[go: up one dir, main page]
ISSN: 2409-9708
Submit Article
Submission Instructions
Open Access Information
Ethics and Malpractice Statement
Most Recent Issue
2022
Issue 31Issue 3Issue 30
2021
Issue 29Issue 28Issue 27Issue 26
2020
Issue 25Issue 24Issue 23Issue 22
2019
Issue 21Issue 20Issue 19Issue 18
2018
Issue 17Issue 16Issue 15Issue 14
2017
Issue 13Issue 12Issue 11Issue 10
2016
Issue 9Issue 8Issue 7Issue 6
2015
Issue 5Issue 4Issue 3Issue 2
2014
Issue 1

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/ct/index

Editor(s)-in-Chief: Predrag K. Nikolic

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.

The journal is not limited to only specific aspects of creative technologies but also open to considering the high-level and innovative contributions from a wide range of subfields, especially articles of interdisciplinary nature.

Some of the journal topics are:

Creative Industries - the paper can describe a whole industrial project or framework integrating creative technologies for domains such as:

  • Creative content providers: TV & radio (broadcast, linked media), fashion (RFID and sensors, energy recycling), music & movies, books (digitization, sonification), museums (creative/artistic installations for tourism or cultural heritage) and media archives, etc.
  • Creative services providers: Advertising (user-centered web and ads optimization), design (communication devices for creative environments), architecture (places that adapt to people, creativity from domotics), new media and transmedia, etc.
  • Creative experience providers: Movies and music experience, game-based installations and playable media (participative creation, DJs and VJs creative tools), interactive social and urban spaces, etc.

Scientific topics - the paper can focus on scientific tools for creative industries on topics such as:

  • Creative content management: Multimedia data mining and innovative browsing, data 2D/3D visualization, digitization and storage, cultural heritage, etc.
  • Creative interactions: Social signal processing, verbal and nonverbal, explicit or implicit interactions, interacting robots, sensors and MoCap, human-computer interaction, user interface design, serious game etc.
  • Creative experiences: Affective content analysis and synthesis, immersive technologies, special effects, augmented reality, expressivity and emotion detection and processing, user experiences, etc.

Additional materials such as videos of installations and setups, public/users reactions and comments, or demo links are welcome.

Call for Papers: ICDM 2021 (Manuscript submission deadline: 2021-10-15; Notification of acceptance: 2021-12-10; Submission of final revised paper: 2022-01-21; Publication of special issue (tentative): 2022-02-04)

Lead Guest Editor: Prof Dr. Mohd Shahrizal Sunar (Universiti Teknologi Malaysia) Guest Editor: Dr. Goh Eg Su (Universiti Teknologi Malaysia) Guest Editor: Ruhiyati Idayu Abu Talib (Universiti Teknologi Malaysia)

  • Albert Ali Salah (Bogazici University, Turkey)
  • Anthony Brooks (SensoramaLab, Aalborg University Esbjerg, Denmark)
  • Anton Nijholt (University of Twente, Netherlands)
  • Antonio Camurri (DIST-University of Genova, Italy)
  • Antonio Camurri (University of Genoa, Italy)
  • Christophe d'Alessandro (LIMSI-CNRS, France)
  • Christophe Hurter (ENAC, French Civil Aviation University, Toulouse France)
  • Cristina Sylla (University of Minho, Portugal)
  • Dirk Heylen (University of Twente, Netherlands)
  • Eva Irene (Aalborg University, Denmark)
  • Fabrizio Lamberti (Politecnico di Torino, Italy)
  • Gualtiero Volpe (University of Genoa, Italy)
  • Insook Choi (Columbia College Chicago, USA)
  • Jesus Ibanez (Escola Municipal d'Art i Disseny (EMAiD), Spain)
  • Jonathan Duckworth (RMIT University, Australia)
  • Jorge C. S. Cardoso (University of Coimbra, Portugal)
  • Jun Hu (Eindhoven University of Technology, Netherlands)
  • Lau Bee Theng (Swinburne University of Technology Sarawak Campus, Malaysia)
  • Mel Krokos (University of Portsmouth, United Kingdom)
  • Michail Kalogiannakis (Faculty of Education, University of Crete, Greece)
  • Milton Luiz Horn Vieira (Federal University of Santa Catarina, Brazil)
  • Mikkel Kirkedahl Lysholm Nielsen (Aalborg University, Denmark)
  • Pedro Campos (ITI/Larsys, University of Madeira, Funchal, Portugal)
  • Pieter Jan Maes (McGill University, Canada)
  • Rainer Malaka (University of Bremen, Germany)
  • Radu-Daniel Vatavu (University of Suceava, Romania)
  • Robin Bargar (Columbia College Chicago, USA)
  • Robby van Delden (University of Twente, Netherlands)
  • Sudarshan Khanna (National Institute of Design, India)
  • Surabhi Khanna (National Institute of Design Haryana, India)
  • Thierry Dutoit (University of Mons, Belgium)
  • Tiago Cardoso (University Nova of Lisbon, Portugal)
  • Tracy Harwood (De Montfort University, Leicester, United Kingdom)
  • Vincent Courboulay (University of La Rochelle, France)
  • Valentina Nisi (University of Madeira, Portugal)
  • Yves Rybarczyk (University Nova of Lisbon, Portugal)
  • Mohd Shahrizal Sunar (Universiti Teknologi, Malaysia)

