Applicability of Similarity Coefficients in Social Circle Matching

Korepanova, A. A.; Oliseenko, Valerii D.; Abramov, Maxim V.

doi:10.1109/scm50615.2020.9198782

Cited by 14 publications

(3 citation statements)

References 5 publications

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“…It is a coefficient related to the Jaccard Index [Vijaymeena and Kavitha, 2016], which summarizes the similarity between two sets of elements based on the smallest of them 7 . Korepanova et al [2020], upon the analysis of six different binary coefficients that verify the similarity between social circles, concluded that the Szymkiewicz-Simpson Coefficient produces better results. This coefficient is given by Equation (1).…”

Section: Szymkiewicz-simpson Coefficientmentioning

confidence: 99%

An Analysis of the Authorship and Co-authorship Networks of the Brazilian Human-Computer Interaction Conference

Lima,

Miranda,

Vasiljevic

et al. 2024

JIS

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In Brazil, the Brazilian Symposium on Human Factors in Computing Systems (IHC) gathers the scientific community of researchers interested in the field of Human-Computer Interaction since 1998, being the main Brazilian event in this sub-area of Computing. Over twenty-one editions, the IHC received works from researchers from different regions of the country who, over the years, have been building their own co-authorship relationships with the other authors of the Symposium. In this context, this paper analysed the IHC from the perspective of those who helped to consolidate this important national scientific event, as well as in the expansion of the Human-Computer Interaction area in the Brazilian scenario, that is, its researchers-authors. In total, 1,443 authors were identified and analysed in the study presented in this work, which considered 873 publications of three IHC tracks: Full Papers, Short Papers, and Innovative Ideas and Emerging Results. Issues related to the publications and to the co-authorship relationships of these authors over the years and in the different article tracks of the IHC were considered. In order to describe their research trajectories within the IHC itself, the study presents, in different scales of time, how these authors evolved in relation to their contributions over time. In addition, this paper analyses how the authors contributed with each other and originated the complex collaboration network of the IHC. For this, co-authorship networks and groups of authors who published together were explored, aiming to clarify the collaborations between these authors, as well as how they evolved until the edition of 2022. In this sense, this work seeks, with each research question, to simplify the presentation of results through different visualizations, which were planned and created to describe information that are not clearly evident when observing the IHC publications in a “disconnected” manner. The results of this study are revealed, described and analysed under different perspectives, as well as discussed in details in this paper.

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Section: Szymkiewicz-simpson Coefficientmentioning

confidence: 99%

An Analysis of the Authorship and Co-authorship Networks of the Brazilian Human-Computer Interaction Conference

Lima,

Miranda,

Vasiljevic

et al. 2024

JIS

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“…The management of data associations (Korepanova et al, 2020;Vijaymeena & Kavitha, 2016) is an integral part of computer vision object tracking, whether it is within the SORT algorithm or in the second step of the Deep-SORT algorithm. This management can be viewed as a problem of linear assignment, the crucial step of solving the tracking problem becomes apparent.…”

Section: Data Associationmentioning

confidence: 99%

OPTIMIZING PEDESTRIAN TRACKING FOR ROBUST PERCEPTION WITH YOLOv8 AND DEEPSORT

Zidani,

DJARAH,

BENMAKHLOUF

et al. 2024

Appl. Comput. Sci.

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Multi-object tracking is a crucial aspect of perception in the area of computer vision, widely used in autonomous driving, behavior recognition, and other areas. The complex and dynamic nature of environments, the ever-changing visual features of people, and the frequent appearance of occlusion interactions all impose limitations on the efficacy of existing pedestrian tracking algorithms. This results in suboptimal tracking precision and stability. As a solution, this article proposes an integrated detector-tracker framework for pedestrian tracking. The framework includes a pedestrian object detector that utilizes the YOLOv8 network, which is regarded as the latest state-of-the-art detector, that has been established. This detector provides an ideal detection base to address limitations. Through the combination of YOLOv8 and the DeepSort tracking algorithm, we have improved the ability to track pedestrians in dynamic scenarios. After conducting experiments on publicly available datasets such as MOT17 and MOT20, a clear improvement in accuracy and consistency was demonstrated, with MOTA scores of 63.82 and 58.95, and HOTA scores of 43.15 and 41.36, respectively. Our research highlights the significance of optimizing object detection to unleash the potential of tracking for critical applications like autonomous driving.

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“…The two models are consistent in the following training parameters: Batch size is 2; the learning rate is 1e-4, and the weight decay is 1e-5. The evaluation index for this training model performance verification is mean Dice Similarity Coefficient (mDice) [16,17].…”

Section: Model Trainingmentioning

confidence: 99%

Segmentation of Pulmonary Cavity in Lung CT Scan for Tuberculosis Disease

Zhuoyi Tan,

Hizmawati Madzin,

Fatimah Khalid

et al. 2023

ARASET

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The complexity of pulmonary tuberculosis (TB) lung cavity lesion features significantly increase the cost of semantic segmentation and labelling. However, the high cost of semantic segmentation has limited the development of TB automatic recognition to some extent. To address this issue, we developed an algorithm that automatically generates a semantic segmentation mask of TB from the TB target detection boundary box. Pulmonologists only need to identify and label the location of TB, and the algorithm can automatically generate the semantic segmentation mask of TB lesions in the labelled area. The algorithm, first, calculates the optimal threshold for separating the lesion from the background region. Then, based on this threshold, the lesion tissue within the bounding box is extracted and forms a mask that can be used for semantic segmentation tasks. Finally, we use the generated TB semantic segmentation mask to train Unet and Vnet models to verify the effectiveness of the algorithm. The experimental results demonstrate that Unet and Vnet achieve mean Dice coefficients of 0.612 and 0.637, respectively, in identifying TB lesion tissue.

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Applicability of Similarity Coefficients in Social Circle Matching

Cited by 14 publications

References 5 publications

An Analysis of the Authorship and Co-authorship Networks of the Brazilian Human-Computer Interaction Conference

An Analysis of the Authorship and Co-authorship Networks of the Brazilian Human-Computer Interaction Conference

OPTIMIZING PEDESTRIAN TRACKING FOR ROBUST PERCEPTION WITH YOLOv8 AND DEEPSORT

Segmentation of Pulmonary Cavity in Lung CT Scan for Tuberculosis Disease

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