Reviewerly/exHarmony huggingface dataset:
Research on AIS Signal Frequency Statistics based on SpringBoot+Vue

With the rapid development of global industry, AIS (automatic identification system) has become an important tool for ship management and safety monitoring. In order to better display the frequency distribution of AIS signals, this paper graphically displays the data in the AIS signals, allowing users to fully understand the data in the AIS signals, including ship type distribution, speed range, and signal frequencies in different areas. It uses Kafka and Flink technology to receive, clean and analyze the sent data, and finally stores it in MySQL and Redis data. It reads the data through Spring Boot + MyBatis, and uses Vue + Echarts chart tool and AutoNavi Map API for display and analysis. To gain an in-depth understanding of its importance and application prospects in the field of epidemics.
Fast Convergent Ali Baba and Forty Thieves Algorithm for Inverse Kinematic Solution of 7-DOF Robotic Manipulator

Inverse kinematics is a crucial topic in robotics, enabling robots to calculate the joint angles required to achieve the desired end effector position and orientation. Solving the inverse kinematics problem quickly with high accuracy is vital for robot manipulators. If sufficient speed is provided, the real-time motion planning task of robot manipulators can be achieved. Real-time motion planning of complex robot manipulators is not possible with classical mathematical methods. Overcoming this problem will provide many benefits in the design and control of robot manipulators. In contemporary research, metaheuristic approaches have become widely employed for addressing the inverse kinematics problem. This investigation utilizes the efficient and simple Ali Baba and the Forty Thieves (AFT) algorithm to resolve the inverse kinematics problem. To increase convergence speed of AFT, a parameter has been used in early iterations of the algorithm to prevent thieves from randomly searching within the search area to find Ali Baba when they realized they had been deceived. Additionally, an approach has been proposed regarding the accuracy of the information brought by the Marjaneh. Finally, the inverse kinematics solution of the 7-DOF robot manipulator was carried out comparatively.
IT Governance Practices in the Development of Digital Space for the Orchestration of Learning Services in Higher Education

It is known globally that the Covid-19 pandemic has accelerated the digital transformation process in every aspect of the organization, which led to the question of how to define the strategy for the transformation itself can be measured and determined in an organized manner. The approach in producing the necessary efforts for the digital transformation process will involve the managerial and executive leaders outlining the resources needed for the business process, which sometimes leads to another problematic situation in how to make the resources can communicate and work together in the new landscape. Understanding the proper way to have the exact governance mechanism for the digital transformation process led to another topic, from solution to adaptation to achieve sustainable conditions for the organization. It raises another question of what ITG practice the university can implement in facing the challenges of multiple contexts of digital transformation from the IT division and academics. The decision-making needs to have happened in cross-functional teams under the expectation that the middle ground can be achieved with IT as the subject for the business process changes. As the unprecedented situation occurs and might happen in the future, the transformation process under the practice of good ITG would become the innovative way and the breakthrough for higher education in overcoming the problems.
Dynamic Allocation Mechanism of Cloud Computing Resources Driven by Neural Network

With the popularization of cloud computing technology, the dynamic allocation mechanism of cloud computing resources has become an important research field to improve resource utilization and meet the needs of diversified workloads. The purpose of this study is to explore the dynamic allocation mechanism of cloud computing resources driven by neural network and introduce the powerful ability of deep learning into cloud computing environment. We put forward a comprehensive framework, which combines data collection, analysis, decision-making and implementation to realize intelligent resource allocation. These data will be used to train BP neural network (BPNN). In order to predict the bidding price, a BPNN is designed, which usually includes input layer, hidden layer and output layer. The number of nodes in the input layer is equal to the dimension of the input feature, and the number of nodes in the output layer is 1, which indicates the prediction of the bidding price. Through experiments and simulations, we verify the effectiveness of the dynamic resource allocation mechanism driven by neural network. The results show that this mechanism can better adapt to the changing workload requirements, improve resource utilization and reduce resource waste. In addition, it provides better performance and user experience, thus enhancing the competitiveness of cloud computing systems.
Petrology and geochemical modeling (Ba-W-Sn) of the Peña do Seo granitoids (West Asturian-Leonese Zone, León, Spain)

