Entertainment Applications Research Articles

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.

Instrument Emotion Recognition from Polyphonic Instrumental Music using MFCC and CENS Features with Deep Neural Networks

The music digital data size is escalating in a manner where efficiently organizing and retrieving the data based on various parameters become tedious. Instrument music emotion recognition (IER) can be effectively utilized to recognize the emotion from instrumental music using machine learning methods. It has wide range of applications in music industry, psychology, medicine, and entertainment. Extracting or selecting suitable features that impact emotion recognition is one of the main tasks in IER. In this work, Mel Frequency Cepstral Coefficients (MFCC) and Chroma Energy Normalized Statistics (CENS) features have been employed to provide information to recognize the emotion pattern in instrumental music. These features have been trained using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) which captures the high dimensional emotion patterns. A recognition rate of 89.7% and an equal error rate of 10.3% have been attained using CNN by combining the features. The experiment results depict that the recognition rate of each instrument-emotion class and the overall performance is improved by the score level fusion of the MFCC and CENS features compared to individual features.

Time–space–frequency feature Fusion for 3-channel motor imagery classification

Low-channel EEG devices are crucial for portable and entertainment applications. However, the low spatial resolution of EEG presents challenges in decoding low-channel motor imagery. This study introduces TSFF-Net, a novel network architecture that integrates time–space–frequency features, effectively compensating for the limitations of single-mode feature extraction networks based on time-series or time–frequency modalities. TSFF-Net comprises four main components: time–frequency representation, time–frequency feature extraction, time–space feature extraction, and feature fusion and classification. Time–frequency representation and feature extraction transform raw EEG signals into time–frequency spectrograms and extract relevant features. The time–space network processes time-series EEG trials as input and extracts temporal–spatial features. Feature fusion employs Maximum Mean Discrepancy (MMD) loss to constrain the distribution of time–frequency and time–space features in the Reproducing Kernel Hilbert Space, subsequently combining these features using a weighted fusion approach to obtain effective time–space–frequency features. Moreover, few studies have explored the decoding of three-channel motor imagery based on time–frequency spectrograms. This study proposes a shallow, lightweight decoding architecture (TSFF-img) based on time–frequency spectrograms and compares its classification performance in low-channel motor imagery with other methods using two publicly available datasets. Experimental results demonstrate that TSFF-Net not only compensates for the shortcomings of single-mode feature extraction networks in EEG decoding, but also outperforms other state-of-the-art methods. Overall, TSFF-Net offers considerable advantages in decoding low-channel motor imagery and provides valuable insights for algorithmically enhancing low-channel EEG decoding.

An emotion recognition system: bridging the gap between humans-machines interaction

Human emotion recognition has emerged as a vital research area in recent years due to its widespread applications in psychology, healthcare, education, entertainment, and human-robot interaction. This research article comprehensively analyzes a machine learning-based six-emotion classification algorithm, focusing on its development, evaluation, and potential applications. The study aims to assess the algorithm's performance, identify its limitations, and discuss the importance of selecting appropriate image descriptors for accurate emotion classification. The algorithm achieved an overall accuracy of 92.23\%, showcasing its potential in various fields. However, the classification of specific emotions, particularly ``excited'' and ``afraid'', demonstrated some limitations, suggesting further refinement. The study also highlights the significance of choosing suitable image descriptors, with the manual distance calculation used in the framework proving effective. This article offers insights into developing and evaluating a six-emotion classification algorithm using a machine learning framework, emphasizing its strengths, limitations, and possible applications in multiple domains. The findings contribute to ongoing efforts in creating robust, accurate, and versatile emotion recognition systems that cater to the diverse needs of various applications across psychology, healthcare, robotics, education, and entertainment.

Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography

The safeguarding of intellectual property and maintaining privacy for video content are closely linked to the effectiveness of security protocols employed in internet streaming platforms. The inadequate implementation of security measures by content providers has resulted in security breaches within entertainment applications, hence causing a reduction in the client base. This research aimed to enhance the security measures employed for video content by implementing a multi-key approach for encryption and decryption processes. The aforementioned objective was successfully accomplished through the use of hybrid methodologies, the production of dynamic keys, and the implementation of user-attribute-based techniques. The main aim of the study was to improve the security measures associated with the process of generating video material. The proposed methodology integrates a system of mathematical equations and a pseudorandom key within its execution. This novel approach significantly augments the degree of security the encryption mechanism provides. The proposed methodology utilises a set of mathematical equations that are randomly employed to achieve encryption. Using a random selection procedure contributes to the overall enhancement of the system’s security. The suggested methodology entails the division of the video into smaller entities known as chunks. Following this, every segment is subjected to encryption using unique keys that are produced dynamically in real-time. The proposed methodology is executed via Android platforms. The transmitter application is tasked with the responsibility of facilitating the streaming of the video content, whereas the receiver application serves the purpose of presenting the video to the user. A careful study was conducted to compare and contrast the suggested method with other similar methods that were already in use. The results of the study strongly support the safety and dependability of the procedure that was made available.

One-Dimensional Convolutional Multi-branch Fusion Network for EEG-Based Motor Imagery Classification

The Brain-Computer Interface (BCI) system based on motor imagery (MI) is a hot research topic nowadays, which can control external devices through the brain and has a wide range of applications in rehabilitation, gaming, and entertainment. Due to the non-smooth, non-linear, and low signal-to-noise ratio of the MI EEG signal, it is challenging to accurately decode the MI task intention. A new end-to-end deep learning method is proposed to decode raw MI EEG signals without preprocessing, such as filtering and feature reinforcement. The 1D convolution is used to learn the time-frequency features in MI signals, and a four-branch fusion network is used as the main body to add a 1D CNN-AE block and 1D SE-block to enhance the algorithm's performance. Experiments on two publicly available datasets demonstrate that our proposed algorithm outperforms the current state-of-the-art methods. It achieves 86.11% and 89.51% on the BCI Competition IV-2a and the BCI Competition IV-2b datasets, respectively, and a 6.9% improvement in the generalizability test. The proposed data enhancement method can effectively alleviate the overfitting of the algorithm and improve the decoding performance. Further analysis shows that 1D convolution can effectively extract the features associated with the MI task.

SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data

Accurate and reliable human motion reconstruction is crucial for creating natural interactions of full-body avatars in Virtual Reality (VR) and entertainment applications. As the Metaverse and social applications gain popularity, users are seeking cost-effective solutions to create full-body animations that are comparable in quality to those produced by commercial motion capture systems. In order to provide affordable solutions though, it is important to minimize the number of sensors attached to the subject’s body. Unfortunately, reconstructing the full-body pose from sparse data is a heavily under-determined problem. Some studies that use IMU sensors face challenges in reconstructing the pose due to positional drift and ambiguity of the poses. In recent years, some mainstream VR systems have released 6-degree-of-freedom (6-DoF) tracking devices providing positional and rotational information. Nevertheless, most solutions for reconstructing full-body poses rely on traditional inverse kinematics (IK) solutions, which often produce non-continuous and unnatural poses. In this article, we introduce SparsePoser, a novel deep learning-based solution for reconstructing a full-body pose from a reduced set of six tracking devices. Our system incorporates a convolutional-based autoencoder that synthesizes high-quality continuous human poses by learning the human motion manifold from motion capture data. Then, we employ a learned IK component, made of multiple lightweight feed-forward neural networks, to adjust the hands and feet toward the corresponding trackers. We extensively evaluate our method on publicly available motion capture datasets and with real-time live demos. We show that our method outperforms state-of-the-art techniques using IMU sensors or 6-DoF tracking devices, and can be used for users with different body dimensions and proportions.

Push mechanism and its applications in learning and entertainment

Nowadays, people often use the push mechanism in daily life, especially on websites and the applications such as video applications. People often spend a lot of time using it because of the push mechanism. The appearance of the push mechanism deeply affects peoples daily life whether in socializing or entertainment, but most people dont know how the push mechanism works. This paper uses the literature analysis method, case study and review to focus on the development of the push mechanism, principle and its applications. The purpose of this paper is to increase public awareness of the application of the push mechanism and its principle. It can be found that the push mechanism is effective and helpful for all people whether what the identity of the people is.

