Entertainment Applications Research Articles

Personalized Emotion Detection Adapting Models to Individual Emotional Expressions

Emotion recognition from text and speech has become a critical area of research in artificial intelligence (AI), enhancing human-computer interaction across various sectors. This paper explores the methodologies used in emotion recognition, focusing on Natural Language Processing (NLP) for text and acoustic analysis for speech. It reviews key machine learning and deep learning models, including Support Vector Machines (SVM), neural networks, and transformers, and highlights the datasets commonly used in emotion detection studies. The paper also addresses challenges such as multimodal integration, data ambiguity, and ethical considerations like privacy concerns and bias in models. Applications in customer service, healthcare, education, and entertainment are discussed, showcasing the growing importance of emotion recognition in AI- driven systems. Future research directions, including advancements in deep learning, multimodal systems, and real-time processing, are also explored to address existing limitations.

Personalization at Scale: Big Data Technologies in Digital Content Delivery and Their Effect on Viewer Experience

This article examines the transformative impact of big data technologies on the digital content delivery industry, focusing on their application in enhancing customer experience through video streaming services. Through a comprehensive case study of a leading provider, we investigate the implementation of Hadoop and AWS Big Data product suites to optimize content delivery, personalize user recommendations, and enable dynamic ad insertion. Our findings demonstrate significant improvements in service reliability, viewer engagement, and advertising effectiveness. The article reveals how Big Data analytics facilitate real-time content optimization and personalized user experiences, increasing viewer retention and satisfaction. Furthermore, we explore the challenges and limitations encountered during implementation, providing insights into the practical applications of Big Data in the evolving landscape of digital media consumption. This article contributes to the growing body of literature on Big Data applications in media and entertainment, offering valuable insights for practitioners and researchers in digital content delivery.

Haptic artificial muscle skin for extended reality.

Existing haptic actuators are often rigid and limited in their ability to replicate real-world tactile sensations. We present a wearable haptic artificial muscle skin (HAMS) based on fully soft, millimeter-scale, multilayer dielectric elastomer actuators (DEAs) capable of significant out-of-plane deformation, a capability that typically requires rigid or liquid biasing. The DEAs use a thickness-varying multilayer structure to achieve large out-of-plane displacement and force, maintaining comfort and wearability. Experimental results demonstrate that HAMS can produce complex tactile feedback with high perception accuracy. Moreover, we show that HAMS can be integrated into extended reality (XR) systems, enhancing immersion and offering potential applications in entertainment, education, and assistive technologies.

Deep Learning-based Emotion Recognition Algorithms in Music Performance

In the realm of artificial intelligence and musicology, emotion recognition in music performance has emerged as a pivotal area of research. This paper introduces EmoTrackNet, an integrated deep learning framework that combines sparse attention networks, enhanced one-dimensional residual Convolutional Neural Networks (CNNs) with an improved Inception module, and Gate Recurrent Units (GRUs). The synergy of these technologies aims to decode complex emotional cues embedded in music. Our methodology revolves around leveraging the sparse attention network to efficiently process temporal sequences, thereby capturing the intricate dynamics of musical pieces. The incorporation of the 1D residual CNN with an upgraded Inception module facilitates the extraction of nuanced features from audio signals, encompassing a broad spectrum of musical tones and textures. The GRU component further refines the model’s capability to retain and process sequential information over longer timeframes, essential for understanding evolving emotional expressions in music. We evaluated EmoTrackNet on the Soundtrack dataset a comprehensive collection of music pieces annotated with emotional labels. The results demonstrated remarkable improvements in the accuracy of emotion recognition, outperforming existing models. This enhanced performance can be attributed to the integrated approach, which efficiently combines the strengths of each component, leading to a more robust and sensitive emotion detection system. EmoTrackNet’s novel architecture and promising results pave the way for new avenues in musicology, particularly in understanding and interpreting the emotional depth of musical performances. This framework not only contributes significantly to the field of music emotion recognition but also has potential applications in music therapy, entertainment, and interactive media where emotional engagement is key.