  • Quadcopter Vehicle Proportional-Integral-Derivative Controller (PIDC) Tuning by Particle-Swarm Optimization (PSO)

    Appears in:
    ct 22(31): e5

    Authors:
    Abdullah Al Gizi

    Published:
    16th Aug 2022

    Abstract:
    Inspection of the faults and damages in high voltage transmission lines is not only unsafe and costly but also a very time-consuming process. In addition, it demands highly skilled manpower for the operation at tens of meters above the ground on cables carrying thousands of Volts. Meanwhile, the Quadcopter Aerial Vehicle (QAV) system became a popular alternative for aerial photography applications due to its low cost, ease of usage, and fast response wherein the proportional-integral-derivative controller (PIDC) is used. The PID controller compares the expected location of the drone with the actual measured position thereby can accurately detect the faults in the transmission lines. Based on these factors, in this study, the flight simulation is done according to the desired flight path, and drone imagery is used for (insulator, power line, and porcelain) flaw identification. Furthermore, three movements (roll, pitch, and yaw) of the quadcopter were controlled by different PIDC that were optimized using the particle swarm optimization (PSO). The MATLAB/Simulink application was used to develop the system and simulate the results (R2021b). This clearly suggested that the quadcopter continues to fly on a trajectory with minimum inaccuracy, wherein the PIDC operated as a closed-loop adaptive controller. Finally, the PSO-PIDC outperformed the PIDC due to the critical nature of manual gain modification for quadcopter flight stability.

  • Methodology to eliminate errors in machining processes through Augmented Reality Applications

    Appears in:
    ct 22(31): e3

    Authors:
    Elva Lilia REYNOSO JARDON, Manuel MERAZ MÉNDEZ

    Published:
    6th Jul 2022

    Abstract:
    Today, precision machining companies must implement new technologies to meet the requirements of the aerospace and automotive industries to maintain their competitiveness in the market and create new business opportunities. It has currently been observed that machining companies face errors in their processes that affect the quality of the products, the operation of the process, the useful life of the machines, and the tools. This research project aims to develop a methodology to reduce errors in Computerized Numerical Control machining (CNC) processes with Augmented Reality Applications (AR-Apps) that help operators interpret manufacturing drawings and assembly operations optimize Setup operations and ensure the reliability of the machining. It begins with a questionnaire applied to a sample of machining companies from Chihuahua Mexico City. To identify the factors that generate critical errors in the machining processes. The main results obtained in this paper are reduction of downtime, elimination of scrap, increment cutting tools life, optimize the machining process, increase productivity, and improved product quality. AR -apps implementation offers a low-cost alternative solution that operators and technicians can use very easily because it is a powerful tool that helps work more efficiently through virtual environments, reducing errors and optimizing the process of machining.

  • Capturing Racial & Gender Inequities on Social Media Platforms using Machine Learning

    Appears in:
    ct 22(31): e4

    Authors:
    Sonika Malik, Harshita Chopra, Aniket Vashishtha

    Published:
    6th Jul 2022

    Abstract:
    Online social media platforms provide a continuously evolving database due to the highly increasing popularity and rapid expansion of its user base. Users share their life experiences towards various inequity incidents faced at the workplace on the basis of their race or gender on these platforms while maintaining their anonymity. We aim at utilising famous social media platforms to perform extensive analysis and classification tasks for posts capturing instances of various types of Inequalities prevalent in today’s workplace. We present a framework to mine opinions expressed towards sexual harassment, mental health, racial injustice and gender-based bias in the corporate workplace using NLP techniques on social media data. The documents are represented by semantic similarity to aspect embedding’s captured using an attention-based framework for aspect extraction. In addition, we used scores from Empath categories to add information related to emotional facets.