Research on brand cultivation and promotion of cultural and creative products with regional characteristics in Shanxi based on DEA model

Abstract This paper efficiently develops cultural and creative product design based on the DEA model, using input-oriented super-efficient expression data and the input surplus of the first stage as the explanatory variable to measure the efficiency value of cultural and creative products. A pre-determined control level conversion value is used to measure the initial standard value of cultural and creative inefficiency, and a further generalized likelihood ratio test is used. It was found that the total revenue of cultural and creative products increased from 1.172 million yuan to 1.784 million yuan, the liking of cultural and creative products clothing series was significantly high as 98%, and the highest scale efficiency of network entertainment application was 0.827. Enhancing local cultural identity and improving national cultural confidence can be achieved by implementing cultural and creative product design based on the DEA model with Shanxi regional characteristics.

Binaural effects and rendering of edge diffraction in geometrical acoustics

Virtual acoustic environments have many applications in entertainment, architectural planning, and hearing research. Geometrical acoustics (GA) is commonly used, offering high computational efficiency by assuming ray-like sound propagation. However, GA does not account for wave-related effects such as edge diffraction, leading to perceptually dissatisfactory results or discontinuities, e.g., when a sound source is occluded. GA can be extended by constructing edge-diffracted sound paths. Recently, we have suggested the universal diffraction filter approximation (UDFA) to model the spectral effects of diffraction from infinite and finite edges and objects composed thereof using highly efficient recursive filters. This contribution investigates how edge diffraction affects the spatial perception of a sound source in conditions where a flat plate occludes the direct sound or produces a reflection. Binaural recordings were obtained with a head and torso simulator at discrete points of a sound source trajectory in the vicinity of a flat plate, including conditions near the incident and reflection shadow boundary. In a listening test, the perceived source location was matched for conditions with and without plate. Differences in localization were observed, and an approach for binaural rendering of simulated diffraction in the GA context is suggested, replicating the localization results obtained for the dummy-head recordings.

ONE SHOT FACE STYLIZATION USING GANS

One-shot face stylization is an interesting and challenging subject in computer vision and deep learning. This work deals with the art of manipulating a target face using a reference image as inspiration, which requires controlling facial recognition while specifying important style characteristics This project has attracted a lot of interest due to its potential applications in digital art, entertainment, and personal products. In this abstract, we examine the important features of a one- shot face stylization. Deep neural networks, especially generative adversarial networks (GANs), are widely used in the process to generate customized facial images. These networks are trained on data structures that combine the target and reference faces, with the reference image acting as a strategic identifier. The success of a one-shot facial lies in the meticulous execution of the fading process, which strikes a balance between preserving identity and improving technique. These disadvantages typically include a combination of manpower retention, strategic formation, emotional quality, and enemy training. In conclusion, advances in this field have the potential to transform creative expression and personalization across industries from digital art and animation to virtual avatars and social media filters. Key Words: Facial recognition, generative adversarial networks, virtual avatar, image to image transfer.

Electronic exoneuron based on liquid metal for the quantitative sensing of the augmented somatosensory system

The increasing demands in augmented somatosensory have promoted quantitative sensing to be an emerging need for athletic training/performance evaluation and physical rehabilitation. Neurons for the somatosensory system in the human body can capture the information of movements in time but only qualitatively. This work presents an electronic Exo-neuron (EEN) that can spread throughout the limbs for realizing augmented somatosensory by recording both muscular activity and joint motion quantitatively without site constraints or drift instability, even in strenuous activities. Simply based on low-cost liquid metal and clinically used adhesive elastomer, the EEN could be easily fabricated in large areas for limbs. It is thin (~120 μm), soft, stretchable (>500%), and conformal and further shows wide applications in sports, rehabilitation, health care, and entertainment.

The Marketing Strategy of Popular Short Video Application -- Taking TikTok as an Example

As technology rapidly developed, many entertainment applications occurred on the internet. Competition among various entertainment software is fierce, Chinese TikTok (also named Douyin) become the most popular entertainment application of its excellent marketing strategy in recent years. Meanwhile, TikTok also revealed many disadvantages which make the network society become chaotic. This passage combing some literature and the history of TikTok analyses the marketing strategies and existing problems of this product. The research found that some marketing strategies of TikTok are product strategy, price strategy, promotion strategy, and content strategy. This research also provides some advice on the development of the company.

