Reviewerly/exHarmony huggingface dataset:
MYbody: Augmented Reality Mobile App for Understanding Body Boundaries Using MADLC

Child abuse is a major problem in our society. This topic has aroused the curiosity of modern society. As a result, teaching children about the interrelationship of body boundaries should begin at a young age. In this project, it is proposed to develop a mobile app for a picture book AR that uses augmented reality (AR) as an advanced technology. Virtual objects projected through a display device augment the view of the physical book. The mobile app AR used the Mobile App Development Lifecycle (MADLC) methodology throughout the development process. The AR technique is marker-based, where the user follows a provided marker to activate the AR storybook. The expected outcome of this project is to raise awareness among parents and children about child sexual abuse. In addition, teachers can use MYbody AR mobile apps in the classroom to teach children about body boundaries. Future work of MYbody AR mobile apps includes adding more scenes as examples and working with the Ministry of Women, Family and Community Development to create additional content.
Detecting the Regions-of-Interest that Enclose the Tumors in Breast Ultrasound Images Using the RetinaNet Model

Ultrasound imaging provides an effective modality to diagnose breast cancer, but the task of interpreting breast ultrasound images is complex and challenging. Computer-aided diagnosis systems can enhance the accuracy of ultrasound-based breast cancer diagnosis, but the creation of these systems necessitates the utilization of effective techniques for detecting the region-of-interest (ROI) that encloses the tumor. The goal of our study is to investigate the effectiveness of utilizing the RetinaNet deep learning model for detecting the ROIs in breast ultrasound images. To the best of our knowledge, the RetinaNet model has not been employed previously to detect the ROIs in breast ultrasound images. To achieve this goal, the ultrasound image is processed using contrast enhancement and sharpening to synthesize an RGB image with the aim of improving the possibility of detecting the ROI. The synthesized RGB image is analyzed using a fine-tuned RetinaNet model to detect the ROI. The performance of the RetinaNet model was assessed and compared with four leading deep learning object detection models, using a dataset of 380 breast ultrasound images. The results demonstrate that the RetinaNet model outperformed the other models, with a failure rate of 7%, a mean recall value of 89%, a mean precision value of 89%, and a mean F1-score value of 88%. These findings suggest that the RetinaNet model provides a feasible and effective approach for detecting the ROIs in breast ultrasound images.
Design of a Low-Cost Prototype with 3D Printed Glasses for Real Time Eye-Tracking System

Eye- Tracking has proven to be very useful in several fields of application. Nowadays, there are wearable devices based on video-oculography, which are less invasive compared to other techniques. Therefore, it is proposed an eye-tracking prototype using a small camera and a microcontroller to stream data to a remote server. This hardware was installed in a printed 3D glasses frame. In addition, a real-time model was implemented in the server to process the eye images and identify pupil movements in four directions. Several volunteers participated to evaluate the prototype with a comfort scale and to validate the algorithm.
The Role of WhatsApp Group to Support Post Pandemic Teaching and Learning

This study looked at how private university students in Malaysia perceived and accepted the use of WhatsApp as a tool for online teaching and learning. The study employed a quantitative approach, gathering data from 146 participants through an administered survey. The results showed that the students had a positive perception of the usefulness, ease of use, and connection to learning of WhatsApp as a learning support tool, as well as a high level of acceptance of technology. Overall, it appears that these students were prepared and open to using WhatsApp as part of their education, both in and out of the classroom.
Improved Kidney Outlining in Ultrasound Images by Combining Deep Learning Semantic Segmentation with Conventional Active Contour

Computer-aided diagnosis systems are used to process kidney ultrasound images with the goal of providing the clinicians with objective, computer-based analyses to improve the diagnosis accuracy. A crucial requirement to develop these systems is to accurately outline the kidney in ultrasound images. The majority of the computer-based methods that are proposed for outlining the kidney in ultrasound images are based on conventional segmentation approaches, such as active contour algorithms. Recently, deep learning semantic segmentation models have been employed to outline the kidney in ultrasound images. However, limited work has been made to investigate the possibility of combining conventional segmentation methods with the recently introduced deep learning semantic segmentation models to achieve effective outlining of the kidney. This study investigates the feasibility of combining deep learning semantic segmentation with a conventional active contour segmentation algorithm with the goal of improving the outlining of the kidney in ultrasound images. In particular, the performance of three deep learning semantic segmentation models, which are configured to use different backbone convolutional neural networks, has been studied in terms of their ability to detect the pixels that belong to the kidney in the ultrasound image. The best performing semantic segmentation approach is used to generate an initial outline for the kidney. Furthermore, a conventional edge-based active contour segmentation algorithm is used to refine and improve the initial kidney outline. The results indicate that our proposed approach for combining the deep learning semantic segmentation with the conventional edge-based active contour segmentation has achieved effective kidney outlining with Precision, Recall, F1-score, Accuracy, and Jaccard values of 94.2%, 89.6%, 91.0%, 98.4%, and 84.5%, respectively.
Programmed Blood Boundary Identification Utilizing Matlab

