This research explores the utilization of mobile applications in a leading transportation service company in Indonesia. The study aims to understand how the company drives innovation through its mobile application to meet the demands of an evolving market. Drawing on James March’s innovation theory, which suggests that organizations can innovate through events that create momentum, the research was conducted qualitatively. Interviews were conducted with key personnel responsible for development within the company, as well as 15 customers. The findings indicate that March’s theory and the concept of digital mastery are applicable within the transportation sector, serving as key factors in the company’s shift from a conventional to a technology-focused approach. The study concludes that the organization’s ability to navigate technological disruption was evidenced by its strategic decisions, aligning with March’s concept of innovation through reach events.
This paper focuses on dynamic analysis for malware detection on Android. Initially, a literature review was conducted to understand both static and dynamic analysis approaches and their limitations, particularly highlighting the shortcomings of static analysis. The study demonstrates techniques for extracting various traces, such as system calls and network traffic, using dynamic analysis. The core of the study is the design of an automated system for the dynamic analysis of Android malware. This system automates the capture and analysis of APK traces using modules that monitor system calls, debug logs, and network traffic. It was found that relying on a single dynamic analysis module is insufficient, leading to false negatives, whereas combining data from all three modules enhances detection accuracy. Future directions include developing an intermediary using MQTT to reduce database load and improving the learning process for the three modules.
Firewalls are network security systems positioned between internal and external networks to isolate them. Their fundamental functions include zone isolation, access control, attack protection, and redundancy design. However, firewalls also face numerous security challenges, with Distributed Denial of Service (DDoS) attacks being a major concern, particularly the Denial of Firewalling (DoF) attacks targeting firewalls. Despite extensive research on DDoS attacks against traditional networks, relatively fewer studies focus on DoF attacks. To comprehensively understand the latest research progress and inspire the development of new solutions to counter DoF attacks, this paper conducts an extensive survey of existing research progress and forms a review. Firstly, we analyze the principles of DDoS attacks against firewalls, as well as the security risks of new firewall technologies, and classify them based on attack rates and target components of firewalls. Secondly, we analyze and evaluate the existing DoF attack detection technologies. Next, we summarize the existing DoF attack mitigation techniques. Finally, we discuss current challenges and open issues. We hoped that this research work will assist relevant researchers in effectively addressing DoF attacks.
This study examines the dynamic transformations occurring within public relations ($\mathbf{P R}$) and journalism in Indonesia in response to the advancement of artificial intelligence (AI) technology. The primary aim is to explore how the integration of AI necessitates the continued adherence to ethical codes in PR and journalism practices. The research is anchored in the theoretical frameworks of media ecology and professional ethics. The methodology includes in-depth interviews with 15 participants, comprising PR professionals, journalists, and media experts, to gather primary data. The analysis reveals key themes related to system dynamics, ethical dilemmas, and perceptions of technology use. The findings indicate that while PR practitioners and journalists in Indonesia are increasingly utilizing AI tools in their work, challenges remain due to previous work habits. However, there is a general acceptance of AI as a tool that enhances efficiency. This supports the applicability of media ecology and professional ethics theories to these professions. The study underscores the need for ongoing adaptation to technological advancements while maintaining professional ethical standards.
This article investigates the robust offloading performance of an unmanned aerial vehicle (UAV)-aided nonorthogonal multiple access (NOMA) incorporating mobile-edge computing (MEC) with the wireless power transfer (WPT)-enabled in Internet of Things (IoT) networks. To assess the system efficacy, we derive the closed-formed expressions of outage successful computation probability (OSCP) under Nakagami-m fading channel. Subsequently, we formulate a system optimization problem of maximizing OSCP by utilizing particle swarm optimization (PSO) algorithm. Numerical findings are implemented with a variety of parameters, thereby validating the precision of our work.
This paper investigates unmanned aerial vehicle (UAV) and nonorthogonal multiple access (NOMA)-mobile-edge computing (MEC) in the Internet of Things (IoT) network, where a UAV acts as a relay. To evaluate offloading performance, we derived closed-form formulas for the successful computation probability (SCP) using the Nakagami-m fading channel model. Furthermore, we also propose an optimization problem to maximize SCP by optimizing the UAV deployment location using the genetic algorithm (GA) method. Finally, numerical results are presented to demonstrate the validity of our analysis.
