This study focuses on the vulnerabilities and attack vectors connected with ransomware in Elastic Sky X integrated (ESXi) settings. We offer a novel technique to address these concerns by mimicking an ESXi environment, focusing on honeypot deployment and monitoring behaviors. Our strategy is creating a controlled emulation of ESXi in which we place honeypots to lure and capture ransomware activity. Furthermore, we use sophisticated monitoring methods to watch and evaluate ransomware behaviors in this simulated environment. Our approach’s effectiveness is tested using the simulated ESXi environment’s detection and response capabilities. The findings show that using honeypots in conjunction with careful behavioral monitoring can considerably improve the identification and mitigation of ransomware threats in virtualized environments.
In academic writing, the accuracy of citation formatting in scientific publications is essential for maintaining the integrity and consistency of scientific communication. However, manually formatting citations according to different styles, such as the IEEE, APA, or MLA, can be time-consuming and error-prone. This paper presents an innovative approach to automate citation formatting in scientific publications using ChatGPT. It proposes an algorithm that incorporates a sequence of instructions and guidance, combined with the capabilities of ChatGPT, and greatly simplifies the process of formatting citations according to different styles. The proposed approach involves training ChatGPT with a dataset containing citation guides and examples from different formatting styles to improve its ability to generate correctly formatted citations. This work presents a comparative characterization of the existing automated citation formatting systems and the proposed algorithm with ChatGPT. Their functionalities are analyzed and their advantages and disadvantages are highlighted. In addition, a SWOT analysis of the systems is performed, which examines their strengths, weaknesses, opportunities, and threats. The analysis highlights the effectiveness and advantages of the proposed solution with ChatGPT. The results show that automation using ChatGPT not only facilitates accurate citation formatting but also offers a practical tool for improving the quality and relevance of scientific publications. ChatGPT can significantly reduce formatting errors and improve the efficiency of academic writing, offering a scalable solution for researchers and institutions.
Reliable direction of arrival (DOA) estimation is crucial for the performance of wireless communication systems. In this paper, we introduce a refined DOA estimation method that combines eigenvalue reconstruction of the noise subspace and Toeplitz preprocessing with the multiple signal classification (MUSIC) algorithm. The proposed technique enhances the consistency of the noise subspace and improves the algorithm’s resolution. Extensive simulations demonstrate that the method outperforms both the standard MUSIC and the MUSIC with Eigenvalue Reconstruction (MUSIC_ER) techniques. Notably, our approach shows enhanced performance in terms of the root mean square error (RMSE) across snapshot ranges from 1 to 10. These enhancements make the proposed method (MUSIC_TR) a practical and effective option, especially in low-snapshot scenarios, providing an alternative solution for DOA estimation.
In recent years, telehealth and telerehabilitation have been on the rise due to lockdown and quarantine placing restrictions on in-person healthcare during the 2020 COVID-19 Pandemic. However, there are some limitations to the service that physicians are able to do remotely. This paper proposes a contemporary way of collecting more data from the remote patient to help during their telerehabilitation appointments. Surface electrodes are placed on the participants’ forearm flexor muscles, and samples were collected for each exercise. The data were then analyzed, and the features-RMS, IEMG, and VARwere used. The exertion of the strength of the muscle during an exercise could be seen from plotting the RMS of the exercise to the IEMG time domain graph, and the classification of the exercise could be interpreted from the IEMG data from one exercise, that has been normalized, which was plotted as a box plot diagram to be compared with the other exercises. The findings from this paper could be used in helping to build a model to ensure that the exercises are done correctly and the muscles are not being strained too hard during an exercise by the physician during a telerehabilitation session.
Technology enables manufacturing small-and medium-sized enterprises (SMEs) to improve operational efficiency through the use of business management software, digital inventory systems, and automation of production processes. Technology opens up wider market access, allowing SMEs to reach potential consumers through digital platforms and e-commerce to international markets. This study aims to measure the readiness of manufacturing SMEs in Indonesia in implementing digital technology. This study uses Maturity Assessment with a questionnaire instrument. The findings show the average manufacturing SMEs in Indonesia is at the “Learning” readiness level with a percentage of $87 \%$ in which the textile and food sectors are recorded as the most ready to adopt digital technology. The findings also show that a higher category of technology readiness for SMEs is associated with the larger proportion of SMEs that participated in the government program.
