Brazil

Face recognition systems are present in many modern solutions and thousands of applications in our daily lives. However, current solutions are not easily scalable, especially when it comes to the addition of new targeted people. We propose a cluster-matching-based approach for face recognition in video. In our approach, we use unsupervised learning to cluster the faces present in both the dataset and targeted videos selected for face recognition. Moreover, we design a cluster matching heuristic to associate clusters in both sets that is also capable of identifying when a face belongs to a non-registered person. Our method has achieved a recall of 99.435% and a precision of 99.131% in the task of video face recognition. Besides performing face recognition, it can also be used to determine the video segments where each person is present.

WebMedia 2020

A Cluster-Matching-Based Method for Video Face Recognition

clustering.

face recognition

deep learning

### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year!<br><br>

**Your Program Chairs,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

technical paper

Promoted by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia and Web (WebMedia) is the main event of the theme in Brazil and an excellent opportunity for scientific and technical exchange among students, researchers and professionals in the areas of Multimedia, Hypermedia and Web. This year we will be celebrating to 26th edition of WebMedia, which will be held online from november 30th to December 4th .

Webmedia 2020’s programming includes:

– Technical sessions for the presentation of scientific papers
– National and international keynotes
– Panel with companies
– Workshops
– Short Courses
– Open community meetings

The topics of interest for submitting papers are:

– Web and Social Networks
– Multimedia
– Digital TV, Ubiquitous and Mobile Computing

Besides the main track, WebMedia 2020 will have in parallel the Tools and Applications Workshop, works Tools and Applications Workshop, the Workshop on Undergraduate Research Work and the Workshop The Future of Interactive.

The WebMedia 2020 organization is under the responsibility of the Programa de Pós-graduação Doutorado em Ciência da Computação – Associação UFMA/UFPI (DCCMAPI), do Programa de Pós-graduação em Ciência da Computação/ CCET (PPGCC), do Laboratory of Advanced Web Systems/UFMA (LAWS) e do Laboratório de Sistemas Multimídia/UFMA (TELEMÍDIA).).

#PartiuWebMedia2020 #WebMedia2020

Promoted by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia and Web (WebMedia) is the main event of the theme in Brazil and an excellent opportunity for scientific and technical exchange among students, researchers and professionals in the areas of Multimedia, Hypermedia and Web.

A major concern when building Recommender Systems (RSs) for recruitment domains is ensuring actual matching opportunities for all candidates and job vacancies on the system as fast as possible. Indeed, long periods waiting for matchings imply losses of business opportunities, causing financial damages for both candidates and companies. We refer to these scenarios where candidates or job vacancies suffer from the absence of matching opportunities as the Matching Scarcity Problem (MaSP). First, we formalize the MaSP itself. Second, we proposed and evaluated strategies to identify automatically candidates and job vacancies suffering from MaSP. Finally, we proposed five heuristic strategies to mitigate MaSP. Our strategies consist of introducing changes in curricula and job descriptions to approximate candidates suffering from MaSP to jobs semantically related to them, and vice versa. The best strategy was able to reduce up to 50% the number of both CVs and jobs suffering from MaSP before applying it in our sample.

Mitigating Matching Scarcity in Recruitment Recommendation Domains

About 50 million people around the world have Alzheimer's disease. The main characteristic of the disease is the deterioration of cognitive function, and as a result of the fact that the disease has no cure or treatment. This article presents a systematic mapping of the literature to identify articles that use technological devices to assist patients with Alzheimer's disease, in order to show an overview of the literature, identifying gaps and research opportunities. The systematic mapping study was conducted out in six databases, including articles from January 2015 to July 2020. An initial search resulted in 12,177 articles, of which 28 were selected after applying the filter criteria. The main results obtained are: (1) most articles (25.71%, 7/28) reported solutions used in devices with sensor development; (2) most technologies (22.06%, 6/28) were focused on monitoring; (3) most papers (82.14%, 23/28) address solutions for dementia, citing Alzheimer's disease indirectly; (4) more than half of the studies aimed at monitoring patients (54%, 15/28); (5) most studies (82.14%, 23/28) performed an experiment with patients with the disease. The results corroborate the need for technological devices to help caregivers monitor patients, in addition to evidence of research opportunities in palliative care and support for daily activities.

