Reviewerly/exHarmony huggingface dataset:
Evaluation of the Usability of the Animated Infographic Module on Arabic Grammar Learning Based on the TUP Model

For today's youth, using technology into educational activities has become essential. The availability of technological resources, such as tablets, laptops, and smartphones, enables students to swiftly access course content. The study of Arabic grammar is a challenging subject, and educators use technology as a teaching tool. Using infographic programmes, which emphasise information presentation in an easy-to-understand and engaging manner, is one of them. This study was conducted to evaluate the usability of an animated infographic module that has been developed through a Design and Development Research (DDR) approach. This usability evaluation is based on the TUP Model (Bednarik, 2002). This model includes technological aspects, module usability aspects and pedagogical aspects. A semi-structured interview was used as a research instrument involving five respondents. Qualitative interview data is structured into themes based on the model. The researcher also used the document analysis method through student feedback notes in the Padlet application after the last learning session. The results show that students are satisfied with the usability aspects of the module and are suitable for use as simple and effective learning materials.
Evaluation Metrics for Augmented Reality in Neurosurgical Preoperative Planning, Surgical Navigation, and Surgical Treatment Guidance: A Systematic Review

BACKGROUND AND OBJECTIVE: Recent years have shown an advancement in the development of augmented reality (AR) technologies for preoperative visualization, surgical navigation, and intraoperative guidance for neurosurgery. However, proving added value for AR in clinical practice is challenging, partly because of a lack of standardized evaluation metrics. We performed a systematic review to provide an overview of the reported evaluation metrics for AR technologies in neurosurgical practice and to establish a foundation for assessment and comparison of such technologies. METHODS: PubMed, Embase, and Cochrane were searched systematically for publications on assessment of AR for cranial neurosurgery on September 22, 2022. The findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: The systematic search yielded 830 publications; 114 were screened full text, and 80 were included for analysis. Among the included studies, 5% dealt with preoperative visualization using AR, with user perception as the most frequently reported metric. The majority (75%) researched AR technology for surgical navigation, with registration accuracy, clinical outcome, and time measurements as the most frequently reported metrics. In addition, 20% studied the use of AR for intraoperative guidance, with registration accuracy, task outcome, and user perception as the most frequently reported metrics. CONCLUSION: For quality benchmarking of AR technologies in neurosurgery, evaluation metrics should be specific to the risk profile and clinical objectives of the technology. A key focus should be on using validated questionnaires to assess user perception; ensuring clear and unambiguous reporting of registration accuracy, precision, robustness, and system stability; and accurately measuring task performance in clinical studies. We provided an overview suggesting which evaluation metrics to use per AR application and innovation phase, aiming to improve the assessment of added value of AR for neurosurgical practice and to facilitate the integration in the clinical workflow.
Project-based Learning to Promote High School Students Descriptive Writing

In high school, EFL learners are supposed to have the capability of writing a good descriptive text as one of the demand of the curriculum. However, many students still experience problems in producing a successful writing. This study aims at promoting high school students’ writing skills, particularly in writing descriptive text, by utilizing project-based learning. This study employed a Classroom Action Research through two cycles which include planning, acting, observing, and evaluating stages. The data were collected from 23 students of X IIS 4 class of SMA Negeri 4 Takalar as the study subjects by using writing tests and observation. The results of the study show that there was an improvement in students’ scores and behaviour when learning with project-based approach. In the diagnostic test, the students mean score was 65.57. The score improved by 9.9% in cycle I and 13.81% in cycle II to 72.09 and 82.04, respectively. Besides, in the pretest, only five students who can achieve the minimum completeness criteria, but in the end of cycle 2, twenty students can pass the minimum score. In addition, students become more actively engaged in the learning process as they are enthusiastic with the projects given by the teacher. This means that the use of project-based learning gave positive impact to students’ learning to write descriptive paragraphs and teachers are suggested to use it in teaching in EFL writing classrooms.
Underwater Object Detection in Marine Ranching Based on Improved YOLOv8