The quality of commutator surfaces in DC motors significantly affects the performance and longevity of the motors. Traditional methods of inspecting commutator surface defects, such as roundness and roughness meters, have limitations in detecting subtle and complex surface irregularities. This study proposes an image analysis technique combined with convolutional neural networks to enhance the detection of commutator surface defects. Our method improves the identification and classification of defects, correlating these findings with the assembly quality of DC motors. Although the experimental results are premilitary, it validates the effectiveness of the proposed approach, demonstrating improvements in defect detection accuracy. Future work will focus on expanding the image dataset and refining the CNN model to enhance its accuracy and real-time application capabilities.
With the rapid growth of online transactions and interactions, the threat landscape of scams and fraud has evolved, necessitating sophisticated detection mechanisms. This paper provides an extensive review of the latest advances in detecting online scams and fraud, covering technological solutions, machine learning techniques, and emerging trends in the field. Key methods discussed include advanced machine learning algorithms for anomaly detection, user behavior analytics, and the integration of threat intelligence. Additionally, this study highlights the role of public awareness and education in preventing scams, as well as the importance of international collaboration in law enforcement. By examining current trends and emerging technologies, this study provides strategies for organizations and individuals to enhance their digital security posture, effectively mitigating the risks associated with online scams and frauds.
Optical advances in skincare technology represent a revolutionary approach to addressing various dermatological concerns and enhancing overall skin health. This study provides an in-depth exploration of the principles, applications, and benefits of optical technologies in skincare. From noninvasive diagnostics to targeted treatments and cosmetic formulations, optical innovations are transforming the landscape of skincare, offering new possibilities for personalized and effective solutions. Optical advances in skincare technology have the potential to transform dermatological practice and improve skin health outcomes for individuals worldwide.
This study analyzes the impact of the loan-to-value (LTV) ratio on housing loan demand and housing price bubbles, emphasizing its importance in shaping investment decisions and risk management in the mortgage market. Using a bibliometric analysis technique, data were refined from the Scopus database, resulting in 198 articles published in English between 2014 and 2023. VOS Viewer was utilized to visualize bibliometric networks and identify research trends. Findings indicate a significant increase in publications from 2021 to 2023, influenced by the COVID-19 pandemic and changes in LTV regulations. The United States and the United Kingdom were identified as leading contributors to the research. Key themes include mortgage lending, macroprudential policy, housing market dynamics, and risk management. The study highlights the evolving nature of LTV research and its critical role in financial stability and macroprudential regulation, underscoring the importance of international collaboration in advancing knowledge in the mortgage sector.
Aimed to the challenge of robotic voice interaction, this study leverages ChatGPT (chat generative pretrained transformer) technology to develop a humanoid robot solution with a central processing system. Employing a top-down design approach, the solution encompasses the design of voice, video, and motion streams between users and the robot, enabling voice communication and expression output. By integrating hardware devices and a central control system, the entire humanoid robot system achieves a twofold purpose. On one hand, it combines data from conversational context, user tone and emotion, and user facial expressions to appropriately exhibit expressions. On the other hand, it formulates reasonable voice responses in conjunction with extracted statement content and emotional cues. Lastly, two physical prototypes of the humanoid robot are constructed. Experimental trials are conducted to assess the voice conversation and expression output capabilities of the humanoid robot, thereby confirming the rationality and effectiveness of the proposed solution.
To overcome the constraints imposed by the Hi3559 processor’s limited general video interfaces and poor device compatibility, a multi-interface video capture system based on field-programmable gate arrays (FPGA) is developed. By employing asynchronous double data rate (DDR) access techniques, a decoding selection module is designed to facilitate the transformation of the four video input formats. This video capture system can accept inputs in the PAL, high-definition multimedia interface (HDMI), Cameralink, and serial digital interface (SDI) formats. It employs an FPGA to decode these inputs and encodes them into the low-voltage differential signaling (LVDS) format for output, allowing seamless data exchange with the Hi3559 processor through the Mobile Industry Processor Interface (MIPI). The experimental results reveal that our system can precisely transcode 720p@30Hz PAL video and 1080p@60Hz Cameralink, HDMI, and SDI videos to the LVDS format which is adapted to the Hi3559 series. Video format conversion using our system is robust, ensuring smooth and uninterrupted video streaming without flickering or frame loss.
Task-oriented dialogue (TOD) systems are built to help users accomplish specific objectives. However, even though ongoing reviews and improvements have been made to its components, an official industrial standard has yet to be established. Additionally, TOD systems face limitations in detecting out-ofscope events, deciding when to access a database, and offering scalability for further processing. To address these issues, we introduce a comprehensive TOD framework and present solutions to overcome these limitations. We also investigate dialogue state tracking, the initial phase of the system, and assess how well it can identify out-of-scope events triggered by user actions not predefined in the conversation.