  • Using Blockchain to Ensure the Integrity of Digital Forensic Evidence in an IoT Environment

    Appears in:
    ct 22(31): e2

    Authors:
    Tao Feng, Muhammad Shoaib Akhtar

    Published:
    3rd Jun 2022

    Abstract:
    Digital forensics deals with digital evidence. Digital forensics is the study of data detection, acquisition, processing, analysis, and reporting. Encouraging the use of digital forensics in law enforcement investigations. With digital forensics, you can find out what data was taken and how it was copied or spread. Some hackers purposefully destroy data to harm their targets. In other cases, malicious software or hacker involvement can accidentally corrupt vital data. Digital forensics faces challenges of security and integrity. IoT devices can collect digital forensic evidence in an IoT setting, putting cybercrime agencies at danger owing to security and integrity. Many studies have been done recently to improve IoT based digital forensics integrity and security, but researchers face the risk of confidentiality. Recent research shows that digital forensics still faces manipulation and security issues. So a clever and effective approach is needed that not only protects security and integrity but also anticipates threats. So we propose an intelligent and effective solution based on Blockchain and Hashing algorithms. We will store the data collected from IoT devices into Blockchain. Anomalies in the evidence and transactions will be predicted using Machine Learning boosted models. So the proposed model works well because it can predict attacks early on.

  • IoT Based Detection of Molded Bread and Expiry Prediction using Machine Learning Techniques

    Appears in:
    ct 22(31): e1

    Authors:
    Tao Feng, Muhammad Shoaib Akhtar

    Published:
    27th Apr 2022

    Abstract:
    Expiration of a bread is a very popular issue in food logistics. Due to various conditions fungal bread can cause food poisoning for consumers. As a result, nausea, diarrhea and different medical issues appear in people. For this purpose, an intelligent system required for the detection of present condition of bread is required which will help the stores and consumers. In this study, we have developed a prototype made up of Arduino Nano as a microcontroller, MQ series sensors for CO and CO2 detection in shopper bags of bread in order to collect data. This data is further processed in different machine learning algorithms for the detection of current condition of bread in these stores. The data collected from these sensors was imbalanced. Data collected from sensors is then balanced by using SMOTE and TOMEC Links (data balancing techniques). Furthermore, data preprocessing and feature engineering has been applied on IoT Based dataset to improve its efficiency. We have applied linear learning models for the prediction of current condition of bread. Within linear models, Gaussian Naïve Bayes has scored highest accuracy of 81.54%.

  • Using Blockchain to Ensure the Integrity of Digital Forensic Evidence in an IoT Environment

    Appears in:
    ct 22(31): e2

    Authors:
    Tao Feng, Muhammad Shoaib Akhtar

    Downloads:
    2432

    Abstract:
    Digital forensics deals with digital evidence. Digital forensics is the study of data detection, acquisition, processing, analysis, and reporting. Encouraging the use of digital forensics in law enforcement investigations. With digital forensics, you can find out what data was taken and how it was copied or spread. Some hackers purposefully destroy data to harm their targets. In other cases, malicious software or hacker involvement can accidentally corrupt vital data. Digital forensics faces challenges of security and integrity. IoT devices can collect digital forensic evidence in an IoT setting, putting cybercrime agencies at danger owing to security and integrity. Many studies have been done recently to improve IoT based digital forensics integrity and security, but researchers face the risk of confidentiality. Recent research shows that digital forensics still faces manipulation and security issues. So a clever and effective approach is needed that not only protects security and integrity but also anticipates threats. So we propose an intelligent and effective solution based on Blockchain and Hashing algorithms. We will store the data collected from IoT devices into Blockchain. Anomalies in the evidence and transactions will be predicted using Machine Learning boosted models. So the proposed model works well because it can predict attacks early on.