Programming microcontrollers through high-level abstractions: The OMicroB project

In this paper, we present an approach for programming microcontrollers that provides more expressivity and safety than the low-level language approach traditionally used to program such devices. To this end, we provide various abstraction layers (abstraction of the microcontroller, of the electronic components of the circuit, and of concurrency) which, while being adapted to the scarce resources of the hardware, offer high-level programming traits for the development of embedded applications. The various presented abstractions make use of an OCaml virtual machine, called OMicroB, which is able to run on devices with limited resources. These take advantage of the expressivity and safety of the OCaml language (parameterized modules, advanced type system). Its extensibility allows to define a synchronous extension to manage concurrency while keeping a good level of efficiency at execution. We illustrate the value of our work on both entertainment applications and embedded software examples.

Peran Harga dan Citra Merek dalam Mediasi Pengaruh E-Word to Mouth terhadap Keputusan Pembelian melalui Aplikasi TikTok

Currently, people in Indonesia need a way to shop that is easy and also reliable. The existence of the TikTok application is the answer to this problem in addition to its main function as an entertainment application that can be used with the Android or IOS application operating system. This study aims to determine whether price, brand image affect purchase intention by e-Word of Mouth mediation in using the TikTok application. This research method uses a quantitative approach by distributing questionnaires to 130 consumers as measured by a scale of 1 to 5. Smart PLS (Partial Least Squares) is used in the analysis of this study to test external and internal models. The results of this study indicate that price, brand image have a significant impact on purchase intention. Through mediation variables e-Word of Mouth. So that the TikTok application can answer consumer questions and problems, simplify, and increase daily purchases. Nowadays, people need a way to shop that is easy, with prices and products that are suitable to save time and additional costs.

Fast-response and durable liquid metal wire for wearable electronics

This work presents a strain sensor which is based on high ductility liquid metal coated on traditional commercial lastex yarns with a unique spiral wound structure. The prepared sensor exhibits exceptional flexibility, elasticity, softness of 0.23 N at 100% tensile strain. It is lightweight, weighing only 1 g/m, and boasts a low cost of less than 1 dollar. The sensor has the ability to detect both static and dynamic in-plane tension and normal pressure. It showcases remarkable sensing characteristics of good sensitivity (0.82 for tension, 12.6 N−1 for pressure), extraordinary linearity within 10–100% strain range with goodness-of-fit value over 0.997, rapid response of less than 10 ms, high resolution spanning up to 0.3%, and high durability over 100,000 repetitive cycles. A smart glove based on the proposed strain sensors achieves gesture recognitions using machine learning technique, with an accuracy of 100%. The outstanding performance of this sensor highlights its potential as an affordable and advanced candidate for wearable electronics, which can have wide-ranging applications in sports, healthcare, entertainment, education, and industry.

Being where, with whom, and when it happens: spatial, interpersonal, and temporal presence while viewing live streaming of collegiate sports in virtual reality

Introduction: Although virtual reality (VR) is most popularly known for its applications to gaming, other entertainment applications are increasingly being explored including in the sports media industry, but little research has so far examined the experiences induced by VR viewing of a live sporting event.Materials and methods: Participants (n = 93) were university students who were approached in the context of a field study from a nearby community eatery area on the university campus to watch brief segments of a 360° live stream of the home games of their university volleyball and basketball teams both while wearing and not wearing an inexpensive smart-phone based head-mounted display (HMD). Immediately afterward, participants then reported on their relative experience of spatial, interpersonal, and temporal presence, as well as their satisfaction-preference with each of the two viewing modalities, in response to brief face-valid screening questions.Results: The majority of participants experienced greater presence while wearing the VR headset, and approximately one in every two reported preferring to watch the games in VR. Participants’ experience of spatial presence independently correlated with preferring to watch the games in VR.Discussion: Media vendors should offer VR viewing of sports including via inexpensive, smart-phone mediated VR as an additional, cost-effective alternative means of heightening fans’ experience of virtual presence at the games when fans are unable to go to the games in person.

Age and Gender Detection Using Deep Learning

Age and gender detection using deep learning with Convolutional Neural Networks (CNNs) is a popular computer vision task that involves predicting the age and gender of a person from their image. CNNs are powerful deep learning algorithms that are well-suited for image classification tasks. In this task, a CNN model is trained on a large dataset of labeled images, where each image is annotated with the corresponding age and gender of the person. The CNN model learns to extract features from the input image and uses these features to predict the age and gender. Age and gender detection using deep learning with CNNs has various applications in different domains, including surveillance, security, healthcare, and entertainment.