Optimizing device-to-device (D2D) topologies is pivotal for enhancing the performance and efficiency of 6G networks. This paper introduces a novel approach for forming optimal subnet trees within the 6G networks using BDIx agents and advanced minimum-weight spanning tree (MWST/MST) algorithms augmented by graph neural networks (GNNs), and feedforward neural networks (FFNNs). Our solution aims to significantly boost network performance, particularly in highdemand scenarios such as urban areas, large-scale events, and remote locations. Our approach dynamically adapts to changing network conditions, user movements, and traffic patterns by minimizing the power consumption and maximizing the throughput. We implement various MWST algorithms, including Kruskal’s, Prim’s, and Boruvka’s algorithms, and introduce a GNN model to predict edge weights combined with FFNNs to select parent nodes (called GNN-FFNN model), aiding in the construction of minimum-weight spanning trees (MWST). Additionally, a “weighted distance” metric is proposed to analyze network performance comprehensively. The proposed AI/MLdriven solution integrates BDIx agents with MWST algorithms, focusing on optimizing subnets under gNodeB in 6G networks, enhancing data transmission efficiency, reducing latency, and increasing throughput. This research contributes to developing scalable and flexible network management solutions suitable for diverse configurations and architectures.
The integration of Internet of Things (IoT) technology into poultry farming has revolutionized the industry, offering new possibilities for automation, real-time monitoring, and data-driven decision-making. This paper explores the innovative applications of IoT in poultry farming, highlighting how these technologies enhance operational efficiency, improve animal welfare, and increase productivity. By examining IoTenabled devices, systems, and their implementation, we present an overview of the current advancements and future potential in smart poultry farming.
The development of analytics has revolutionized human resource management by enhancing data-driven decisionmaking. However, human resources analytics (HRA) adoption remains limited, and this is where HR professionals play a crucial role. This study examines the determinants of HR professionals, intention to adopt HRA in addition to their subsequent usage behaviour, utilizing the Unified Theory of Acceptance and Use of Technology model. The study also examines the moderating role of organizational culture in this relationship. Data was gathered through a structured questionnaire from 73 Human Resources professionals in Jakarta. Structural equation modelling-partial least squares was used for the analysis process. The results expose that performance expectancy and social influence significantly impact HRA usage intention, while effort expectancy and facilitating conditions do not. Furthermore, HRA Adoption Intention significantly influences HR analytics usage behaviour. Particularly, organizational culture strengthens the connection between HRA usage intention and usage behaviour. These results emphasize HR professionals’ importance in driving HRA’s adoption, highlighting its performance gains and leveraging social influence. Organizations should adopt a supportive culture to improve the transition from intention to actual usage. The results contribute to the literature by addressing gap in the current understanding of the factors influencing HRA adoption while providing practical implications for organizations targeting to connect the value of HR analytics.
Phishing attacks are the most common cyberattacks nowadays. Phishing attacks rely on social engineering concepts to trick victims into reaching the goals of malicious attackers. In addition, phishing attacks are the largest vector for various cyberattacks. However, URLs are a fulcrum for phishing attacks. The difficulty distinguishing between legitimate and phishing URLs is the reason for the increased success rates of these attacks. An integrated framework is proposed in this study to detect phishing attacks based on classifying URLs into phishing or legitimate URLs through machine learning models such as decision tree (DT) and random forest (RF), which have high power and prediction accuracy in binary classification tasks. The RF model, using the cross validation (CV) technique, achieved an accuracy score of $\mathbf{9 8. 2}$. This methodology is embedded in a web application with a graphical user interface to provide ease of handling and show alerts in real time and visually. This contributes to providing the field of cybersecurity with a highly accurate verification system to reduce users falling victim to these dangerous attacks.
Recently, 3D Gaussian, as an explicit 3D representation paradigm, has demonstrated strong competitiveness over NeRF (neural radiance fields) in terms of expressing complex scenes and training duration. These advantages signal a wide range of applications for 3D Gaussians in 3D understanding and editing. Meanwhile, the segmentation of 3D Gaussians is still in its infancy. The existing segmentation methods are not only cumbersome but also incapable of segmenting multiple objects simultaneously in a short amount of time. In response, this paper introduces a 3D Gaussian segmentation method implemented with 2D segmentation as supervision. This approach uses input 2D segmentation maps to guide the learning of the added 3D Gaussian semantic information, while nearest neighbor clustering and statistical filtering refine the segmentation results. Experiments show that our concise method can achieve comparable performances on mIOU and mAcc for multi-object segmentation as previous single-object segmentation methods.
Wheat is one of the crucial cereal crops globally, and its productivity is affected by various diseases. Therefore, the timely and precise detection and classification of these diseases are important. The advancements in deep learning (DL), especially convolutional neural networks, have observed significant evolution in wheat disease identification. This article provides a comprehensive overview of research works, which effectively utilized DL for wheat disease prediction and classification. The paper begins by introducing the importance of wheat disease management and the challenges associated with traditional disease identification methods. The survey further explores the general architecture of the DL-based wheat disease prediction framework the study highlights the significance of DL models in achieving accurate and effective disease classification. In addition, it demonstrates the challenges associated with different DL models relative to wheat disease identification. Furthermore, it examines the research works related to wheat disease detection using DL models and highlights the limitations of those methods. Finally, the survey provides the future research scope for an improved disease identification model.