  • IoT Based Detection of Molded Bread and Expiry Prediction using Machine Learning Techniques

    Appears in:
    ct 22(31): e1

    Authors:
    Tao Feng, Muhammad Shoaib Akhtar

    Downloads:
    1512

    Abstract:
    Expiration of a bread is a very popular issue in food logistics. Due to various conditions fungal bread can cause food poisoning for consumers. As a result, nausea, diarrhea and different medical issues appear in people. For this purpose, an intelligent system required for the detection of present condition of bread is required which will help the stores and consumers. In this study, we have developed a prototype made up of Arduino Nano as a microcontroller, MQ series sensors for CO and CO2 detection in shopper bags of bread in order to collect data. This data is further processed in different machine learning algorithms for the detection of current condition of bread in these stores. The data collected from these sensors was imbalanced. Data collected from sensors is then balanced by using SMOTE and TOMEC Links (data balancing techniques). Furthermore, data preprocessing and feature engineering has been applied on IoT Based dataset to improve its efficiency. We have applied linear learning models for the prediction of current condition of bread. Within linear models, Gaussian Naïve Bayes has scored highest accuracy of 81.54%.

  • Quadcopter Vehicle Proportional-Integral-Derivative Controller (PIDC) Tuning by Particle-Swarm Optimization (PSO)

    Appears in:
    ct 22(31): e5

    Authors:
    Abdullah Al Gizi

    Downloads:
    999

    Abstract:
    Inspection of the faults and damages in high voltage transmission lines is not only unsafe and costly but also a very time-consuming process. In addition, it demands highly skilled manpower for the operation at tens of meters above the ground on cables carrying thousands of Volts. Meanwhile, the Quadcopter Aerial Vehicle (QAV) system became a popular alternative for aerial photography applications due to its low cost, ease of usage, and fast response wherein the proportional-integral-derivative controller (PIDC) is used. The PID controller compares the expected location of the drone with the actual measured position thereby can accurately detect the faults in the transmission lines. Based on these factors, in this study, the flight simulation is done according to the desired flight path, and drone imagery is used for (insulator, power line, and porcelain) flaw identification. Furthermore, three movements (roll, pitch, and yaw) of the quadcopter were controlled by different PIDC that were optimized using the particle swarm optimization (PSO). The MATLAB/Simulink application was used to develop the system and simulate the results (R2021b). This clearly suggested that the quadcopter continues to fly on a trajectory with minimum inaccuracy, wherein the PIDC operated as a closed-loop adaptive controller. Finally, the PSO-PIDC outperformed the PIDC due to the critical nature of manual gain modification for quadcopter flight stability.

  • Methodology to eliminate errors in machining processes through Augmented Reality Applications

    Appears in:
    ct 22(31): e3

    Authors:
    Elva Lilia REYNOSO JARDON, Manuel MERAZ MÉNDEZ

    Downloads:
    655

    Abstract:
    Today, precision machining companies must implement new technologies to meet the requirements of the aerospace and automotive industries to maintain their competitiveness in the market and create new business opportunities. It has currently been observed that machining companies face errors in their processes that affect the quality of the products, the operation of the process, the useful life of the machines, and the tools. This research project aims to develop a methodology to reduce errors in Computerized Numerical Control machining (CNC) processes with Augmented Reality Applications (AR-Apps) that help operators interpret manufacturing drawings and assembly operations optimize Setup operations and ensure the reliability of the machining. It begins with a questionnaire applied to a sample of machining companies from Chihuahua Mexico City. To identify the factors that generate critical errors in the machining processes. The main results obtained in this paper are reduction of downtime, elimination of scrap, increment cutting tools life, optimize the machining process, increase productivity, and improved product quality. AR -apps implementation offers a low-cost alternative solution that operators and technicians can use very easily because it is a powerful tool that helps work more efficiently through virtual environments, reducing errors and optimizing the process of machining.

  • Capturing Racial & Gender Inequities on Social Media Platforms using Machine Learning

    Appears in:
    ct 22(31): e4

    Authors:
    Sonika Malik, Harshita Chopra, Aniket Vashishtha

    Downloads:
    479

    Abstract:
    Online social media platforms provide a continuously evolving database due to the highly increasing popularity and rapid expansion of its user base. Users share their life experiences towards various inequity incidents faced at the workplace on the basis of their race or gender on these platforms while maintaining their anonymity. We aim at utilising famous social media platforms to perform extensive analysis and classification tasks for posts capturing instances of various types of Inequalities prevalent in today’s workplace. We present a framework to mine opinions expressed towards sexual harassment, mental health, racial injustice and gender-based bias in the corporate workplace using NLP techniques on social media data. The documents are represented by semantic similarity to aspect embedding’s captured using an attention-based framework for aspect extraction. In addition, we used scores from Empath categories to add information related to emotional facets.
Publisher
EAI
ISSN
2409-9708
Number of Volumes
9
Last Published
2022-07-12
EAI Logo

About EAI

Community

Publish with EAI