Embedded resources have been becoming part of the Internet of Things networks, where they are increasingly taking part in various kinds of decision-making using Tiny Machine Learning (TinyML) models. Although offloading the TinyML model for these devices includes removing many layers that have less impact on the overall performance, they often lead to a sacrifice on the overall performance of the model. In this paper, we propose a novel device-aware training strategy to customize the training based on the resources on which the model will be applied. We proposed AlphaEnhancer, a resource-aware game agent for medical image super-resolution. We baseline our approach on the Residual Feature Distillation Model (RFDN) and propose a device efficacy metrics, which is based on the learned actions of the agent. The model with the highest efficacy is deemed appropriate for that particular device. Our preliminary results show that our methods performed significantly well with respect to the baseline and other recent state-of-the-art.

The 5th industrial revolution is characterized by an extensive interconnection of embedded devices, which offer a range of services, including the monitoring of their environments. Images captured from remote cameras require enhancements for effective analysis. Despite recent progress in single-image super-resolution techniques by yielding impressive results through deep convolutional neural networks, the complexity of these advanced models renders them impractical for use on miniaturized Internet of Things (IoT) devices, primarily due to their limited computational capabilities and memory constraints. Furthermore, the rapid evolution of IoT devices necessitates efficient image super-resolution techniques, while existing advanced methods, based on deep convolutional neural networks, are too resource-intensive for these devices, and this gap highlights the need for a more suitable solution. In this study, we introduce a lightweight, efficient super-resolution model specifically designed for IoT devices. This model incorporates a novel deep residual feature distillation block (DRFDB), which leverages a depthwise-separable convolution block (DCB) for effective feature extraction. The focus is on reducing computational and memory demands without compromising on image quality. The proposed DCB extracts coarse features from given input features as calculation units, using two operations, depthwise and pointwise convolutions. These two operations are able to significantly reduce the number of parameters and floating-point operations while maintaining a PSNR value higher than the 90% threshold. We modify the proposed DCB and introduce a multi-kernel depthwise-separable convolution block (MKDCB) to fine-tune the model. The experiments, conduct on various standard datasets such as DIV2K, Set5, Set14, Urban100, and Manga109 by demonstrating that our model significantly outperforms existing methods in terms of both image quality and computational efficiency. The model shows improved performance metrics like PSNR, while requiring fewer parameters and less memory usage, making it highly suitable for IoT applications. This study presents a breakthrough in super-resolution for IoT devices, balancing high-quality image reconstruction with the limited resources of these devices.

The gaming industry, which was among the industries least affected by the COVID-19 outbreak, exhibited positive growth trends during the COVID-19 period. This paper explores the impact of COVID-19 on the gaming industry by analyzing news texts from 2020 to 2022 using text mining and LDA (latent Dirichlet allocation) topic classification, and visualizing charts. The study focuses on three themes, namely the current situation, the future, and possible problems of China’s game industry in the post-epidemic era. The findings of this study suggest that the development of the game industry during the COVID-19 outbreak prompted the government to regulate policies and promote the transformation of game companies, which had a positive impact on the development of China’s game industry. However, this study also found that due to the effects of COVID-19, society and the government have increased their focus on the time management of underage game users, which poses a significant challenge to the games industry. This paper recommends improvements from three perspectives, namely society, policy, and enterprise, with the aim of contributing to the long-term development of China’s game industry.

Strong efforts have been undertaken to enhance the diagnosis and identification of diseases that cause facial paralysis, such as Bell's palsy and stroke, because of their detrimental social effects. Stroke is one of the most serious and potentially fatal conditions among the major cardiovascular disorders. We are introducing a deep-learning-based method for early diagnosis of facial paralysis diseases such as stroke and Bell's palsy. Recognizing the costs associated with traditional diagnostic techniques like magnetic resonance tomography (MRI) and computed tomography (CT) scan images, our model employs a multi-task network, integrating face parsing, facial asymmetry parsing, and category enhancement. Spatial inconsistencies are addressed via a depth-map estimation module that leverages an instance-specific kernel approach. To clarify the boundaries of facial components, we use category edge detection with a foreground attention module, generating generic geometric structures and detailed semantic cues. Our model is trained on two datasets, comprising individuals with regular smiles and those with one-sided facial weakness. This cost-effective, easily accessible solution can streamline the diagnostic process, minimizing data gaps, and reducing needless rescreening and intervention costs.