Technologies applied in the care of patients with Alzheimer’s disease: a systematic review

The popularization of Location-based social networks (LBSNs) in last years has provided a lot of improvements in several Recommender Systems to the task of points-of-interest (PoI) recommendation. In this paper, we provide an updated view of the PoI recommendation, identifying relevant efforts, results, contributions, and limitations. Through a systematic mapping, we selected 73 relevant papers published in the last three years (2017, 2018, and 2019) in the main vehicles of the area (e.g., RecSys, VLDB, SIGIR, WWW, TKDE, etc). As major limitations, first, we identified that these works prioritize accuracy over other quality dimensions, despite the consensus in the RS community that accuracy is not enough to assess the practical effectiveness of RSs. Further, we found a low intersection of metrics and datasets used in these works, along with a large number of metrics used in a few distinct studies. These observations show restrictions for reproducibility and straightforward comparison of results in the area. Finally, we highlight as a promising future work the in-depth exploitation of textual data, since just a few of the evaluated papers marginally use this rich data source.

A Survey on Point-of-Interest Recommendation in Location-based Social Networks

The current advances have recognized the online recommendation task as a Multi-Armed Bandit (MAB) problem and proposed several algorithms by combining it with Collaborative Filtering (CF). In general, their effectiveness is related to their practical ability to handle the trade-off between selecting the most relevant item (exploitation) or choose an unexpected item to add more knowledge to the system (exploration). However, we observed that a naive failure has been introduced in the main CF bandit models, which have intrinsically reduced itself to pure exploitation of the information known a prior by the system (e.g., items’ popularity) or a pure exploration of the items in the first user’s interaction. Conversely, we hypothesize this trade-off should be considered since the first user’s interactions to improve the system’s effectiveness. Thus, we propose a new methodology to measure the impact of this problem. This methodology has two main phases. In phase 1, traditional non-personalized methods resemble pure-exploitation and pure-exploration to feed the learning of all MAB models. Then, analyzing the MAB effectiveness in phase 2, we may determine the best approach to be considered in the first user’s interactions. Moreover, we also propose and add a new strategy that combines exploration and exploitation in phase 1. Indeed, our experimental results show that the combination of exp-exp at first user’s interactions improves the system’s effectiveness without adding more effort to users.

The impact of first recommendations based on exploration or exploitation approaches in recommender systems’ learning

Location-Based Social Networks (LBSNs) have become important tools for people interested in exploring new places. And, similar to traditional recommendation domains, handling the trade-off between accuracy and diversity is a major challenge to provide useful recommendations. However, this domain adds an equally relevant dimension to this challenge: the geographical distance between users and each point-of-interest (POI). Besides understanding how the characteristics of services offered by each POI fit the user needs, realizing how far users are willing to move to fulfill these needs is of paramount relevance. Moreover, the users present distinct levels of interest in diversification. In this paper, we propose a strategy to provide POI recommendations linearly combining categorical and geographical diversifications in a personalized way. Indeed, our strategy is able to prioritize quality dimensions that better suit the personalized needs of each user, with gains up to 10% when compared with unpersonalized versions.

Combining complementary diversification models for personalized POI recommendations

More than just a common habit or pastime of people, listening to music is often used as a way to facilitate listeners to achieve a certain mental state or to perform specific activities. Among the most common activities performed when listening to music is relaxation. According to findings from the area of music therapy, a good musical choice for relaxation needs personalization. However, until now, many studies in the area of Music Information Retrieval (MIR) have focused on only identifying general characteristics of music according to certain contexts. Thus, this paper focuses on expanding the findings of music therapy, considering aspects of musicology and a large amount of data. To this end, we: (i) explore more than 60 thousand handcrafted playlists of almost 5 thousand users; (ii) create a user preference model based on high and low-level features of the songs and (iii) analyze the differences between the users' preferences. The results of our analysis suggest that personalization plays a key role in the playlists created by the users. With this work, we want to pave the way and encourage the improvement of context-aware music recommender systems or other MIR concepts like user behavior analysis.