Blood and its components are main indicator tool in determining many pathological conditions. The deficiency of red blood cells, which constitutes 99% of blood cells and specialized as an oxygen carrier, causes various blood disorders. The diagnosis of blood cells manually is tedious and time consuming that could be simplified utilizing automatic analysis. We aim to automate the process by means of making a system that will detect RBC and WBC count along with detecting the level of hemoglobin and cancer cell detection. We aim to use processing along with machine learning to create this system that will provide RBC as well as WBC count of the user and will provide an accurate hemoglobin level for the sample provided by the user. We will use multiple image processing techniques and machine learning technique to achieve this desired result. With the advent of image processing, neural network microscopic photographs can be processed for the parameters required. The input of the system will be an image of the blood sample that will be acquired using microscopic camera and will be stored in database. Various algorithm are being applied on the image to count different parameters from it and accordingly the output will be displayed on the screen. In simple words, this system will prove to be cost effective and will not be time consuming as it will be using the best algorithms to automate the system.
Deep Learning-Based Data Hiding

Data hiding, also known as steganography, is an essential technique for concealing sensitive information within digital media to ensure secure communication and protect data confidentiality. After 1950, in recent years, deep learning methods have revolutionized various domains, including computer vision and natural language processing. This paper introduces a robust watermark embedding technique using CNN (Convolutional Neural Network). By leveraging the power of deep neural networks and using the transform domain technique as DWT (Discrete Wavelet Transform) and also the domain-specific technique as SVD (Singular Value Decomposition), we propose an efficient and robust data-hiding framework that surpasses traditional methods in terms of capacity, security, and imperceptibility. Experimental results demonstrate the effectiveness of our proposed approach in various scenarios, highlighting its potential for real-world applications.
CODE SWITCHING AND CODE MIXING JAPANESE AND INDONESIAN: AN INTERACTION ON VLOG CHANNEL

Code Switching and Code Mixing are one of the pragmatic studies in the sociolinguistics field. The rapid language phenomena related to it is in line with the development of information technology facilitated by the internet. This linguistic event is marked by the symptoms of multilingualism and multiculturalism. The purpose of this research is formulated as follows; 1) Explain the causes of Code Switching and Code Mixing in the Indonesian and Japanese YouTuber Channel video blogs, and 2) Describe the functions of Code Switching and Code Mixing in the Indonesian and Japanese YouTube Channel video blogs. Both of these goals can be achieved through an inventory of data related to Code Switching and Code Mixing from conversations in vlogs. This research used descriptive qualitative meyhod by utilizing Holmes' theory. From the analysis conducted, the important points from the Code Switching aspect occur because; 1) the communication style of speakers and recipients of speech, 2) the presence of people and or third parties, and the need to explain something in a certain context, 3). Then, from the aspect of Code Mixing in general, it is caused by 1) multilingual speakers and interlocutors, 2) the influence of multicultural life, and 3) accidentally using something from another language. Based on the discussion, it can be seen that Code Switching and Code Mixing occur because of one's habits, needs, and speaking style, and fill in the blanks, create familiarity, and to reinforce something. These conclusions answer the objectives of the research, namely 1) to explain the causes of Code Switching and Code Mixing, and 2) to describe the functions of Code Switching and Code Mixing. Of course with the object of the Indonesian and Japanese YouTube Channel video blogs, and at the same time the main data source. Specifically, this research has shown that Code Switching and Code Mixing under certain conditions and situations may be a transfer of language usage not only to the two languages but more than that.
DEVELOPMENT OF LITERARY COMICS AS A SOURCE OF INDEPENDENT LEARNING FOR JUNIOR HIGH SCHOOL STUDENTS

