Reviewerly/exHarmony huggingface dataset:
QKBAKA: A Quantum-Key-Based Authentication and Key Agreement Scheme for Internet of Vehicles

With the rapid development of intelligent connected vehicles, networked vehicles carry a large amount of sensitive data. Secure authentication and controlled access to data in the Internet of Vehicles (IoV) are essential to protect vehicular private data. We propose a quantum-key-based authentication and key agreement scheme for IoV, called QKBAKA. Its features are as follows: i) Role-based data access control strategy. Access to private data requires authorization and management by vehicle owners. ii) Vehicle privatization manages quantum session keys. The quantum random number generator (QRNG) deployed on the vehicle side generates quantum session keys and realizes secure agreement through multi-level quantum keys. The multi-level quantum key includes the quantum pre-filled key, the quantum protection key and the quantum session key. iii) Secure and efficient authentication scheme. QKBAKA uses quantum keys, symmetric encryption algorithms and hash algorithms. It is relatively secure for quantum computing and has better performance. Security analysis shows that QKBAKA is secure under the real-or-random (ROR) model and meets the security requirements of IoV. Performance evaluation shows that, compared with the existing schemes, QKBAKA reduces computation and communication costs by 74.83%–98.59% and 28.7%–83.1%. Simulation and real vehicle tests show that QKBAKA has good communication stability and feasibility.
Deep Learning Approach for Predicting Prostate Cancer from MRI Images

According to medical data, prostate cancer has been one of the most lethal malignancies in recent years. Early detection of prostate cancer significantly influences the tumor's treatability. Image analysis software that operates using a machine learning or deep learning algorithm is one of the techniques utilized to aid in the early and rapid identification of prostate cancer. This paper evaluates the performance of three deep learning Convolutional neural network (CNN) algorithms in detecting prostate cancer. Using Python, three deep learning models, ResNet50, InceptionV3, and VGG16, are subsequently created on the Kaggle platform. These three models have been applied to various medical image diagnostic problems and have won several contests. This study used 620image samples from the Cancer Imaging Archive (TCIA) data source. Accuracy, f1 score, recall, and precision are used to evaluate the performance of the three models. The extracted test results indicate that the VGG16 achieves the highestlevel of accuracy at 95.56percent, followed by the ResNet50 at 86.67percent and the InceptionV3 at 85.56percent.
Peningkatan Kemampuan Membaca dengan Metode Demonstrasi pada Siswa Kelas III SD Negeri Duren Bandungan

This study aims to improve reading skills using the demonstration method for third grade students at Duren Elementary School, Bandungan District, Semarang Regency. The type of research used is classroom action research (PTK). The research subjects were class III students at SD Negeri Duren Bandungan with a total of 25 students in the even semester of the 2022-2023 academic year. Data collection techniques with the performance of students' reading abilities, observing student activities and observing teacher skills during the learning process take place. The results showed that the reading ability of class III students from cycle I increased by 32% to 88% in cycle II. The results of observations of student activity in the conversion of scale 4, namely in the first cycle of 2.11, qualified enough to increase in the second cycle of 3.33 with good qualifications. The results of observations of teacher skills on a scale of 4, the first cycle of 2.11 qualified enough to increase in the second cycle of 2.45 with good qualifications. Based on the results of this study indicate that the use of the demonstration method can improve the reading ability of class III students at SD Negeri Duren Bandungan.Keywords: reading, reading ability, Demonstration Method
Peningkatan Aktivitas dan Hasil Belajar Menulis Teks Berita melalui Metode Pembelajaran Berbasis Masalah Siswa Kelas VIII C Semester Gasal SMP Negeri 1 Pabelan 2017/2018

This research aims to find out whether the learning activity of writing news texts for class VIII C students at SMP Negeri 1 Pabelan for the 2017/2018 academic year can be improved through problem-based learning methods and to find out what the learning outcomes for writing news texts for students at class VIII C at SMP Negeri 1 Pabelan are. The 2017/2018 academic year can be improved through problem-based learning methods.This research is classroom action research designed to be carried out in 2 cycles. Each cycle consists of planning, action, observation and reflection stages. The subjects of this research were students in class VIII C of SMP Negeri 1 Pabelan for the 2017/2018 academic year. This class was chosen because the results of learning to write news texts for this class were still below the expected minimum completeness criteria, namely 71.82.The research results showed that in cycle II the average score for students' learning activities to write news texts was 86.07 (high), while the average learning outcomes for students in writing news texts in cycle II reached a score of 85.51 (good). Classically, students have completed their studies because 93.75% of students have obtained a score of â‰¤ 76. Thus, the success indicator for this research has been achieved in cycle II so that this research can be declared successful in increasing students' activities and learning outcomes in writing news texts.Keywords: learning activities, learning outcomes, problem-based learning.
A DIGITAL SOLUTIONS FOR AGRICULTURE: BUILDING A DEMAND AND SUPPLY (D&amp;S) PORTAL FOR MONITORING AGRICULTURAL PRODUCTS