Analysis of User Satisfaction with the Netflix Application as an Entertaiment Medium Using the UX Method

This research was conducted to analyze user satisfaction with the Netflix application using the UX Honeycomb method. The UX Honeycomb method is used to evaluate various aspects of user experience, such as usability, aesthetic value, sustainability, accessibility, satisfaction, and diversity. This study involved data collection through online surveys and interviews with active Netflix users. Data analysis was conducted using statistical techniques and qualitative interpretation. The UX Honeycomb method was employed to understand the essential aspects of user experience within the application. The results of this study are expected to provide valuable insights for other streaming service providers to enhance their service quality and create a better user experience. Additionally, the contribution of the UX Honeycomb methodology in assessing user satisfaction can serve as a foundation for further research in the context of digital entertainment applications.

Interactive Mesh Sculpting with Arbitrary Topologies in Head-Mounted VR Environments

Shape modeling is a dynamic area in computer graphics with significant applications in computer-aided design, animation, architecture, and entertainment. Virtual sculpting, a key paradigm in free-form modeling, has traditionally been performed on desktop computers where users manipulate meshes with controllers and view the models on two-dimensional displays. However, the advent of Extended Reality (XR) technology has ushered in immersive interactive experiences, expanding the possibilities for virtual sculpting across various environments. A real-time virtual sculpting system implemented in a Virtual Reality (VR) setting is introduced in this paper, utilizing quasi-uniform meshes as the foundational structure. In our innovative sculpting system, we design an integrated framework encompassing a surface selection algorithm, mesh optimization technique, mesh deformation strategy, and topology fusion methodology, which are all tailored to meet the needs of the sculpting process. The universal, user-friendly sculpting tools designed to support free-form topology are offered in this system, ensuring that the meshes remain watertight, manifold, and free from self-intersections throughout the sculpting process. The models produced are versatile and suitable for use in diverse fields such as gaming, art, and education. Experimental results confirm the system’s real-time performance and universality, highlighting its user-centric design.

3D Gamut Morphing for Non-Rectangular Multi-Projector Displays.

In a spatially augmented reality system, multiple projectors are tiled on a complex shaped surface to create a seamless display on it. This has several applications in visualization, gaming, education and entertainment. The main challenges in creating seamless and undistorted imagery on such complex shaped surfaces are geometric registration and color correction. Prior methods that provide solutions for the spatial color variation in multi-projector displays assume rectangular overlap regions across the projectors that is possible only on flat surfaces with extremely constrained projector placement. In this article, we present a novel and fully automated method for removing color variations in a multi-projector display on arbitrary shaped smooth surfaces using a general color gamut morphing algorithm that can handle any arbitrarily shaped overlap between the projectors and assures imperceptible color variations across the display surface.

The application of Verilog in the development of casual games

Verilog is a hardware description language (HDL) that is widely used in digital circuit design and simulation. Its development is closely related to computer science and electrical engineering. Verilog gained popularity in the early 1980s as digital circuit designs became increasingly complex, requiring more efficient circuit design and verification tools. At the same time, rapid advances in computer hardware also stimulated the demand for digital circuit design languages. Furthermore, the popularity and adoption of Verilog highlight the growing necessity for digitisation, automation, and intelligence in modern society. As digital technology continues to advance across various industries, the need for effective and dependable digital circuit design languages is also increasing. This paper delves into the complex process of recreating the timeless arcade classic Pac-Man on the Spartan 3E FPGA platform using hardware description and digital circuit techniques and the Verilog programming language. Through a comprehensive review of existing literature and research, this study investigates the fusion of traditional game design principles with state-of-the-art hardware programming methods, demonstrating the seamless integration of software-driven game mechanics with hardware-based implementation. Through careful design and coding strategies, Pac-Man's basic functionality, such as maze traversal, ghost AI, and pellet consumption, is faithfully replicated using Verilog modules customized for the Spartan 3E FPGA board. By bridging the realms of game development and hardware engineering, this paper not only showcases the versatility of Field Programmable Gate Array (FPGA) technology in entertainment applications but also underscores the interdisciplinary nature of modern computing endeavours.