This research aims to develop literary comic media as a source of independent learning for junior high school students, it produce decent comic products, and it find out the differences in the results of the application of comic media in the experimental class and the control class. This research used the Research and Development research design. The trials were carried out at SMPN 177 Jakarta and SMPN 235 Jakarta. This usage trial used the True Experimental Design class VII-D at SMPN 177 Jakarta and class VII-A at SMPN 235 Jakarta. The instruments provided a product requirement questionnaire with 14 questions and a test with 2 questions. Data analysis used the independent t-test which contained a different test of 2 independent and hypothetical samples using the Mann Withney test where there were differences in the results of independent learning for junior high school students between using comic media and not using comic media. This is evident from the results of the calculation of the Mann Withney test, so it can be concluded that there are differences in the results of independent learning for junior high school students, and literary comic media is suitable for use as a learning medium. The results of this study are expected for related parties who can benefit from them, such as teachers, students and further researchers.
Adaptive clustering algorithm based on improved marine predation algorithm and its application in bearing fault diagnosis

&lt;abstract&gt;&lt;p&gt;In cluster analysis, determining the number of clusters is an important issue because there is less information about the most appropriate number of clusters in the real problem. Automatic clustering is a clustering method that automatically finds the most appropriate number of clusters and divides instances into the corresponding clusters. In this paper, a novel automatic clustering algorithm based on the improved marine predator algorithm (IMPA) and K-means algorithm is proposed. The new IMPA utilizes refracted opposition-based learning in population initialization, generates opposite solutions to improve the diversity of the population and produces more accurate solutions. In addition, the sine-cosine algorithm is incorporated to balance global exploration and local development of the algorithm for dynamic updating of the predator and prey population positions. At the same time, the Gaussian-Cauchy mutation is combined to improve the probability of obtaining the globally optimal solution. The proposed IMPA is validated with some benchmark data sets. The calculation results show that IMPA is superior to the original MPA in automatic clustering. In addition, IMPA is also used to solve the problem of fault classification of Xi*an Jiaotong University bearing data. The results show that the IMPA has better and more stable results than other algorithms such as the original MPA, whale optimization algorithm, fuzzy C-means and K-means in automatic clustering.&lt;/p&gt;&lt;/abstract&gt;
Opportunities and Challenges of Postgraduate Entrance Examination Preparation Within an Inclusive Education Context in China

There have been many researchers who have gone into the adverse effects that COVID-19 has had on the education sector, however the various types of examinations in the context of the COVID-19 pandemic have not received much attention, and it is on this basis that the current study was undertaken. More specifically, this study employed a qualitative descriptive case study design, collecting data through semi-structured interviews with five enrolled undergraduates majoring in biology regarding their preparation for the Postgraduate Entrance ExaminationPostgraduate entrance examinations (PEE) (PEE) and analysing the data using thematic data analysis approach. Ultimately, the study concluded that the COVID-19 pandemic did not adversely affect their preparation, but in fact motivated them to study more intensely, and they felt that emotionally supportive conditions were more essential to their preparation. Finally, built on the research limitations of this study, suggestions for future related research are made.
Wide kernels and their DCT compression in convolutional networks for nuclei segmentation

The locality and spatial field of view of image operators have played a major role in image analysis, from hand-crafted to deep learning methods. In Convolutional Neural Networks (CNNs), the field of view is traditionally set to very small values (e.g. 3 × 3 pixels) for individual kernels and grown throughout the network by cascading layers. Automatically learning or adapting the best spatial support of the kernels can be done by using large kernels. Due to the computation requirements of standard CNN architectures, this has been little investigated in the literature. However, if large receptive fields are needed to capture wider contextual information on a given task, it could be learned from the data. Obtaining an optimal receptive field with few layers is very relevant in applications with a limited amount of annotated training data, e.g. in medical imaging. We show that CNNs (2D U-Nets) with large kernels outperform similar models with standard small kernels on the task of nuclei segmentation in histopathology images. We observe that the large kernels mostly capture low-frequency information, which motivates the need for large kernels and their efficient compression via the Discrete Cosine Transform (DCT). Following this idea, we develop a U-Net model with wide and compressed DCT kernels that leads to similar performance and trends to the standard U-Net, with reduced complexity. Visualizations of the kernels in the spatial and frequency domains, as well as the effective receptive fields, provide insights into the models’ behaviors and the learned features.
Enhancing Single Object Tracking With a Hybrid Approach: Temporal Convolutional Networks, Attention Mechanisms, and Spatial–Temporal Memory