This study addresses these challenges by exploring the issues faced by the agriculture industry, particularly the need for marketing information, difficulties in reaching potential purchasers, and the need for effective marketing strategies. However, effectively managing this sector's complex supply and demand dynamics presents substantial challenges. The primary objective of this research is to develop a user-friendly agriculture portal called a Demand and Supply (D&amp;S) Portal that empowers small-scale farmers to make informed decisions about marketing their products. The Agile methodology guides the development of this portal, ensuring flexibility, adaptability, and iterative development. The research process incorporates rigorous literature reviews and problem recognition as foundational steps before system development. The evaluation of the portal indicates high user satisfaction. Respondents found the portal's features for managing and monitoring agricultural product demand and supply straightforward and valuable. The portal's usability and efficiency were well-received, with users expressing their intention to recommend it to others. It serves as a reference model for developers and researchers in the agricultural sector, offering valuable insights into demand and supply management. Future enhancements include optimising the portal for mobile use, incorporating product images and pricing information, and enhancing security features. The D&amp;S Agri Portal is a promising tool for empowering small-scale farmers and improving the efficiency of agricultural transactions. Continuous user feedback and technological adaptations will enhance its usability and effectiveness in facilitating agricultural demand and supply management. This research contributes to understanding the agriculture industry's challenges and the potential solutions to address them. It also highlights the importance of empowering small-scale rural farmers through effective marketing platforms.
Unraveling the Interplay of Language and Culture in BIPA Learning for International Students

This research aims to describe the role of language and culture in the success of international students in BIPA learning at AndalasUniversity. The method used in this research is descriptive. The data used is data obtained from observations, interviews and documentation on the role of language and culture with BIPA students at Andalas University. Data collection techniques use observation, interviews and documentation techniques. The data source in this research is interviews with Andalas University students. According to the results of the analysis, it was found that 24 language roles and 30 cultural roles were known to Andalas University international students.
RPA: a memory-efficient metric-space recall@&amp;lt;italic&amp;gt;R&amp;lt;/italic&amp;gt; ANNS index

Approximate nearest neighbor search (ANNS) for high dimensional data has received extensive research efforts.Many existing ANNS methods in metric spaces are essentially based on the permutation of pivots, or pre-selected reference points, which are ordered by their distances to the data.Unfortunately, most of these permutation-based methods are quite memory demanding, because of either complexity of index structure, excessive pivots, or insufficient exploitation of distances.This paper proposes the reduced permutation array (RPA), a delicate integration of existing technical elements based on in-depth understanding, for recall@R ANNS in metric spaces.As an array, for each data element, RPA stores only l identities (IDs) of its nearest pivots out of k pre-selected ones (l ≪ k).During search, the order of distances from each data element to the query is approximated by a score function based on the distances from its nearest pivots to the query, and a bounded heap is maintained to store R candidate results.RPA is simple, memory-efficient, and scalable.Experiments show that RPA is approximately 3 times more memory-efficient than existing methods on average.The research results can provide an effective ANNS method for massive data in a single-machine environment with limited memory resources.
Mobile-Assisted Listening Instructions with Jordanian Audio Materials: A Pathway to EFL Proficiency