The Cadafresnas granites and granitoid breccias (Peña do Seo, W of León) are small post-tectonic stocks of Variscan age related to W-Sn mineralizations. According to their mineralogical geochemical characteristics, they are classified as granites-syenogranites, granodiorites and granitoids rich in quartz, of peraluminous/metaluminous composition. The Rb-Ba-Sr relationships allow assigning a metapelitic source for the granites and a metagreywacke source for the granitoid breccias. Using geochemical modeling (partial melting), the contribution of these sources to explain the Ba, W and Sn contents of the granites and granite breccias is investigated. The results indicate that the partial melting of these lithologies explains part of the content of these elements in the studied rocks, requiring additional processes of fractional crystallization and/or hydrothermal injections.
Augmenting Early Childhood Education: The Integration of Augmented Reality in Promoting Personal Hygiene

Personal hygiene plays a significant role in supporting and safeguarding the overall development and wellbeing of 4 to 6-year-old children who are particularly at risk of health concerns. Augmented reality (AR) technology combines familiar technology with everyday environments, offering children opportunities for extended learning outside the traditional classroom setting and into their everyday experiences. AR has recently gained considerable interest as part of an educational framework due to its capacity for immersing students in real experiences. Educational games provide children with an engaging alternative way of being taught. This research aims to develop an AR-based game to facilitate children's knowledge acquisition process regarding personal hygiene. By harnessing AR technology, this initiative seeks to foster an appreciation of its importance within young minds. Data for this project was primarily collected through an online Google survey that engaged both MSU community members and members of Selangor, Malaysia's general public. With 50 responses received mostly from parents, this poll provided insight into a general understanding of personal hygiene among its respondents. Parents were found to agree on the need to instil hygiene importance from early on with their child(ren). Sixty four percent (64%) had knowledge of AR games while remaining respondents did not. While respondents were aware of AR games, most did not actively engage with them. Yet most agreed that creating games as an effective method to promote hygiene awareness among young children. Based on the target age group and survey responses, game development should aim for simplicity by including quizzes and mini games focused on specific subjects that will create an enjoyable learning experience for children. This would ensure an engaging educational journey. This project concludes that video games can indeed serve as an effective learning tool for young children, given their affinity and capacity to simultaneously enjoy and learn through gameplay. Such an approach should prove fruitful to this endeavor.
Implementation of Potential Fields in a Differential Mobile Robot

This paper presents the implementation of the autonomous navigation technique, Potential Fields. It describes the model of the mobile robot in a differential configuration, considering a point displaced from the axis of action of the wheels. In addition, it discusses the robot's odometry and the implementation of the autonomous navigation method for obstacle avoidance. The study presents results from both simulations in the CoppeliaSim environment and experimental tests on a mobile robot in a differential configuration. This robot was built using low-cost electronics such as the ESP32 development board and 6 HCSR-04 ultrasonic sensors. This provides a valuable opportunity to learn and acquire practical knowledge about mobile robotics and the implementation of navigation and control algorithms.
Efficient Spatial Coordination for Multi-Robot Exploration

In this study, we introduce an efficient approach for spatial coordination in cooperative robotics using Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAV). The approach involves distributing mathematical calculations among three roles: a central robot, an upper robot, and multiple surrounding robots. To maintain the desired parametric formation, we propose a specific control law based on the linear and angular velocity of the central robot, enabling dynamic formation adjustments for efficient exploration. Our strategy employs visual localization with ArUco markers and artificial potential fields for navigation. We present experimental results utilizing Robot Operating System (ROS) and OpenCV.
Design and Development of the “GU CyberCar”: A Solar-Powered Electric Vehicle Based on IoT Technology

This paper presents the conception, design, and development of the “GU CybcrCar,” a solar-powered electric vehicle prototype implemented at Gulf University (GU). Inspired by Tesla's Cybertruck and driven by sustainability goals, the GU Cyber Car leverages advanced technologies such as the Internet of Things (IoT) and solar power to offer a novel approach to sustainable mobility. The vehicle's design employs a unibody structure and exoskeleton made from stainless steel, reminiscent of the Cybertruck, ensuring robustness and safety. The body shape is optimized for aerodynamics while providing ample surfaces for solar panel integration. The car utilizes high-efficiency solar panels and a state-of-the-art energy storage system to efficiently harness and store solar energy. Moreover, the GU Cyber Car embodies the Internet of Things (IoT) technology principles for enhanced functionality and user convenience. It incorporates a network of sensors and communication modules, facilitating real-time monitoring and control of the vehicle's system. These IoT features enhance the driving experience and contribute to energy management and overall vehicle safety. The GU CyberCar is a testament to GU's commitment to fostering innovation, promoting sustainable mobility, and offering practical learning experiences for its students. This project showcases the potential of solar and IoT technologies in automotive design and sets a foundation for further research and development in this arena. Through this project, GU aims to inspire a new generation of environmentally conscious engineers and pave the way for more sustainable transportation solutions. The GU CyberCar is a tangible example of how academic institutions can actively contribute to addressing global environmental challenges and shaping the future of mobility.
Does the Input Image Spatial Resolution Generate Different Synthetic Images? A Comparative Study of Facial Expression Synthesis Performance