Internet of things has been one of the predominant research areas for the past two decades. Many application domains have embraced it to solve challenges that have long been considered hurdles. A recent trend in information communication technologies is integrating miniature sensing devices to elevate the experience in serious games. Serious games are games whose sole aim is not entertainment but rather to serve as a source of information and learning in a playful manner. Serious games are becoming one of the sizzling literature topics and are applied to every part of human lives, such as education, healthcare, and physical training, to name a few. Internet of Things, the biggest provider of modern-day games via personal mobiles, can be utilized to design serious games. However, deploying serious games in the Internet of Things environment engenders new challenges. This paper aims to provide a comprehensive survey on Internet of things-enabled serious games and investigate the challenges towards their realization. First, we highlight serious game domains and spot the evolution and motivation that lead to Internet-of-Things-enabled Serious Games. Later, we classify the state-of-the-art by devising a comprehensive taxonomy. In the end, we present numerous unaddressed open challenges in the current form of the state-of-the-art and identify future directions.

Metaverse technology is expanding to industries in various fields, such as medical, national defense, and education, and training simulation programs have been mainstream so far. However, there have been increasing attempts to apply metaverse content to web-based platforms linked to social media services and, as a result, we face the problem of access to web-based metaverse content. Unlike traditional content, metaverse content interacts with many users, so content accessibility is the first important part to consider. In other words, to maximize the quality of metaverse content, it is essential to pull out the optimal UX through a detailed HCI (human computer interaction) design. Metaverse content development methodologies have effective methods proposed by many researchers. However, they are limited to web-based metaverse content that limits the use of high-end hardware. They are ineffective for platforms such as PCs and VR devices, as most studies focus on improving the visual performance of PCs or high-performance VR devices. Therefore, unlike existing research, the key theme of our research is to study optimized development standards that can be applied to web-based metaverse content and find out their effects through experiments. We created a development standard to be applied to a Web-based platform based on the existing metaverse content development methodology. Then, we redeveloped the VR content into the metaverse content and named them the VR build and the metaverse build. We had 25 people play virtual reality builds and metaverse builds simultaneously. Then, we measured the overall experience with an evaluation tool called the Game Experience Questionnaire (GEQ); the GEQ is a proven tool for evaluating content experiences by dividing them into positive/negative scales. When comparing the results measured from the two builds, the metaverse build showed consistent results with a higher positive scale, and a lower negative scale, than the VR build. The results showed that users indeed rated metaverse content positively. The bottom line is that the web-based metaverse content development standards that we have produced are practical. However, since generalization is limited, continuous research will be needed in more experimental groups in the future.

Although the birthrate is declining, the average life expectancy continues to increase. Therefore, it is more important for elderly people to maintain their independence while staying at home. Ambient Assisted Living (AAL) includes the use of devices and methods of ensuring that elderly people can stay safe and age at home rather than at a facility. Assisted living services help people live as independently and safely as possible when they can no longer perform everyday activities on their own. Because the information transmitted in AAL systems is personal, the security and privacy of such data are becoming important issues that must be addressed. Herein, we propose an efficient and secure authentication scheme for an AAL system. Our proposed authentication scheme not only satisfies several important security requirements of such a system but also withstands various types of attacks. Moreover, the proposed authentication scheme achieves lightweight performance by manipulating basic cryptographic operations including bitwise-eXclusive-OR (XOR) and hash functions. We simulated our proposed authentication scheme using Automated Validation of Internet Security Protocols and Applications (AVISPA), which is a prominent security verification tool. Security and performance analysis show that our proposed scheme is not only robust against several attacks and has a lower computational cost in terms of execution time than those of existing authentication schemes.