This research examined the impact of mobile devices and Jordanian audio resources on the listening abilities of English as a foreign language (EFL) learners. The research included 100 EFL students from Jadara University in Jordan, with 50 students in the experimental group and 50 students in the control group. Quantitative research and ANOVA testing were used to analyze the data. Both groups were pre- and post-tested on listening comprehension, vocabulary, and proficiency. ANOVA was used to compare the mean scores between the experimental and control groups. The ANOVA test determined the impact of mobile devices and Jordanian audio on participants’ listening abilities. The test compared the mean scores of the experimental and control groups to determine whether the intervention significantly improved listening skills. The ANOVA test showed that mobile devices and Jordanian audio resources improved the listening abilities of EFL learners. The experimental group had a superior understanding of vocabulary and listening skills compared to the control group. These studies show that mobile devices and realistic audio content improve EFL listening training. ANOVA showed that this training strategy improved listening skills. This research contributes to the literature on EFL pedagogy literature and assists EFL teachers and curriculum designers in enhancing their listening skills. Future studies may examine other factors that could impact the effectiveness of mobile devices and audio resources in EFL listening training, including intervention duration and learner proficiency. This research shows that these materials can enhance the listening experiences of EFL student.
Design and Development of Sign Language Learning Application for Special Needs Students Based on Android Using Flutter

Sign language is a communication system used by individuals with hearing impairments to interact and convey messages without using verbal sounds. Sign language has its own unique grammatical structure and vocabulary, enabling sign language users to express ideas and emotions through facial expressions, hand movements, and body positions. In this research, we explore important aspects of sign language, including its uniqueness, complexity, and diversity across different countries and cultures. We also discuss the role of sign language in facilitating communication, education, and social inclusion for the deaf community. This research highlights the significance of introducing and developing sign language competence among the general public, educators, and professionals in the fields of healthcare and social services. The development of the sign language learning media utilizes the Multimedia Development Life Cycle method. The main application used in creating the sign language learning media is Flutter, along with supporting applications such as Adobe Illustrator 2020 and Adobe Photoshop CC 2019. The features included in this application are finger alphabet signs, number signs, video tutorials for numbers and letter signs, quizzes, and learning objectives.
Design of Sign Language Learning Media Based on Virtual Reality

Sign language has become a very important means of communication for individuals with hearing loss, allowing them to interact with people who are not hearing impaired. In recent years, virtual reality (VR) technology has advanced rapidly, opening up new opportunities to enhance the user experience in various situations. This study aims to design and develop an easy-to-use virtual reality-based interactive sign language application. The software development method used is the Software Life Cycle Development Method (MDLC), which includes the stages of analysis, design, implementation and testing. This app is designed to run on affordable and popular VR devices to make it accessible to as many individuals as possible. In the development process, we conducted an analysis of various sign languages used globally, focusing on hand gestures and facial expressions which have an important role in sign language communication. Users can learn and practice sign language through interactive experiences in a virtual environment that mimics real-life situations. This application is also equipped with visual and audio feedback features to help users improve and hone their skills. At the testing stage, this application was evaluated through trials involving participants with diverse backgrounds in the use of sign language. The test results show that users feel involved in the VR experience and experience increased confidence in using sign language after using this application. This research proves that VR technology can be an effective tool in sign language learning and development. Thus, the virtual reality-based sign language application that has been designed and developed in this study makes a significant contribution in increasing the accessibility of communication for individuals with hearing impairments. The implications of this research can be the basis for further development in the field of sign language and VR technology.
On the Rate Redundancy of Uniform Scalar Quantization and Golomb-Rice Coding

In this paper, we consider rate redundancy not only of uniform scalar quantizer (USQ) for the assumed Laplacian source, but also of USQ coupled with Golomb-Rice coding. For a given bit rate of USQ, we select the key parameter of the Golomb-Rice coder so that the rate redundancy of USQ with Golomb-Rice coding is minimal. We further determine and analyse the robustness of the rate redundancy to changes in the standard deviation mismatch over a relatively wide range of values. The analysis presented in the paper is very useful, since in numerous systems with limited memory resources, any savings achieved by means of compression can indeed be beneficial.
Image Denoising by Incorporating Noisy Image Patch and Impulse Response of Low-Pass Filter in CNN Learning

In this paper, we present a novel image denoising approach which incorporates the noisy image patch and the impulse response of low-pass filter (LPF) in convolutional neural network (CNN) learning. We demonstrate the performance of the proposed method for spatially variant additive white Gaussian noise (AWGN) and realistic noise removal. Our method outperforms the conventional fast and flexible denoising CNN (FFDNet) in preserving image information and perceptual quality, particularly in high noise variance.
A Fundamental Study on Gaze Tracking for Programming Learners Based on Hierarchical Segmentation

