EAI Endorsed Transactions on Creative Technologies https://publications.eai.eu/index.php/ct <p>EAI Endorsed Transactions on Creative Technologies is open access, a peer-reviewed scholarly journal focused on a whole industrial project or framework integrating creative technologies, creative content management, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). Authors are not charged for article submission and processing.</p> en-US <p>This is an open-access article distributed under the terms of the Creative Commons Attribution <a href="https://creativecommons.org/licenses/by/3.0/" target="_blank" rel="noopener">CC BY 3.0</a> license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.</p> publications@eai.eu (EAI Publications Department) publications@eai.eu (EAI Support) Wed, 23 Nov 2022 12:10:40 +0000 OJS http://blogs.law.harvard.edu/tech/rss 60 TailorEd: Classroom Configuration and Activity Identifiers (CCID & CAID) https://publications.eai.eu/index.php/ct/article/view/2229 <p>INTRODUCTION: The study of how classroom layout and activities affect learning outcomes of students with different demographics is difficult because it is hard to gather accurate information on the minute by minute progression of every class in a course. Furthermore, the process of data gathering must produce an abundance of data to work with and hence must be automated.</p><p><br />OBJECTIVES: A machine learning model trained on images of a classroom and thus capable of accurately labeling the classroom layout and activity of many thousands of images much faster and cheaper than employing a human. </p><p><br />METHODS: Transfer learning can allow for preexisting computer vision models to be retrained on a smaller, more specific dataset in order to still achieve a highly accurate result. </p><p><br />RESULTS: In the case of the classroom layout, the final model achieved an accuracy of 97% on a four category classification. And for detecting the classroom activity, after experimentation with several different versions that could work on a very small sample sizes, the best model achieved an accuracy of 86.17%.</p><p><br />CONCLUSION: In addition to showing that using computer vision to determine human activities is possible albeit more difficult than layouts of inanimate objects such as classroom desks, the study shows the differences between the use of self-supervised learning techniques and data augmentation techniques in order to overcome the problem of small training data-sets.</p> Andres Calle, Quan Nguyen, Kristin Lee, Julia Voss, Navid Shaghaghi Copyright (c) 2022 EAI Endorsed Transactions on Creative Technologies https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ct/article/view/2229 Thu, 28 Jul 2022 00:00:00 +0000 Integrating the Fairlight CVI into the video workflow https://publications.eai.eu/index.php/ct/article/view/2650 <p>The Fairlight Computer Video Instrument (CVI) is one of the earliest video synthesisers, released in 1984. Over time the aesthetic of its effects has evolved from revolutionary to retro. Although MIDI was developed at around the same time, the CVI is controllable via RS232 rather than MIDI. This paper reviews its history and applications in live and studio-based video production environments. A method is outlined for controlling the CVI that allows sequencing of control data, effect automation, and integration with a digital audio workstation. Results are presented from a system using Logic Pro X for sequencing and Processing for MIDI-serial conversion.</p> D.J.E. Nunn Copyright (c) 2022 EAI Endorsed Transactions on Creative Technologies https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ct/article/view/2650 Tue, 30 Aug 2022 00:00:00 +0000 Movie Recommender System using Machine Learning https://publications.eai.eu/index.php/ct/article/view/2712 <p class="ICST-abstracttext"><span lang="EN-GB">In this research, we propose a movie recommender system that can recommend movies to both new and existing customers. It searches movie databases for all of the relevant data, such as popularity and beauty that is required for a recommendation. We apply both content-based and collaborative filtering and evaluate their advantages and disadvantages. To build a system that delivers more exact movie recommendations, we employ hybrid filtering, which is a combination of the outcomes of these two processes. The recommendation engines are also used for business purposes and to make strategies for organizations. Due to the growing demands of customers and user’s recommendation systems plays a huge role. These recommender systems also help us to utilize our time in the busy world by giving us more relevant searches. These systems are generally used with the movie’s websites or with many commercial applications and are of great use. This type of recommendation system can be also used for precise results. It will make movies suggestions more relevant as per the need of the users.</span></p> Sonika Malik Copyright (c) 2022 EAI Endorsed Transactions on Creative Technologies https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ct/article/view/2712 Tue, 11 Oct 2022 00:00:00 +0000 Techniques For Reducing Energy And Delay For Data Aggregation In Wireless Sensor Networks https://publications.eai.eu/index.php/ct/article/view/2140 <p class="ICST-abstracttext"><span lang="EN-GB">WSN have many applications in different fields like medical, military, health and agriculture, etc., due to its data sensing and gathering abilities to Base station. The main issue in wireless sensor network is energy efficiency under consideration of QOS parameter like delay and security. Many of techniques have been proposed in literature but few work on energy efficient network with QOS. Due to lack of prior research in this area of study, this research will optimize the existing result in manner of giving efficient energy mechanisms and will also provide QOS as well as reduction of delay. It is very important to calculate energy efficiency and data transmission rate in wireless sensor networks because it is widely used in every field of life, especially when we are talking about medical, military and navigation system. After identifying main issues, this research will focus on energy efficiency and delay reduction. But other factors like security and average transmission time is not fully focused. </span></p> Shoaib, Feng Tao Copyright (c) 2022 EAI Endorsed Transactions on Creative Technologies https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ct/article/view/2140 Tue, 11 Oct 2022 00:00:00 +0000 AI_deation: A Creative Knowledge Mining Method for Design Exploration https://publications.eai.eu/index.php/ct/article/view/2685 <p>Ideation is a core activity in the design process which begins with a design brief and results in a range of design concepts. However, due to its exploratory nature it is challenging to formalise computationally. Here, we report a creative knowledge mining method that combines design theory with a machine learning approach. This study begins by introducing a graphic design style classification model that acts as a model for the aesthetic evaluation of images. A Grad-CAM technique is used to visualise where our model is looking at in order to detect and interpret visual syntax, such as geometric influences and color gradients, to determine the most influential visual semiotics. Our comparative analysis on two Nordic design referents suggests that our approach can be efficiently used to support and motivate design exploration. Based on these findings, we discuss the prospects of machine vision aided design systems to envisage concepts and possible design paths, but also to support educational objectives.</p> George Palamas, Alejandra Mesa Guerra, Liana-Dorina Møsbæk Copyright (c) 2022 EAI Endorsed Transactions on Creative Technologies https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ct/article/view/2685 Wed, 23 Nov 2022 00:00:00 +0000