces competitive results on Breakfast and Inria Instructional Videos dataset benchmarks.
整体视频理解的一个关键任务是对场景中出现的不同动作或事件的识别和定位。为了解决这个问题,必须实现行动分割。动作分割是通过在每一帧上标记一个特定的动作来对视频进行暂时分割。在这项工作中,我们提出了一种新的动作分割方法,不需要事先的视频分析和注释数据。我们的方法包括使用预训练的深度网络从0.5s样本的视频中提取时空特征。然后使用位置编码器转换数据,最后应用聚类算法,使用轮廓分数来找到最佳数量的聚类,其中每个聚类可能对应于不同的单一可区分的动作。在实验中,我们证明了我们的方法在Breakfast和Inria教学视频数据集基准上产生了具有竞争力的结果。
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引用次数: 0
Quality Enhancement of Highly Degraded Music Using Deep Learning-Based Prediction Models for Lost Frequencies 使用基于深度学习的丢失频率预测模型来提高高度退化音乐的质量
Pub Date : 2021-09-27 DOI: 10.1145/3470482.3479635
A. Serra, A. Busson, Alan Livio Vasconcelos Guedes, S. Colcher
Audio quality degradation can have many causes. For musical applications, this fragmentation may lead to highly unpleasant experiences. Restoration algorithms may be employed to reconstruct missing parts of the audio in a similar way as for image reconstruction --- in an approach called audio inpainting. Current state-of-the art methods for audio inpainting cover limited scenarios, with well-defined gap windows and little variety of musical genres. In this work, we propose a Deep-Learning-based (DL-based) method for audio inpainting accompanied by a dataset with random fragmentation conditions that approximate real impairment situations. The dataset was collected using tracks from different music genres to provide a good signal variability. Our best model improved the quality of all musical genres, obtaining an average of 12.9 dB of PSNR, although it worked better for musical genres in which acoustic instruments are predominant.
音频质量下降的原因有很多。对于音乐应用程序,这种分裂可能会导致非常不愉快的体验。恢复算法可以用来重建音频的缺失部分,以类似于图像重建的方式-在一种称为音频修复的方法中。目前最先进的音频绘制方法覆盖了有限的场景,具有明确的间隙窗口和很少的音乐类型。在这项工作中,我们提出了一种基于深度学习(dl)的音频修复方法,该方法伴随着一个具有近似真实损伤情况的随机碎片条件的数据集。数据集是使用来自不同音乐流派的曲目收集的,以提供良好的信号可变性。我们的最佳模型提高了所有音乐类型的质量,平均获得12.9 dB的PSNR,尽管它在原声乐器占主导地位的音乐类型中效果更好。
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引用次数: 2
Sensory Effect Extraction for 360° Media Content 360°媒体内容的感官效果提取
Pub Date : 2021-09-27 DOI: 10.1145/3470482.3479620
R. Abreu, J. Santos, D. Muchaluat-Saade
The presentation of sensory effects in sync with 360° content has the potential to increase user immersion. However, the authoring process for such effects is laborious and slow. It requires the author to specify in time and space the presentation characteristics of effects. This paper contributes to address this need with a generic architecture for inferring the presentation characteristics of sensory effects in 360° content. The proposed architecture decouples and eases the adaptation of multimedia content analysis techniques for recognizing sensory effects. We highlight the original contribution of this work of presenting the automatic recognition of sensory effects location in 360° content.
与360°内容同步呈现的感官效果有可能增加用户的沉浸感。然而,这种效果的创作过程既费力又缓慢。它要求作者在时间和空间上明确效果的呈现特征。本文通过一个通用架构来推断360°内容中感官效果的呈现特征,从而有助于解决这一需求。所提出的架构解耦并简化了多媒体内容分析技术在识别感官效果方面的适应性。我们强调了这项工作的原始贡献,即在360°内容中呈现感官效果位置的自动识别。
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引用次数: 1
Ingredient Substitute Recommendation Based on Collaborative Filtering and Recipe Context for Automatic Allergy-Safe Recipe Generation 基于协同过滤和配方上下文的自动过敏安全配方生成的成分替代推荐
Pub Date : 2021-09-27 DOI: 10.1145/3470482.3479622
L. Pacífico, Larissa F. S. Britto, Teresa B Ludermir
Recipe sharing websites have become even more popular in the past few decades, and such repositories are able to keep hundreds of thousands of cooking recipes at the same time. Many recipe websites are developed with the participation of their community of users, which are allowed to upload new recipes and to provide evaluations and comments on the available recipes. However, in such repositories, the amount of recipes that are safe for users with special needs, such as food restrictions or allergies, is much smaller than ordinary food recipes, what may restrict the access and usability provided by such websites to that public. In this work, we propose a new recipe recommendation and generation system, based on a data-driven approach for single ingredient substitution, in such a way that recipes containing forbidden ingredients, according to a category of user food restrictions, are adapted by replacing such ingredients by safe ingredients. The proposed ingredient substitute recommendation system is based on a filtering process that takes into consideration the original recipe context, the relationship among sets of ingredients and the user preferences, towards the generation of recipes that are safe, and at the same time contemplate both user needs and tastes. The proposed system is evaluated by means of a qualitative analysis, showing promising results.
在过去的几十年里,食谱分享网站变得更加流行,这样的存储库可以同时保存成千上万的烹饪食谱。许多食谱网站都是在用户社区的参与下开发的,用户可以上传新的食谱,并对现有的食谱进行评估和评论。然而,在这样的存储库中,对于有特殊需求(如食物限制或过敏)的用户来说安全的食谱数量比普通的食物食谱要少得多,这可能会限制此类网站向公众提供的访问和可用性。在这项工作中,我们提出了一种新的食谱推荐和生成系统,基于数据驱动的单一成分替代方法,通过这种方式,根据用户食品限制的类别,通过将含有禁用成分的食谱替换为安全成分来适应这些成分。所提出的配料替代品推荐系统基于一个过滤过程,该过程考虑了原始配方上下文、配料集之间的关系和用户偏好,以生成安全的配方,同时考虑了用户的需求和口味。采用定性分析的方法对所提出的系统进行了评价,显示出令人满意的结果。
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引用次数: 3
Semantically Time Tracking of Events from Web Documents 从Web文档事件的语义时间跟踪
Pub Date : 2021-09-27 DOI: 10.1145/3470482.3479627
Welton Santos, E. Fazzion, D. Dias, M. Guimarães, Elisa Tuler, L. Rocha
Exploring large news collections created by media outlets with traditional search engines is impractical for demanding users. We propose a temporal exploration tool that aims to facilitate the consultation of news collections. We concentrated our efforts on two fronts: (i) allowing users to make queries with the addition of information from documents represented by word embbedings, and; (ii) retrieving temporal information to generate timelines presented by an appropriate interface. We evaluated our solution in a collection of a Brazilian newspaper, demonstrating that it can draw different timelines, covering different subtopics of the same theme.
对于要求苛刻的用户来说,用传统搜索引擎搜索媒体机构创建的大型新闻集合是不切实际的。我们提出了一个时间探索工具,旨在促进新闻集合的咨询。我们把精力集中在两个方面:(i)允许用户通过添加单词嵌入表示的文档中的信息来进行查询;(ii)检索时间信息,生成由适当接口呈现的时间轴。我们在一份巴西报纸的合集中评估了我们的解决方案,证明它可以绘制不同的时间线,覆盖同一主题的不同子主题。
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引用次数: 0
期刊
Proceedings of the Brazilian Symposium on Multimedia and the Web
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