3D modeling by 3D scanning of real objects using laser scanners is effective for surveying disaster sites to confirm the extent of damage and for digital archiving of tangible cultural assets to preserve their shapes. In recent years, smartphones and tablets are equipped with distance measurement sensors, making it possible to easily perform 3D scanning. However, generating a 3D model by extracting only the necessary data that constitutes an object is not easy. The point cloud acquired by a ranging sensor consists of a very large number of points and includes many points other than the object and noise. It has generally been necessary for a person to visually designate target objects and unnecessary areas. However, the large data size, such as data from a wide area of the surrounding environment, increases the amount of work required. In addition, if the object has a complex shape, the process of defining the area of the shape itself is complicated. This requires careful attention to delete unnecessary areas and save necessary areas, making it difficult to specify the area appropriately. A 3D model with a large data size is undesirable for data transfer over information networks and for the execution of various processes. In this paper, we design an algorithm for generating lightweight 3D models with reduced data size based on point cloud surveying and demonstrate its effectiveness.
The research aims to find a strategy for how YouTube ads remain effective and watched and create persuasion to consumers in the midst of changing viewing behavior. The unit of analysis is advertising text in the form of documents and qualitative content analysis by Hsieh and Shannon. The study results show that the unique strategy of highlighting several advertising formulas, the prominence of voiceover, instructive sentences, and brand messages at the beginning of the broadcast can strengthen the persuasion of children's product ads on YouTube. The research implies that ad creators can create ads that can be persuasive to support marketing success. However, YouTube has video content with a busy audience, overloaded information, and active behavior. So, ads often need to be more readable because viewers skip the content by skipping and giving direct feedback. Future research should expand the scope to Instagram and TikTok, focusing on persuasion formulas and types of advertising messages.
This research aims to develop a model of digital leadership skills in moderating the relationship between Organizational Resources to improve the digital competence of health professionals. Digital transformation in the health sector requires new knowledge and competencies regarding integrating technology for its users, especially health professionals who are intended to support health service work practices. Often, literature on increasing digital competency of the workforce focuses on training and providing access to technology so that the expected benefits still need to be improved. Therefore, a model that involves digital leadership skills in maximizing organizational resources is needed to increase the digital competence of health professionals. This model was built based on a literature review and developed to become an updated model. A future research agenda has been presented primarily to test the validity of the model proposed in this study.
In recent years, there are many natural disasters associated with extreme weather events. Intensive and prolonged rainfall increases flooding and causing landslides. They may cause extensive damage to residential areas and infrastructure, increasing the need for rapid and appropriate evacuation action to prevent human casualties. Despite the fact that accurate information provision and rapid evacuation guidance are essential immediately after a disaster, the fast and wide area restoration of networks is very important in order that different entities communicate together. Wireless Mesh Networks (WMNs) are communication infrastructure, which use multiple mesh routers for communication. Even some network links may fail, the WMN can maintain the network and communication through other mesh routers. Also, they can continuously provide stable wireless communication even in the event of a disaster. In this paper, we propose a mesh router placement optimization system for three-dimensional environments. In simulations, we consider Normal and Uniform distributions of mesh clients. The optimal placement of WMNs is performed to verify the usefulness of the proposed system.
In binary analysis, performing static analyses on architecture-agnostic intermediate representation is efficient and strongly demanded. Sound and accurate Low-Level Virtual Machine Intermediate Representation (LLVM IR) lifted from binary could make the reuse of dozens of existing analysis programs of the LLVM ecosystem possible. However, current binary lifters lack the resources to improve manually developed lifting rules and develop more of them. This work aims to solve the problem of lifting low-level language to sound high-level Intermediate Representation (IR) as a formal language translation problem, enabling automatic learning of binary lifting. Therefore, we propose a neural machine translation-based binary lifting framework named LEARNT with a parallel corpus generation method leveraging a compiler. The evaluation results show that LEARNT’s average translation accuracy is 93
Faced with the increasing demand for multi UAV collaboration and joint applications, the application of UAVs collaboration to complete specific tasks has received great attention. Therefore, it involves two important links: dynamic networking of UAVs and collaborative task execution. This requires dynamic network switching of UAVs from management network to task execution network. The existing identity authentication schemes for UAVs can no longer meet the dynamic construction and cross network identity authentication needs of UAVs. Therefore, this article proposes a cross network identity authentication scheme for unmanned aerial vehicle swarms based on layered blockchain technology, which divides the identity authentication of UAVs into two stages. The first stage is to establish a main blockchain for identity authentication during UAV group building, and the second stage is to establish a sub blockchain for identity authentication between UAVs during task execution. This hierarchical blockchain method based on the main chain sub chain can effectively achieve unified identity management of UAVs and effectively respond to various security threats in the construction and application of dynamic UAVs, in order to adapt to cross network UAV identity authentication.
Mood disorders, such as depression, manifest in various psychological and physical symptoms. Persistent feelings of sadness can disrupt daily functioning, while accompanying issues like insomnia and loss of appetite further exacerbate the condition. If left untreated, depression can lead to a range of complications, including severe illnesses and potentially life-threatening outcomes. In recent years, there has been a notable rise in psychiatric disorder cases, with a significant increase observed in mood disorders, particularly depression. Many individuals either fail to recognize their symptoms or are hesitant to seek professional help. As a result, only a fraction of affected individuals receive adequate treatment. This paper proposes a method for estimating depressive tendencies by analyzing observable data, such as conversational content between users and chatbots, as well as their usage patterns. By leveraging these insights, we aim to improve early detection and intervention strategies for individuals at risk of depression.
Trust is fundamental for the functioning of social networks because it impacts the user interactions, information reliability and overall network cohesion. This paper introduces a Fuzzy-based System for the Evaluation of User Trust (FSEUT) in social networks, which assesses trust using three key parameters: Attitude (At), Behavior (Bh), and Experience (Ep). We evaluated the system through computer simulations, and the results show a positive correlation between the input parameters and User Trust (UT). The increase of At, Bh, and Ep leads to corresponding increase in UT, demonstrating the system effectiveness for trust evaluation.
One of the biggest advancements in automotive technology that is revolutionizing transportation is autonomous driving. According to international standards, autonomous driving is categorized into levels 0 through 5. Currently, South Korea has commercialized Level 2 and is waiting for Level 3. Level 3 is the stage where the system controls everything under certain conditions and the driver intervenes in emergency situations. In autonomous driving, accidents due to system problems can occur at the moment when the driver does not intervene, and the manufacturer is responsible for them. Event Data Recorder and Data Storage System of Automated Driving can identify the cause of the accident, but the records are only visible to the manufacturer, making it difficult for consumers to access the information. This research aims to develop a CAN-based device that can accurately identify the driving mode and increase the safety and reliability of the vehicle by developing a network inside the car. The proposed system will improve the accuracy of driving mode identification and help solve the liability issue between consumers and manufacturers in the event of an accident.
Some types of Internet of Things data depend on the time and location at which they were generated. We refer to such data as spatio-temporal data (STD). To effectively utilize STD, we previously proposed an STD retention system called STD-RS that uses vehicles to retain STD within a specific area. In STD-RS, vehicles autonomously operate based on their location information. However, location information errors at vehicle nodes may lead to STD being distributed outside the target area. This could lead to an increase in packet loss and pose the risk of information leakage. Therefore, this study proposes a method for detecting vehicles with location information errors based on the attenuation of signal strength with distance. Specifically, the distance to vehicle nodes and the received signal strength obtained during STD distribution are collected and stored on multi-access edge computing servers. Machine learning is then applied to this information for detection. Simulations demonstrate that vehicles with location information errors can be detected with an accuracy of approximately 80
With the proliferation of digital economy, data fusion applications drive innovation across industries. Yet privacy protection poses challenges. In response to this issue, this paper proposes a vertical federated learning scheme based on logistic regression for collaboration between hospitals and schools to predict student depression risk. It utilizes Bind RSA for sample alignment without raw data sharing, Paillier homomorphic encryption for secure data transmission, and RSA for secure exchange of prediction values, ensuring the security and confidentiality of private data for both hospitals and schools. This scheme enhances depression prediction safety and efficiency, addresses privacy concerns, and provides useful guidance for data fusion applications in other fields.
The transformational power of e-prescription systems in healthcare is examined in this study, which also acknowledges their contribution to pharmaceutical process simplification. Yet, in centralized systems, new approaches are required to address security threats, like cyberattacks and illegal data manipulation. To overcome these obstacles, researchers suggest a decentralized e-prescription system based on self-sovereign identification theory and blockchain technology. In addition to addressing issues like fraud and identity theft, this system guarantees a safe prescription exchange, encouraging amongst healthcare players. It also gives patients more control by giving them access to their drug histories and prescription details, which promotes adherence to treatment programs and active participation. The study also looks at the variables that are causing the expansion of digital payments, such as government initiatives, one-touch payments, internet penetration, and non-banking financial institutions. The fiat and stable coin currencies benefit from the short-term stability of their purchasing power, which makes them appropriate for making purchases of goods and services. This paper examines the challenges brought about by fungible tokens’ volatile value in typical blockchains.
Changes in the transportation systems in Japan are long overdue, as the country continues to struggle with the aging population and the subsequent lack of workers across countless industries. Although the government recently introduced the “green slow mobility project” in an effort to address such issues, the continuation of these projects is in the hands of the individual municipalities, and the very survival of the compromised communities is at stake. The project aims to provide an environmentally friendly mode of transportation that can be sustained by the members of the targeted communities, so as not to have to hire younger workers who they are already at a shortage of. The difficulty in realizing such ideas lies in the scheduling of the vehicles, as well as in keeping it cost-efficient for both the local government and the users. The system described in this paper, in collaboration with Toyonaka city of Osaka prefecture, optimizes the bus route and schedule based on the passengers’ requests while incorporating a patrolling feature. While the on-demand bus system has been tried in numerous places both within and outside the country using various algorithms, patrolling is a function newly suggested by the local government and has yet to be tested elsewhere.
With the increase in remote work due to the impact of COVID-19, which started at the beginning of 2020, the use cases of the metaverse have expanded beyond games to include business applications. Smartphones equipped with ranging sensors are also becoming more popular, making the generation of 3D models by scanning real objects easier. As a result, data transfer of 3D models over information networks is becoming common. Although the speedup of information networks has made it possible to handle large 3D models, which were difficult in the past, using large 3D models of real objects in the metaverse is undesirable because it affects the performance and increases latency. Therefore, reducing the complexity and size of 3D models is necessary. One approach is to generate an approximate 3D shape using a combination of primitive blocks, such as LEGO bricks. In this paper, we propose an algorithm that reduces the amount of transferred data and decreases operational load in the metaverse by approximating 3D models of real objects with as few primitive blocks as possible. In addition, we evaluate the effectiveness of the proposed algorithm.
Digital Twin is expected to be a key technology for realizing the next-generation smart city. It can be applied to social infrastructure, manufacturing, and medical fields. This paper focuses on the concept of ‘Digital Twin City,’ which collects various types of data within a city and uses it for public services such as transportation, disaster prevention, and urban planning, thereby improving the quality of life for residents. Three-dimensional urban landscapes formed by point cloud data are essential for developing these public services and other applications in Digital Twin Cities. However, installing sensors and collecting fresh data from every corner of the city involves high initial and maintenance costs. Therefore, involving residents in data collection is considered a promising option. To create incentives for residents to participate as data providers, a data trading cooperation game using a coalition form game is proposed. The characteristic function of the coalitional game will be modeled to account for the overlapping of point cloud data. For the distribution of rewards to each data provider, we use Shapley value, which is a solution representing the contribution to the data.
Unmanned aerial vehicles (UAVs) are crucial in realizing the execution of sensing tasks for digital twin and provision of smart city services in the city airspace without constraints owing to congestion on the ground. Therefore, wireless communication characteristics between UAVs in airspace are important for verifying their practical feasibility. In particular, millimeter wave communication capable of supporting various city services with high traffic loads, emerges as a vital technology for urban airspace services in the 5G era, and beyond. In this study, we conducted real-field experiments to analyze the characteristics of 60 GHz millimeter wave communication standardized by IEEE 802.11ay in airspace using a testbed called the city airspace testbed realized by self-standing smart poles.
The connectome, a comprehensive map of neural connections in the brain, has been extensively studied at the macroscale level to understand complex brain behavior. The macroscale connectome can be modeled as a network, thereby allowing the application of network analysis techniques to explore its characteristics. In spectral graph theory, the weighted spectral distribution (WSD) has been proposed to analyze networks (e.g., communication networks) effectively. In this paper, we introduce new metrics based on the WSD for brain network analysis. Using actual connectome data from fMRI, we evaluate the effectiveness of our WSD-based metrics for brain network analysis. Our findings show that these metrics more accurately reflect the subject’s age and gender compared to other metrics calculated from the connectome data.
This research aims to develop and test a model for strengthening digital competence to become a successful employee career by considering formal personal/internal factors, namely digital competence and digital literacy, where these formal factors result from interventions needed in the era of society 5.0. Data was collected using a questionnaire from 67 administrative staff at hospitals in Semarang City, and analysis was carried out using SmartPLS. The research results show that digital literacy greatly influences digital competence. However, branching Literacy, socio-emotional Literacy and real thinking skill literacy do not affect digital competence. In the end, digital competency is proven to increase career readiness in the future. These findings recommend that institutions develop specific practices and policies regarding digital competence to create intrinsic and extrinsic career success.