Video Detection Method Based on Temporal and Spatial Foundations for Accurate Verification of Authenticity

With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research disciplines are actively engaged in developing deepfake video detection technologies to mitigate the risks associated with malicious deepfake content. Therefore, the importance of deepfake video detection technology cannot be overemphasized. This study addresses the challenge posed by images in nonexistent datasets by analyzing deepfake video detection methods. Using temporal and spatial detection techniques and employing 68 facial landmarks for alignment and feature extraction, this research integrates the attention-guided data augmentation (AGDA) strategy to enhance generalization capabilities. The detection performance is evaluated on four datasets: UADFV, FaceForensics++, Celeb-DF, and DFDC, with superior results compared to alternative approaches. To evaluate the study’s ability to accurately discriminate authenticity, detection experiments are conducted on both genuine and deepfake videos synthesized using the DeepFaceLab and FakeApp frameworks. The experimental results show better performance in detecting deepfake videos than other methods compared.

EMOJIFY

Emoticons have become a ubiquitous part of online communication, but their limitations hinder the expression of nuanced emotions. This project proposes "Emojify," a system that leverages deep learning for real-time emotion recognition through video input. By employing techniques like pre-processing, face detection, and Convolutional Neural Networks (CNNs), Emojify analyzes facial expressions and generates corresponding emojis. This approach goes beyond traditional emoticons, dynamically reflecting emotional intent and enriching virtual communication. The project explores the potential for entertainment applications and user-friendly emoji creation tools with encryption and decryption methods. Emojify holds promise for bridging the emotional gap in virtual interactions, fostering a more expressive and engaging online experience.

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

SummaryWith the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively.

Role of age, gender and cultural factors as moderator on technology acceptance of online entertainment

Purpose Research on technology acceptance of online entertainment with age, gender and cultural factors as moderator, is rarely conducted. Previous research predominantly focused on age or gender as moderator, neglecting the influence of cultural factors. Therefore, this study aims to investigate acceptance of online entertainment technology, incorporating age, gender and cultural factors as moderator. Design/methodology/approach Data were collected through a survey comprising 1,121 individuals aged 14–24 years from three cities in Indonesia. The proposed theoretical model examined the causal effect of acceptance and moderating effects due to individual gender, age, power distance, individualism, feminism and uncertainty avoidance (AU). Subsequently, structural equation modeling was used to evaluate the theoretical model, and the results confirmed several findings from previous research. Findings The findings confirmed the positive direct impact of habit and price value (PV) on behavioral intention and hedonic motivation, as well as social influence on habit. The recent findings derived from the moderating effect analysis showed that age, individualism and feminism played a moderating role in the effects on individual intention due to habit. Additionally, gender and AU moderated the effects on individual habits due to hedonic motivation. Originality/value This research contributes to the limited knowledge of technology acceptance of online entertainment, and also integrates the causal effects of individual intention due to habit, PV, hedonic motivation and social influence, considering the moderating role of culture, age and gender. Consequently, the investigation provides valuable insights into the literature by presenting evidence of age, gender and cultural differences in acceptance. Furthermore, it offers practical guidance to online entertainment application developers on designing applications to satisfy consumers of different ages, genders and cultures.

3D image processing technology based on interactive entertainment application in cultural and creative product design

3D printing technology has many advantages that traditional production models do not have, greatly accelerating the speed of product design and development. In order to achieve the application of machine learning based 3D image processing technology in cultural and creative product design, this article collects a large amount of 3D image data as training samples and uses machine learning algorithms to establish a training model. Considering the characteristics of the data and the processing objectives, these 3D image data are used as a training set and trained through machine learning algorithms to learn the features and patterns of the image. After the training is completed, use this model to process the new 3D image. By inputting new image data, the model automatically extracts image features and predicts and processes them based on previously learned patterns. Evaluate the advantages and disadvantages of machine learning based methods in processing complex 3D images by comparing experimental results. The experimental results show that machine learning based 3D image processing technology has high accuracy and efficiency in the design of cultural and creative products. Compared with traditional methods, this method can better process complex 3D images and provide better design effects.