Deep neural network-based tracking tasks have experienced significant advancements in recent years. However, these networks continue to face challenges in effectively adapting to appearance changes in both target and background, as well as linking objects after extended periods. The primary challenge in tracking lies in the frequent changes in a target’s appearance throughout the tracking process, which can potentially reduce tracker robustness when faced with issues such as aspect ratio changes, occlusions, scale variations, and confusion from similar objects. To address this challenge, we propose a tracking architecture that combines a temporal convolutional network (TCN) and attention mechanism with spatial-temporal memory. The TCN component empowers the model to capture temporal dependencies, while the attention mechanism reduces computational complexity by focusing on crucial regions based on context. We leverage the target’s historical information stored in the spatial-temporal memory network to guide the tracker in better adapting to target deformation. Our model attains a 63.0% average overlap (AO) on the GOT-10K dataset, a 71.9% success score (AUC) on OTB2015, a 65.6% success score (AUC) on UAV123, and achieves 56.9% accuracy on the VOT2018 dataset. These outcomes demonstrate the high effectiveness of our proposed tracker in tracking a single object.
Gamification and Virtual Reality for Tongue Rehabilitation

Purpose: Lingual exercises based on tongue movements are common in speech therapy.These exercises can be tedious for patients, but gamification and virtual reality (VR) with head-mounted displays (HMD) can serve as effective strategies to enhance their motivation and engagement.However, the use of these technologies can be challenging for therapists due to a lack of technological skills.Material and Methods: A new system to support HMD-based VR for gamified tongue rehabilitation exercises is proposed.The system offers a variety of games that challenge users to achieve their goals through tongue movements and sound interaction.These games support different interaction actions that can be set by therapists using easy-to-use editors.The system also provide functionalities for patient follow-up.The system has been implemented and tested considering different technologies such as mobile devices, personal computers, and HMD complemented with an external camera to properly capture the tongue movements.Results: The therapists found the system to be user-friendly, requiring no additional support for effective utilization.The system's versatility allows it to be used on mobile devices, as well as with augmented and virtual reality techniques, resulting in more engaging rehabilitation sessions.However, the sensibility of device movements to face detection strategies is a limiting factor of this configuration.In the case of using personal computers with HMD, better results are obtained and especially when virtual reality is considered.In this last case, it is better to consider illuminated scenarios to ensure the proper detection of facial movements.Conclusion: HMD-based VR for gamified tongue rehabilitation with ease to use editors to prepare sessions is a good strategy to improve patient engagement in tongue rehabilitation sessions.
Fast Planning and Tracking of Complex Autonomous Parking Maneuvers With Optimal Control and Pseudo-Neural Networks

This paper presents a framework to plan and execute autonomous parking maneuvers in complex parking scenarios.We formulate a minimum-time optimal control problem for trajectory planning, using an indirect optimal control approach. A novel smooth penalty function is devised for collision avoidance with optimal control, and an effective technique is adopted to compute an initial solution guess. The trajectory planning tasks are solved with low computational times, and a dense mesh is used to discretize the domain of the optimal control problems, resulting in accurate collision-free solutions. The planned parking maneuvers are tracked with an original pseudo-neural feedforward-feedback steering controller, which outperforms other techniques from the literature, and a feedback longitudinal controller, to drive a realistic 14-degree-of-freedom vehicle simulator. We validate the planning and tracking algorithms in challenging narrow parking scenarios, including reverse, parallel and angle parking, and unstructured environments. The framework robustness is assessed by changing the vehicle mass, the road adherence conditions, and by introducing measurement noise with realistic sensor models. A video of the trajectory planning and tracking results is available as supplementary material.
GPU-Accelerated Deep Learning-Based Correlation Attack on Tor Networks

The Tor network, renowned for its provision of online privacy and anonymity, faces the constant threat of correlation attacks that aim to compromise user identities. For almost two decades, these correlation attacks were based on statistical methods. However, in recent years, deep learning-based correlation attacks have been introduced to make them more accurate. Nevertheless, in addition to being accurate, fast correlation attacks on Tor are crucial for assessing the real-world viability of such attacks because reduced correlation time aids in estimating its practical implications. Moreover, a reduction in correlation time also helps improve efficiency and ensures practical relevance of the attack. The existing state-of-the-art implementation of a correlation attack on Tor suffers from slow performance and large memory requirements. For instance, training the model required 133 GB of memory, and correlating 10,000 flows takes about 976 seconds. In this paper, we present a novel GPU-based correlation strategy and a fast traffic flow loading technique to reduce time complexity by 7.12× compared to existing methods. Our computational approach, reliant on PyCUDA, facilitates the parallelization of operations used in the attack, thereby enabling efficient execution through the utilization of GPU architecture. Leveraging these two approaches, we introduced an improved correlation attack, which shows high accuracy and fast performance compared to state-of-the-art methods. Moreover, we address resource limitation issues by reducing memory consumption by 47.37% during the training phase, which allows the model to be trained with much fewer resources.