IMPLEMENTASI ARSITEKTUR REST DALAM APLIKASI VIDEO CONFERENCE DENGAN FITUR PENGENALAN EMOSI MENGGUNAKAN WEBRTC

Emosi memiliki pengaruh yang signifikan dalam proses pembelajaran, karena dapat memengaruhi ingatan dan tindakan. Saat ini, berbagai jenis Application Programming Interface (API) pengenalan emosi telah tersedia, memberikan kesempatan untuk menerapkan aplikasi pengenalan emosi dan aplikasi visualisasi secara real-time. Mendapatkan data real-time biasanya dilakukan melalui penggunaan suatu API, seperti Representational State Transfer (REST). Terdapat beberapa aplikasi yang digunakan untuk mengenali emosi siswa dalam pembelajaran daring, salah satunya adalah aplikasi video conference. Salah satu teknologi yang digunakan dalam membangun suatu aplikasi video conference yaitu WebRTC. Penelitian ini bertujuan untuk mengembangkan aplikasi WebRTC pengenalan emosi dengan arsitektur REST, serta menganalisis performa aplikasi back-end dan front-end. Performa aplikasi back-end diukur menggunakan metrik Quality of Service (QoS) seperti response time, throughput, memory utilization, dan CPU Load. Performa aplikasi front-end diukur dengan metrik Google Lighthouse Performance. Hasilnya pada aplikasi back-end pada endpoint Recognition Grup nilai rata rata Response Time sebesar 2567,34 ms, Throughput 36,89 request/s, Memory Utilization 622,05 MB, CPU Load 9,53 %. Sedangkan pada endpoint Recognition Individu nilai rata rata Response Time sebesar 3209,18 ms, Throughput 29,39 request/s, Memory Utilization 623,96 MB, CPU Load 7,67 %. Hasilnya pada aplikasi front-end nilai rata - rata pada metrik FCP sebesar 936,1 ms, SI sebesar 1095,28 ms, LCP sebesar 1154,54 ms, TTI sebesar 972,55ms, TBT sebesar 0,728 dan CLS sebesar 0. Dengan demikian keseluruhan metrik menghasilkan nilai 96% Performance Score sehingga performa aplikasi front-end dapat dikatakan Baik. -------- Emotions have a significant impact on the learning process as they can influence memory and actions. Currently, various types of Emotion Recognition Application Programming Interfaces (APIs) are available, providing opportunities to implement real-time emotion recognition and visualization applications. Real-time data acquisition is typically accomplished through the use of an API, such as Representational State Transfer (REST). One of the applications used for recognizing students' emotions in online learning is video conferencing applications. WebRTC is one of the technologies used to build a video conferencing application. This research aims to develop a WebRTC-based emotion recognition application with a REST architecture and analyze the performance of the backend and front-end components. The back-end application's performance is measured using Quality of Service (QoS) metrics, including response time, throughput, memory utilization, and CPU load. The front-end application's performance is measured using Google Lighthouse Performance metrics. The results for the back-end application show that, in the Recognition Group endpoint, the average response time is 2567.34 ms, throughput is 36.89 requests/s, memory utilization is 622.05 MB, and CPU load is 9.53%. Meanwhile, in the Recognition Individual endpoint, the average response time is 3209.18 ms, throughput is 29.39 requests/s, memory utilization is 623.96 MB, and CPU load is 7.67%. The results for the front-end application indicate that the average values for the FCP, SI, LCP, TTI, TBT, and CLS metrics are 936.1 ms, 1095.28 ms, 1154.54 ms, 972.55 ms, 0.728, and 0, respectively. Thus, the overall metrics yield a 96% Performance Score, indicating that the front-end application's performance can be considered Good

Emotion Analysis in Hospital Bedside Infotainment Platforms Using Speeded up Robust Features

Far from the heartless aspect of bytes and bites, the field of affective computing investigates the emotional condition of human beings interacting with computers by means of sophisticated algorithms. Systems that integrate this technology in healthcare platforms allow doctors and medical staff to monitor the sentiments of their patients, while they are being treated in their private spaces. It is common knowledge that the emotional condition of patients is strongly connected to the healing process and their health. Therefore, being aware of the psychological peaks and troughs of a patient, provides the advantage of timely intervention by specialists or closely related kinsfolk. In this context, the developed approach describes an emotion analysis scheme which exploits the fast and consistent properties of the Speeded-Up Robust Features (SURF) algorithm in order to identify the existence of seven different sentiments in human faces. The whole functionality is provided as a web service for the healthcare platform during regular Web RTC video teleconference sessions between authorized medical personnel and patients. The paper discusses the technical details of the implementation and the incorporation of the proposed scheme and provides initial results of its accuracy and operation in practice. © 2019, IFIP International Federation for Information Processing

Emotion Analysis in Hospital Bedside Infotainment Platforms Using Speeded up Robust Features

Part 4: Biomedical AIInternational audienceFar from the heartless aspect of bytes and bites, the field of affective computing investigates the emotional condition of human beings interacting with computers by means of sophisticated algorithms. Systems that integrate this technology in healthcare platforms allow doctors and medical staff to monitor the sentiments of their patients, while they are being treated in their private spaces. It is common knowledge that the emotional condition of patients is strongly connected to the healing process and their health. Therefore, being aware of the psychological peaks and troughs of a patient, provides the advantage of timely intervention by specialists or closely related kinsfolk. In this context, the developed approach describes an emotion analysis scheme which exploits the fast and consistent properties of the Speeded-Up Robust Features (SURF) algorithm in order to identify the existence of seven different sentiments in human faces. The whole functionality is provided as a web service for the healthcare platform during regular Web RTC video teleconference sessions between authorized medical personnel and patients. The paper discusses the technical details of the implementation and the incorporation of the proposed scheme and provides initial results of its accuracy and operation in practice

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality

Esa 12th Conference: Differences, Inequalities and Sociological Imagination: Abstract Book

Esa 12th Conference: Differences, Inequalities and Sociological Imagination: Abstract Boo

The Essential Cult TV Reader

The Essential Cult TV Reader is a collection of insightful essays that examine television shows that amass engaged, active fan bases by employing an imaginative approach to programming. Once defined by limited viewership, cult TV has developed its own identity, with some shows gaining large, mainstream audiences. By exploring the defining characteristics of cult TV, The Essential Cult TV Reader traces the development of this once obscure form and explains how cult TV achieved its current status as legitimate television. The essays explore a wide range of cult programs, from early shows such as Star Trek, The Avengers, Dark Shadows, and The Twilight Zone to popular contemporary shows such as Lost, Dexter, and 24, addressing the cultural context that allowed the development of the phenomenon. The contributors investigate the obligations of cult series to their fans, the relationship of camp and cult, the effects of DVD releases and the Internet, and the globalization of cult TV. The Essential Cult TV Reader answers many of the questions surrounding the form while revealing emerging debates on its future. David Lavery, professor of English at Middle Tennessee State University, is the founding editor of Critical Studies in Television: Scholarly Studies of Small Screen Fictions. “An engaging, in-depth look at the often-slippery category of cult programming and is the perfect starting point for further studies on the subject.”—Studies In American Culturehttps://uknowledge.uky.edu/upk_american_popular_culture/1001/thumbnail.jp