Campus Companion : Creating a Supportive Chat – Assistant for Students

This article explores the creation and usage of chatbots, or intelligent conversational agents, for online communication. Python and other machine learning methods are employed in the chatbot's design. To be more explicit, TensorFlow/Keras is used for natural language processing, MySQL is utilized for database administration, and Flask is used for web hosting. The initiative attempts to accomplish its goals by enhancing the user experience and fostering more effective communication on the website. Using massive datasets, the chatbot will be educated to comprehend users' objectives and offer replies that fit with them. Natural language processing technologies, including voice recognition, natural language interpretation, and natural language production, will be used to foster natural discussions. A number of machine learning approaches, such as transformers, attention processes, and recurrent neural networks, will be investigated in order to classify intent and deliver replies. The objective is to construct a powerful and versatile chatbot that can grasp arguments in their context, preserve the debate's status, and answer in a manner that sounds human. Assessment criteria such as accuracy, recall, and precision will be utilized to develop and adapt the chatbot. Numerous businesses, including education, entertainment, customer service, and other disciplines, may profit from the employment of this chatbot. The implementation of intelligent conversational interfaces on websites has the potential to greatly enhance user experience. Keywords— Chatbot, Flask, MySQL, TensorFlow, Keras, Website Interaction, Natural Language Processing (NLP), Conversational AI, Intent Classification, Response Generation, Machine Learning Models, Recurrent Neural Networks (RNNs), Transformer Models, Attention Mechanisms, User Experience (UX), Contextual Conversations, Dialogue State Management, Human-like Responses, Customer Service Automation, Educational Chatbot, Entertainment Applications.

The Expanding Variety of Media and Entertainment Applications of Artificial Intelligence

The Expanding Variety of Media and Entertainment Applications of Artificial Intelligence

A Comprehensive Framework for Face age Synthesis, Voice Transformation, and Cartoon Generation

This paper presents a comprehensive framework addressing age-invariant face recognition (AIFR), face age synthesis (FAS), voice transformation, and avatar generation. Traditional AIFR techniques focus on minimizing age-related variations in face recognition but lack visual interpretability, while FAS methods aim to synthesize faces of different ages but often compromise recognition due to artifacts. To address these limitations, we propose MTLFace, a multi-task learning framework that simultaneously handles AIFR and FAS tasks. MTLFace employs attention-based feature decomposition to separate identity and age-related features spatially, improving interpretability. Additionally, we introduce an identity conditional module for fine-grained face age synthesis, enhancing the naturalness of synthesized faces. Unlike conventional methods that achieve age group-level synthesis, our identity conditional module enables identity-level synthesis, resulting in smoother age transitions and preserving individual facial characteristics. Leveraging high-quality synthesized faces, we enhance AIFR performance via selective fine-tuning, where synthesized faces are used to augment training data, leading to improved robustness against age variations. Furthermore, we contribute a large cross-age face dataset with annotations, facilitating research in age-related tasks. In addition to AIFR and FAS, we explore limitations in existing methodologies for voice transformation and propose advancements to improve speech quality and address current challenges. Lastly, we introduce a novel approach for cartoon face generation, utilizing component-based facial feature extraction and template matching to produce diverse and stylistic cartoon faces. Experimental results across various benchmarks demonstrate the effectiveness of our approaches in achieving superior performance in face recognition, voice transformation, and cartoon face generation, thereby contributing to the advancement of multimodal synthesis technologies with potential applications in entertainment, virtual communication, and assistive technologies

Perlindungan Penggunaan Logo Stasiun TV oleh Konten Kreator Tiktok di Kota Medan Perspektif Fatwa MUI

The development of entertainment application technology is very rapid making people follow the trend of content creators to voice their ideas, has both positive and negative impacts. The positive change is that people can express ideas about traveling, cooking, and even creating their own comedy. The parody carried out by one of the Tiktok platforms has violated IPR by using the Indosiar TV station logo as parody material whose content is not in accordance with the purpose of the series being parodied. The purpose of this article is to describe MUI Fatwa No. 1/MUNAS/5/2005 regarding the use of the Indosiar TV logo by Medan Tiktokers. This research is normative legal research. The results of this research show violations committed by Medan Tiktokers regarding the use of the Indosiar TV logo in their content in terms of MUI Fatwa No. 1/MUNAS/5/2005 constitutes a violation of design rights, patent rights, trademark rights and copyright. If we look at the content, it is stated that Tiktokers have committed acts by using, distributing, plagiarizing, and reproducing the Indosiar TV logo for commercial purposes and it is declared haram. This research recommends that Indosiar TV take legal action against individuals who parody their series using the TV logo