Our research is aimed at building a system that estimates the parts of a program that learners do not understand based on eye gaze information and provides appropriate support for novice programmers. In this paper, we propose a method for dividing the display area of a program shown on the screen into a hierarchical structure in order to utilize eye gaze information to assist novice programmers. In order to estimate the comprehension of programs that include structures such as repetition and branching using eye gaze information, it is necessary to conduct analysis using a model that reflects the structure of the program. We show that the hierarchical approach proposed in this paper can simplify and represent complex eye movement.
Monocular Depth Estimation: A Survey

Monocular depth estimation is an ill-posed task in computer vision, which holds great significance in the fields such as artificial intelligence, virtual reality, augmented reality, path planning, unmanned driving, and navigation guidance. The primary objective of monocular depth estimation is to predict the depth value of each pixel or infer depth information, given just a single red-green-blue (RGB) image as input. Traditional monocular depth estimation methods rely on limited depth cues, such as strict scene conditions. With the significant advancements in computer vision and artificial intelligence, monocular depth estimation using deep learning has been extensively researched and has yielded substantial results. This paper presents a comprehensive survey of monocular depth estimation. Firstly, we give an overall introduction to monocular depth estimation and explain it from traditional and deep learning-based methods, respectively. To specify, supervised, self-supervised and semi-supervised models are described in detail in deep learning-based methods. Additionally, we introduce publicly available benchmark datasets and evaluation metrics commonly used in this field. Finally, we discuss the current challenges and promising prospects for the development of monocular depth estimation.
Development of Advanced Control Strategy Based on Soft Actor-Critic Algorithm

The paper demonstrates the differences between two reinforcement learning methods – the Actor-Critic (AC) and the Soft Actor-Critic (SAC) applied for the benchmark pendulum and double pendulum control problems. The advantages and disadvantages of both methods as well as proposed modifications of the algorithms are discussed. The neural network has been developed using the Python and Tensorflow 2 libraries, and PyBullet Envs was used for environment simulations the case study.
Parameter Estimation of LFM Signals Based on FOTD-CFRFT under Impulsive Noise

Due to the short duration and high amplitude characteristics of impulsive noise, these parameter estimation methods based on Gaussian assumptions are ineffective in the presence of impulsive noise. To address this issue, a LFM signal parameter estimation method is proposed based on FOTD and CFRFT. Firstly, the mathematical expression of FOTD is presented and its tracking performance is verified. Secondly, the tracked signal is subjected to discrete time CFRFT, and a mathematical optimization model for LFM signal parameter estimation is established on the fractional spectrum characteristic. Finally, a correction method for non-standard SαS distributed noise is proposed, and the performance of parameter estimation under both standard and non-standard SαS distributions are analyzed. The simulation results show that this method not only effectively suppresses the impact of impulsive noise on the fractional spectrum of LFM signal, but also has better parameter estimation accuracy and stability in the low GSNR. The proposed method is particularly effective under the measured noise environment, as it successfully suppresses the impact of impulsive noise and achieves high-precision parameter estimation.
Automation of Databases in Oracle

Abstract: Ever since notable advancements were made concerning Database Management and regulation in the early 2000s, Automation of Databases in the Oracle environment have long been a crucial topic for research and development within the Computer Science and Information Technology sector. This research paper aims to explain what all factors necessarily require automation whilst bringing into light the growing demand for automation database management. As organizations across the globe face the imminent issue of overwhelming volumes of data, efficient database management systems that offer minimum complications are highly in demand. The paper not only highlights automation of various tasks such as routine maintenance, performance tuning, backup and recovery routine, but also emphasises the importance of security enhancements, real-time monitoring and scalability whilst placing forth key requirements of automation in general. The research presents a smarter and more robust database system model that can manage itself without the need of constant monitoring or intervention. Furthermore, we discuss the combining of cloud-based technologies such as Oracle Cloud and other containerization techniques to make the database more flexible and sustainable, all while keeping in mind the importance of having absolute rules and procedures for managing data in an automated system, which ensures that the data is well-organized, complies with legal requirements and is ready to be audited for accountability if needed. To conclude, the paper suggests nurturing practices of setting up systems that keep track of changes and versions to automate the infrastructure and managing of databases more consistently and efficiently. As organizations delve deeper into the landscape of database automation, they inadvertently face several challenges such as accuracy, security and accessibility of the data. This research paper specifically explores the requirements that promise to reshape the future of data management.