Face parsing refers to the labeling of each facial component in a face image and has been employed in facial stimulation, expression recognition, and makeup use, effectively providing a basis for further analysis, computations, animation, modification, and numerous other applications. Although existing face parsing methods have demonstrated good performance, they fail to extract rich features and recover accurate segmentation maps, particularly for faces with high variations in expression and sufficiently similar appearances. Moreover, these approaches neglect the semantic gaps and dependencies between facial categories and their boundaries. To address these drawbacks, we propose an efficient dilated convolution network with different aspect ratios to attain accurate face parsing of the output by applying the feature extraction capability. The proposed network-structured multiscale dilated encoder–decoder convolution model obtains rich component information and efficiently improves the capture of global information by obtaining low- and high-level semantic features. To achieve a delicate parsing output of the face components along the borders and analyze the connections between the face categories and their border edges, the semantic edge map is learned using a conditional random field, which aims to distinguish border and non-border pixels during the modeling. We conducted experiments using three well-known publicly available face databases. The recorded results demonstrate the high accuracy and capacity of the proposed method in comparison to previous state-of-art methods. Our proposed model achieved a mean accuracy of 90% on the CelebAMask-HQ dataset for the category case and 81.43% for the accessory case, and achieved accuracies of 91.58% and 92.44% on the HELEN and LaPa datasets, respectively, thereby demonstrating its effectiveness.

In general, although most data existing in the WWW are modeled as some type of relational database, characteristically even information in the same domain is often structured in different ways depending on the individual manager. In order to materialize a semantic Web, a method to efficiently map these different kinds of data is needed. This paper presents an algorithm for the matching between a relational database and domain ontology. Existing studies on the matching between a relational database and domain ontology were performed by extracting local ontology from a relational database. However the problem of losing domain information remains as a correlation with domain ontology is not used in the process of extracting local ontology. As a solution to this problem, we attempted to prevent the information loss through the measurement of similarity between the instances of the relational database and domain ontology, and increased the efficiency of the matching process by using information on the relation between tables in the relational database and also the relation between classes in ontology.

Accurate measurement of poses and expressions can increase the efficiency of recognition systems by avoiding the recognition of spurious faces. This paper presents a novel and robust pose-expression invariant face recognition method in order to improve the existing face recognition techniques. First, we apply the TSL color model for detecting facial region and estimate the vector X-Y-Z of face using connected components analysis. Second, the input face is mapped by a deformable 3D facial model. Third, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector and action unit of expression. Finally, the damaged regions which occur during the process of normalization are reconstructed using PCA. Several empirical tests are used to validate the application of face detection model and the method for estimating facial poses and expression. In addition, the tests suggest that recognition rate is greatly boosted through the normalization of the poses and expression.

This paper deals with the efficiency of route services in mobile environments with necessitate methods which reduce map description data and increase user's understanding of the map. In order to reduce the size of map data, first, the data is aligned by importance and incrementally serviced to the user by importance. In addition, the size of the map data can be reduced by using symbols which represent data in a concise manner. Second, to increase the user's understanding about route (optimal path showing start and destination) and its surroundings, we propose a new method which converts a complex route and its surroundings into a simplified sketch map. A process is also suggested to correct distortion which occurs during the sketch map generation process. For speedy service in mobile environments, a new indexing method, leveled spatial indexing, is proposed in this paper. Current spatial indexing methods do not support all the map generalization operations and real-time processing. The indexing method proposed in this paper supports all the map generalization operations and manipulates zoom in-out quickly by leveling the generalized data. To verify the efficiency of the methods proposed in this paper, several experiments using cellular phones are conducted and the results of performance evaluation are presented.

Recognition of 3D objects using computer vision is complicated by the fact that geometric features vary with view orientation. An important factor in designing recognition algorithms in such situations is understanding the variation of certain critical features such as angles. In this paper we derive the two dimensional joint density function of two angles in a scene given an isotropic view orientation and an orthographic projection. The analytic expression for the densities are useful in determining statistical decision rules to recognize surfaces and objects. Experiments to evaluate the usefulness of the proposed methods are reported.