Facial expression synthesis has gained significant attention in the image synthesis field. Generative Adversarial Network (GAN) models have recently gained popularity due to the high-quality synthetic images they produce. However, these models require complex network architectures that can take days to train, even with high-performance Graphics Processing Units (GPUs). Many efforts have been made to accelerate and compress such models, but little attention has been paid to the resolution of the images. This study aims to assess the impact of input/output spatial resolution on the resources needed for training a facial expression synthesis model, as well as on the quality of the results. Our results indicate that the produced images and videos had similar quality results measured through objective measures for the spatial resolution of 128 × 128, 256 × 256, and 480 × 480. Furthermore, we found that lower-resolution images could significantly reduce the time required to generate new facial expressions without compromising quality, as measured by objective measures.
Car Engine and Chassis Assembly Training Simulation Using VR Technology

The process of educating and learning about the automotive industry is still ongoing, and one of the most effective techniques is the provision of direct practice. Such a system is regarded as effective, albeit with certain downsides. One of them is the restricted area and time required to conduct the exercise, as well as the significant risk of accidents during vehicle engine assembly. Based on these justifications, Vehicle Engine Assembly Simulation with Virtual Reality (VR) Technology is now available as an alternate means of educating students about the automotive industry, particularly car engine assembly. This concept employs a virtual environment that allows users to perceive the automobile world in real time through an immersive experience. VR allows users to directly disassemble automobile engines. This virtual reality application was created utilizing the Oculus Quest 2 platform and the Unity Game Engine.
Leveraging Zero and Few-Shot Learning for Enhanced Model Generality in Hate Speech Detection in Spanish and English

Supervised training has traditionally been the cornerstone of hate speech detection models, but it often falls short when faced with unseen scenarios. Zero and few-shot learning offers an interesting alternative to traditional supervised approaches. In this paper, we explore the advantages of zero and few-shot learning over supervised training, with a particular focus on hate speech detection datasets covering different domains and levels of complexity. We evaluate the generalization capabilities of generative models such as T5, BLOOM, and Llama-2. These models have shown promise in text generation and have demonstrated the ability to learn from limited labeled data. Moreover, by evaluating their performance on both Spanish and English datasets, we gain insight into their cross-lingual applicability and versatility, thus contributing to a broader understanding of generative models in natural language processing. Our results highlight the potential of generative models to bridge the gap between data scarcity and model performance across languages and domains.
Pengaruh Kualitas Sistem Informasi Akuntansi dan Teknologi Informasi terhadap Kinerja Perusahaan: Peran Mediasi Pengendalian Internal PT Semen Indonesia Distributor Distributor

Through improving company performance derived from all business activities, businesses can increase their competitiveness. This performance can run with procedures that can be completed with workers as data framework administrators. The effectiveness of a company's accounting information system is very important to support overall company performance and must meet high quality standards. This research uses a quantitative approach with analytical descriptive methods. The information source was gotten by finishing up a poll directed by representatives of PT Semen Indonesia Distributor 2023. Information obtaining will be examined utilizing SEM model with the assistance of the PLS program. The findings of a number of tests indicate that some aspects have a positive effect while others have a negative effect on each other. The quality of AIS has no effect on company performance but has a positive effect on internal control, while internal control and information technology have a direct positive effect on performance. Based on these results, it can be seen that improving the quality of AIS and information technology can basically improve company performance.
The Development of the MSU MyCoral Virtual Reality Application: An Innovative Approach to Educating Students on the Imperative of Coral Reef Rehabilitation

Coral reefs play critical roles in protecting coastal environments from erosion, mitigating wave impacts, and providing recreational and cultural experiences. Yet, these invaluable ecosystems face global threats, such as illegal human activities that cause widespread devastation. Management and Science University created MyCoral as part of its MyReef project to spread awareness and foster coral reef rehabilitation efforts. A key aspect of MyCoral is a Virtual Reality application designed specifically to simulate undersea environments - something Management and Science University was already doing through the MyReef VR application. Experiences provided through MyCoral VR enhance user understanding of coral rehabilitation processes for users. Following an iterative development model allows for robust user feedback and constant improvement through various testing stages; findings indicate that MSU MyCoral VR successfully increases public awareness regarding coral reefs by designing virtual reality-based training modules that emphasize coral habitation.
Automation Lamp Healing Application based on Face Expression Identification System using Convolution Neural Network (CNN)

The disruption of routines, education, recreation, as well as concerns about family income and health, has left many young people feeling fearful, angry, and worried about their future. In this system, a smart system will be developed that can capture, process, and interpret facial expressions so that a person's emotions can be easily and accurately identified, which is one of the best solutions. The system will use artificial intelligence that has been trained beforehand using suitable datasets. In the processed of developing the system, Face image with the size of 48 x 48 x 1 became the input of the system, processes by Convolution layer using Relu activation function. The activation function used in the output layer is softmax because it is suitable for more than 2 classifications. by applying CNN based on Adam optimization which have result on 98.96% and 70%, for training and testing accuracy, respectively. Furthermore, the system accurately turn-on the healing lamp based on the emotion identified.
Enhancing Children's Communication Skills Through an Interactive AR Board Game

Communication skills, which play an integral part of social life, encompass various types and methods. At its core, communicating involves transmitting data between two points while listening plays an integral part in processing and comprehending received messages. Today's digital environment means that over 60% of parents have received advice regarding their child's screen time from healthcare professionals or peer parents. This project takes advantage of this intersection between technology and learning by using Augmented Reality (AR) with an age-old board game called Snakes and Ladders to bring Augmented Reality into children aged 8-12's learning environment and foster family engagement through fun family interaction. The AR game promotes cognitive engagement through quizzes and questions at every turn, taking inspiration from popular AR models like Pokemon Go, AR Dragon and Harry Potter: Unite. The purpose is to help improve communication skills using immersive AR interactions; its touch-and-action mechanics currently make this available solely on mobile phones - providing modern skills development solutions.
Implementation of Potential Fields in a Differential Mobile Robot (TurtleBot2)

This paper focuses on the implementation of a method based on the physical principle of “Potential Fields.” A differential robot, known as TurtleBot2 (Kobuki), was utilized to address the problem of autonomous navigation. To perceive the environment, a laser-based object detection and measurement system was used, enabling TurtleBot2 to navigate complex environments by detecting and avoiding obstacles. Additionally, an NVIDIA Jetson Nano ARM architecture computer was utilized, equipped with a streamlined set of instructions for essential processing, enabling it to control the robot's angular and linear speed. The wireless connection between the computer and the TurtleBot2 enabled efficient information transfer and data processing. This wireless connection extends robot's working range based on Wi-Fi network coverage, ensuring safe navigation in restricted and challenging spaces. Tests were conducted in two distinct environments, presenting results from both simulation and experimentation, demonstrating the efficacy of the technique.
Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methods

In recent years, weakly supervised models have aided in mass detection using mammography images, decreasing the need for pixel-level annotations. However, most existing models in the literature rely on Class Activation Maps (CAM) as the activation method, overlooking the potential benefits of exploring other activation techniques. This work presents a study that explores and compares different activation maps in conjunction with state-of-the-art methods for weakly supervised training in mammography images. Specifically, we investigate CAM, GradCAM, GradCAM++, XGradCAM, and LayerCAM methods within the framework of the GMIC model for mass detection in mammography images. The evaluation is conducted on the VinDr-Mammo dataset, utilizing the metrics Accuracy, True Positive Rate (TPR), False Negative Rate (FNR), and False Positive Per Image (FPPI). Results show that using different strategies of activation maps during training and test stages leads to an improvement of the model. With this strategy, we improve the results of the GMIC method, decreasing the FPPI value and increasing TPR.
Autonomous Distribution of Programmable Multiqubit Entanglement in a Dual-Rail Quantum Network

We propose and analyze a scalable and fully autonomous scheme for preparing spatially distributed multiqubit entangled states in a dual-rail waveguide QED setup. In this approach, arrays of qubits located along two separated waveguides are illuminated by correlated photons from the output of a nondegenerate parametric amplifier. These photons drive the qubits into different classes of pure entangled steady states, for which the degree of multipartite entanglement can be conveniently adjusted by the chosen pattern of local qubit-photon detunings. Numerical simulations for moderate-sized networks show that the preparation time for these complex multiqubit states increases at most linearly with the system size and that one may benefit from an additional speedup in the limit of a large amplifier bandwidth. Therefore, this scheme offers an intriguing new route for distributing ready-to-use multipartite entangled states across large quantum networks, without requiring any precise pulse control and relying on a single Gaussian entanglement source only.