Investigating the Role of Personalization When Creating Relaxing Playlists

Currently, videos play an important role in people's every day lives and there is a continuous increase in their production and consumption. However, to make video information access a feasible task, smarter and more efficient strategies are needed in order to deal with this complex and huge data volume. In this context, automatic video summarization becomes a significant tool. This research area aims to produce a smaller version of original video, keeping its most meaningful information. Particularly for some relavant domains, like news and education, it is important a summary to be generated from a set of related videos - different videos about the same subject. In the automatic multi-video summarization field a common and essential step is to define content selection criteria. A promising approach, yet unexploited by related work, is to apply inclusion and exclusion criteria derived from human strategies. In this work, we investigate how exclusion criteria can help to keep multi-video summaries short and informative. We introduce an approach to detect subjectivity into video segments from news domain, allowing those segments to be not included into the summary. Our approach is evaluated by the means of objective measures of effectiveness. We also analyze the impact that removing such subjective segments has on multi-video summarization outputs by conducting a study with final users. Results indicate that the proposed approach is able of producing more compact summaries, keeping the relevant content for the users.

Investigating Subjectivity Criterion for Multi-video Summarization

The recent availability of HMD devices (Head-Mounted Display) spurred advances in Virtual Reality and Augmented Reality. It allowed scenarios of immersion of users in realistic environments, e.g., synthesized 3D environments for games and simulation, and omnidirectional videos, namely 360° videos. These HMD devices are commonly used in conjunction with home computers or in controlled environments (e.g., museums). However, despite these scenarios, current interactive TV systems still focus on using interactive content in two-dimensional layouts. These systems could use HMDs to provide immersive experiences to viewers. This paper, therefore, presents Guaraná, a player for interactive 360° scenes using HMDs as second screen devices. Guaraná controls a 360 scene execution based on external control from a TV set. Thus, it enables its use in conjunction with an intereactive multimedia application.

Interactive 360-degree Videos in Ginga-NCL UsingHead-Mounted-Displays as Second Screen Devices

Smart city services are typically defined according to domains (e.g., health, education, safety) and supported by different systems. Consequently, the analysis of smart city data is often domain-specific, thus limiting the capabilities of the offered services and hampering decision-making that relies on isolated domain information. To support a suitable analysis across multiple domains, it is necessary having a unified data model able to handle the inherent heterogeneity of smart city data and take into account both geographic and citizen information. This presentation is related to an ontology-based information model to support multi-domain analysis in smart cities to foster interoperability and powerful automated reasoning upon unambiguous information. The semantic relationships and properties defined in the model also allow inferring new pieces of information that improve accuracy when analyzing multiple city domains. An evaluation of the information model through ontological metrics and competence questions also will be presented.

An Ontology-based Information Model for Multi-Domain Semantic Modeling and Analysis of Smart City Data

On social media platforms, comments associated with news pieces are usually filled with negativity and toxicity, many times promoting flamed discussions and insults among users. Although designed to encourage conversations and interactions, the high toxicity might end up contributing to create a hostile environment in the online space, which is detrimental to both social media platforms and their users. In this work, we provide a diagnostic about the toxicity in comments associated with news shared on Facebook. To do that, we collected all posts and comments from relevant pages during a major political event in Brazil, the release of Former President Lula from prison. We then used the Perspective API from Google to measure the toxicity of the comments and posts. Our analysis of the toxicity unveils features that influence toxicity associated with the news, especially in relation to public figures. We hope our findings may affect the design of better content policies able to mitigate the problem.

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Mitigating Matching Scarcity in Recruitment Recommendation Domains

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How Face Mask in COVID-19 Pandemic Disrupts Face Learning and Recognition in Adults with Autism Spectrum Disorder?

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Next from WebMedia 2020

Mitigating Matching Scarcity in Recruitment Recommendation Domains

Similar lecture

How Face Mask in COVID-19 Pandemic Disrupts Face Learning and Recognition in Adults with Autism Spectrum Disorder?

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads