In today’s highly connected digital era, security has become a crucial concern for online learning platforms, particularly in programs such as the TOEFL ITP (Test of English as a Foreign Language Institutional Testing Program) and Massive Open Online Courses (MOOCs). This study examines the use of Internet of Things (IoT) technology to strengthen security through two-factor authentication (2FA) on TOEFL ITP MOOCs platforms. IoT provides an advanced and efficient solution for securing user access by utilizing connected devices, such as smartphones and wearables, to verify user identity in real time. The research explores the architecture of IoT technology that facilitates 2FA integration, analyzing its advantages and challenges, and evaluating its impact on both security and user experience. By implementing IoT-based 2FA, the system protects personal data and prevents unauthorized access, ensuring that only authenticated users can access learning resources or participate in the TOEFL ITP exam. Furthermore, this study underscores how enhanced security through IoT can foster trust among users and encourage wider adoption of online learning technologies. The findings suggest that IoT-based 2FA not only bolsters security but also upholds academic integrity in an increasingly complex digital landscape.
Knowledge distillation (KD), particularly in multiteacher settings, presents significant challenges in effectively transferring knowledge from multiple complex models to a more compact student model. Traditional approaches often fall short in capturing the full spectrum of useful information. In this paper, we propose a novel method that integrates local and global frequency attention mechanisms to enhance the multiteacher KD process. By simultaneously addressing both fine-grained local details and broad global patterns, our approach improves the student model’s ability to assimilate and generalize from the diverse knowledge provided by multiple teachers. Experimental evaluations on standard benchmarks demonstrate that our method consistently outperforms existing multiteacher distillation techniques, achieving superior accuracy and robustness. Our results suggest that incorporating frequency-based attention mechanisms can significantly advance the effectiveness of KD in multiteacher scenarios, offering new insights and techniques for model compression and transfer learning.
This paper proposes a monopolar microstrip antenna with symmetric ring-shaped trapezoid ground slots. The center patch facilitates gap-coupling feeding directly connected with a 50 Ohm coaxial line, while six gap-coupled radiators are arranged in a quasi-circle configuration. The trapezoid ground slots beneath the six radiators serve to adjust the impedance bandwidth and reduce the overall antenna size. The proposed antenna exhibits an omnidirectional radiation pattern with broad bandwidth. From the optimization, we have obtained a smaller size and a thinner substrate of the proposed antenna.
The present study investigates the monitoring of reservoir water levels to help prevent floods by accurately measuring and controlling the water flow through the reservoir’s door. The system uses multiple technologies, such as the OTT C31 universal current meter, float level switches, and the YagiUda antenna, to gather real-time data from substations and main stations around the dam. The data collected includes water level before and after the dam door, and water velocity at the dam door. Ensuring the efficiency and security of data transmission, the system employs an HT12E encoder and HT12D decoder, which are used to encode and decode data for secured transmission. The data was processed in Arduino Mega 2560 and sent to Raspberry Pi due to its ability to connect to WiFi, which could host a website providing users with real-time data and reducing the damage caused by flooding. The real-time data transmission allows the system to significantly improve the capability for proactive flood management, making it a vital tool for protecting public safety and infrastructure. The simulation and experimental results demonstrate the effectiveness of the proposed system in both controlled and real-world environments. Key findings include the performance of the Yagi-Uda antenna at 433 MHz and the water velocity measurement through varied cross-sectional dam doors.
This work presents a new circularly polarized (CP) monopolar microstrip antenna for future smart mobility communication system. The proposed antenna consists of hexagonal quasi-circular patches on the top plane, complemented by asymmetric trapezoid slots on the ground. By integrating rotated asymmetric trapezoid ground slots, the antenna induces a radiation pattern characterized with right-handed CP. The parameters have been optimized by precise design considerations for enhanced performance.
This paper explores the transformative impact of AI-powered digital assistants on business operations and the evolving role of secretarial work. As organizations increasingly integrate artificial intelligence (AI) into their workflows, digital assistants are becoming essential tools that streamline tasks, enhance the efficiency, and support decision-making processes. These AIdriven technologies are not only automating routine administrative functions but also enabling more strategic contributions by secretaries, such as managing complex schedules, data analysis, and personalized communication. The study examines how AI is reshaping the traditional secretarial role, leading to a shift in job responsibilities and skill requirements. It also discusses the potential challenges such as ethical considerations and the need for upskilling that arises with this technological advancement. The findings suggest that AI-powered digital assistants are set to revolutionize the business landscape, offering both opportunities and challenges for the future of secretarial work.
Efficient resource allocation in device-to-device (D2D) communication within 6G networks is crucial for enhancing overall network performance and efficiency. This paper presents a novel Deep Learning (DL) based approach for radio resource allocation (RRA), leveraging distributed artificial intelligence (DAI) using belief-desire-intention extended (BDIx) agents, dynamic feedback allocation, and a Deep Feedback Neural Network (DFBNN). Additionally, Federated Learning (FL) is integrated to enable distributed training across BDIx agents, serving as D2D Relays (D2DR) or D2D Multihop Relays (D2DMHR), ensuring data privacy and reducing communication overhead. The proposed method is thoroughly evaluated against traditional graph-based and game-theoretic algorithms and deep feedforward neural networks (DFNNs). Results demonstrate significant improvements in interference management, data rate, and execution time. By providing scalable, adaptive, and resilient resource allocation, this proposed method meets the stringent requirements of 6 G applications, paving the way for more efficient and reliable network operations.