Streamlining LEGO® model design: an automated optimisation approach

LEGO® models have become increasingly popular beyond their traditional toy and entertainment applications, and are now used in fields such as education, cognitive science, engineering and robotics. However, designing LEGO® models for such diverse applications poses new challenges in terms of automation and optimisation. To address such issues, this paper proposes a design support system that automatically generates LEGO® models from any 3D model of a target object using a specific set of standard LEGO® bricks in a layer-by-layer fashion. The system converts the 3D model into a LEGO® model using the minimum number of LEGO® bricks required. The core of the proposed system lies in the use of a greedy algorithm for merging optimisation of each layer of bricks and a depth-first search algorithm for optimising the connection between multiple layers of bricks. Experimental results on several 3D graphics validate the effectiveness of the proposed methodology and its ability to output the corresponding LEGO® model and detailed layout illustrations for each layer of bricks. The proposed system can provide LEGO® builders with an automated optimisation approach for designing LEGO® models, minimising errors compared to manual design.

Mind the Gap: Learning Modality-Agnostic Representations With a Cross-Modality UNet.

Cross-modality recognition has many important applications in science, law enforcement and entertainment. Popular methods to bridge the modality gap include reducing the distributional differences of representations of different modalities, learning indistinguishable representations or explicit modality transfer. The first two approaches suffer from the loss of discriminant information while removing the modality-specific variations. The third one heavily relies on the successful modality transfer, could face catastrophic performance drop when explicit modality transfers are not possible or difficult. To tackle this problem, we proposed a compact encoder-decoder neural module (cmUNet) to learn modality-agnostic representations while retaining identity-related information. This is achieved through cross-modality transformation and in-modality reconstruction, enhanced by an adversarial/perceptual loss which encourages indistinguishability of representations in the original sample space. For cross-modality matching, we propose MarrNet where cmUNet is connected to a standard feature extraction network which takes as inputs the modality-agnostic representations and outputs similarity scores for matching. We validated our method on five challenging tasks, namely Raman-infrared spectrum matching, cross-modality person re-identification and heterogeneous (photo-sketch, visible-near infrared and visible-thermal) face recognition, where MarrNet showed superior performance compared to state-of-the-art methods. Furthermore, it is observed that a cross-modality matching method could be biased to extract discriminant information from partial or even wrong regions, due to incompetence of dealing with modality gaps, which subsequently leads to poor generalization. We show that robustness to occlusions can be an indicator of whether a method can well bridge the modality gap. This, to our knowledge, has been largely neglected in the previous works. Our experiments demonstrated that MarrNet exhibited excellent robustness against disguises and occlusions, and outperformed existing methods with a large margin (>10%). The proposed cmUNet is a meta-approach and can be used as a building block for various applications.

Contextualized Relation Predictive Model for Self-Supervised Group Activity Representation Learning

Group activity analysis has attracted remarkable attention recently due to the widespread applications in security, entertainment and military. This paper targets at learning group activity representations with self-supervision, which differs from the majorities relying heavily on manually annotated labels. Moreover, existing Self-Supervised Learning (SSL) methods for videos are sub-optimal to generate such representations because of the complex context dynamics in group activities. In this paper, an end-to-end framework termed Contextualized Relation Predictive Model (Con-RPM) is proposed for self-supervised group activity representation learning with predictive coding. It involves the Serial-Parallel Transformer Encoder (SPTrans-Encoder) to model the context of spatial interactions and temporal variations, and the Hybrid Context Transformer Decoder (HConTrans-Decoder) to predict the future spatio-temporal relations guided by holistic scene context. Additionally, to improve the discriminability and consistency of prediction, we introduce a united loss integrating group-wise and person-wise contrastive losses in frame-level as well as the adversarial loss in global sequence-level. Consequently, our Con-RPM learns robust group representations via describing temporal evolutions of individual relationships and scene semantics explicitly. Extensive experimental results on downstream tasks indicate the effectiveness and generalization of our model in self-supervised learning, and present state-of-the-art performance on the Volleyball, Collective Activity, VolleyTactic, and Choi's New datasets.