As COVID-19 cases continue to rise, minimizing physical contact is essential to curb the virus’s spread. IDE LPKIA, an educational institution, currently uses a centralized attendance system based on fingerprint scanning, which increases the physical contact and thus the potential for virus transmission. To address this issue, this research proposes a new attendance system that allows employees to mark their attendance independently using their personal smartphones, eliminating the need for centralized attendance stations. The proposed system integrates facial recognition and location radius technology. Facial recognition is implemented using a convolutional neural network (CNN) to ensure accurate identification, while the Haversine formula is employed to calculate the location radius, ensuring attendance can only be registered within a specific geographic area around the institution. This approach not only reduces physical contact but also prevents attendance fraud, as employees can only check in based on their facial identity and within the defined location radius. This system aims to enhance safety and integrity in attendance tracking amidst the ongoing pandemic.
This study focuses on the market share reduction of an Indonesian Internet provider in spite of rising revenue and client base. More research on this issue would be intriguing, particularly in light of how crucial customer pleasure is to retaining market share. There aren’t many research that particularly examine how brand recognition and image affect consumers’ intentions to stick with fixed broadband packages, particularly in Indonesia. By examining the impact of brand image and brand awareness on continuation intention through customer satisfaction on Internet provider goods in Indonesia, this study seeks to close a gap in the literature. We implemented a quantitative technique by disseminating a structured survey. We employed the SEM-PLS approach to examine the data that we had gathered. According to our research, customer satisfaction is positively and significantly impacted by brand image, customer satisfaction is positively and significantly impacted by continuity intention, and customer satisfaction is positively and significantly impacted by brand awareness, which in turn influences Continuance Intention through customer satisfaction. Conversely, neither directly nor indirectly, brand awareness has no appreciable impact on customer satisfaction or continuation intention. Also, Continuance Intention is not greatly impacted by Brand Image.
The Internet is integral to modern life, with Internet service provider (ISP) offering appealing deals to meet the demand for unlimited data. However, reality often falls short of expectations. While recommendation systems exist, user-centric options are rare. This paper proposes a novel ISP selection methodology using user experience data and a calibrated random forest (CRF) model. Unlike traditional methods that focus on advertised features, this approach emphasizes user-defined criteria such as cost, device connectivity, and technical support experience. By analyzing survey data, the model highlights the critical link between user needs and support quality, enabling users to choose ISPs that prioritize customer service. The model demonstrates promising results with a strong R-squared value and low mean squared error (MSE). This user-centric approach fosters informed decision-making, potentially driving competition and encouraging ISPs to improve service standards, laying a foundation for future developments in ISP selection.
This study explores the integration of artificial intelligence (AI) with human-centered design (HCD) principles in crafting user interface (UI) and user experience (UX) for epromotion platforms within Indonesia’s smart cities. As culinary tourism emerges as a significant driver of local economies, particularly in diverse and culturally rich countries like Indonesia, the need for innovative promotional strategies becomes essential. AI technologies are increasingly being utilized to personalize and enhance user interactions, providing tailored recommendations and engaging experiences for tourists. However, to ensure these AI-driven solutions meet the needs and expectations of users, incorporating HCD in the design process is crucial. This research examines how AI-powered public applications can effectively boost culinary tourism by delivering personalized, seamless, and culturally relevant experiences to users. The study focuses on designing UI/UX that not only leverages AI for functional efficiency but also prioritizes the emotional and cognitive engagement of users, ensuring that technology serves as an enabler rather than a barrier. By analyzing current trends and case studies within Indonesia’s smart cities, the paper provides insights into best practices for integrating AI and HCD in e-promotion strategies. The findings aim to offer valuable guidelines for developers, marketers, and policymakers in enhancing the appeal and effectiveness of digital tools designed to promote culinary tourism, ultimately contributing to the growth of Indonesia’s tourism sector in the smart city context.
This article chooses to use the random forest algorithm to improve the performance of network intrusion detection systems (IDS). The algorithm significantly improves the accuracy, recall and precision of network intrusion detection compared to traditional methods. The required data and experimental results were obtained from the LUFlow dataset by using a more accurate feature extraction method. Eventually, the readability and comprehension of the experimental results were enhanced by visualizing them. Overall, the performance of the network IDS based on the random forest method has been significantly improved. However, there are still some problems in the experiment, such as the lack of comparison with other commonly used intrusion detection methods or algorithms. Similar problems make the experiment lack of comprehensiveness. Therefore, future research should consider introducing more kinds of intrusion detection methods for comparative analysis to further validate and improve the performance of the system. In addition, extending the dataset of the experiments and improving the feature extraction techniques may also bring additional improvements. In summary, although the performance of the random forest-based network IDS has been improved, there is still much room for improvement and research potential.