The aquaculture of marine ranching is of great significance for scientific aquaculture and the practice of statistically grasping existing information on the types of living marine resources and their density. However, underwater environments are complex, and there are many small and overlapping targets for marine organisms, which seriously affects the performance of detectors. To overcome these issues, we attempted to improve the YOLOv8 detector. The InceptionNeXt block was used in the backbone to enhance the feature extraction capabilities of the network. Subsequently, a separate and enhanced attention module (SEAM) was added to the neck to enhance the detection of overlapping targets. Moreover, the normalized Wasserstein distance (NWD) loss was proportionally added to the original CIoU loss to improve the detection of small targets. Data augmentation methods were used to improve the dataset during training to enhance the robustness of the network. The experimental results showed that the improved YOLOv8 achieved the mAP of 84.5%, which was an improvement over the original YOLOv8 of approximately 6.2%. Meanwhile, there were no significant increases in the numbers of parameters and computations. This detector can be applied on platforms for seafloor observation experiments in the field of marine ranching to complete the task of real-time detection of marine organisms.
A Dynamic Model for the Computation of Gesture Types for Image-based Software Agents

&lt;p&gt;Computer vision technology allows the computer to interact with a human operator to quickly complete the interpretation of events and improve the operational workflow of processing events. As computer vision technology continues to evolve, the cost of the equipment continues to increase. Therefore, the stability of the system can be ensured through the design of the middleware and the calculation of the auxiliary functions of the software agents. At present, the recognition of characters for image processing is based on the technology of image recognition, which can provide a more flexible user experience. However, the dilemma of contactless design lies in the processing and calculation of images, which should reduce the inconvenience caused by delays.This article uses a Raspberry Pi as an example of a computing proxy application. After the visual inspection, the verification operation of the software is carried out. Our system is the detection of hand position and movement, and the detection of hand mark position in reconnaissance. In addition, we simultaneously developed management and remote control events and connected to the remote edge computer. After that, we successfully completed the automatic control and serial application of two different edge computing recognition jobs, and verified the image vision computing based on the Raspberry Pi software agent, which can be used for image vision analysis and control applications.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
TransDDPM: Transformer-Based Denoising Diffusion Probabilistic Model for Image Restoration

Although diffusion models have achieved impressive success for image generation, its application for image restoration is still underexplored. Following tremendous success in natural language processing, transformers have also shown great success for computer vision. Although several researches indicate that increasing transformer depth/width improves the applicability of diffusion models, application of Transformers in diffusion models is still underexplored due to quadratic complexity with the spatial resolution. In this work, we proposed a Transformer-based Denoising Diffusion Probabilistic Model (TransDDPM) for image restoration. With multi-head cross-covariance attention (MXCA), TransDDPM can operates global self-attention with cross-covariance matrix in channel dimension rather than spatial dimension. Another gated feed-forward network (GFFN) is included to enhance the ability to exploit spatial local context. Powered by these designs, TransDDPM is capable for both long-range dependencies and short-range dependencies and flexible for images of various resolutions. Comprehensive experiments demonstrate our TransDDPM achieves state-of-the art performance on several restoration tasks, e.g., image deraining, image dehazing and motion deblurring.
A Multi-Faceted Encryption Strategy for Securing Patient Information in Medical Imaging

In modern healthcare, transmitting digital medical images through open-source networks for diagnosis in remote centers poses a significant security challenge due to the sensitive patient information involved. This paper presents an algorithm designed to encrypt these crucial medical images comprehensively. The algorithm generates hash code to validate image integrity, followed by feature-based watermarking, transformation into the frequency domain via Discrete Cosine Transform (DCT), and encryption using Advanced Encryption Standard (AES) and RSA encryption techniques. Each step plays a pivotal role in fortifying the images against unauthorized access, tampering, or interception during transmission, Guaranteeing the secrecy, unaltered state, and legitimacy of the included medical information. Implementing this encryption algorithm mandates strict adherence to cryptographic best practices, robust execution of encryption algorithms, secure key management, and compliance with industry standards. These meticulous measures bolster the confidentiality and integrity of medical images, which are crucial in protecting patient privacy and maintaining data integrity within healthcare systems. This comprehensive encryption strategy addresses the need for the purpose of ensuring safe and protected delivery and protection of sensitive medical information across networks.
Object-Aware Transfer-Based Black-Box Adversarial Attack on Object Detector

Deep neural networks have been demonstrated to be vulnerable to adversarial noise from attacks. Compared with white-box attacks, black-box attacks fool deep neural networks to yield erroneous predictions without knowing the model parameters. Black-box attacks include query-based attacks and transfer-based attacks; the former rely on querying the model while the latter just rely on the transferability of adversarial examples, thus challenging. Existing transfer-based black-box adversarial attack methods focus on the image classification task. Especially, we empirically verify that those methods struggle to balance the attack on objects with different classes and sizes, and thus they perform poorly in the attack on object detectors. In this work, we propose an Object-Aware mechanism to address this issue. It includes Object-Wise Gradient (OWG) calculation to balance the attack on multiple objects and a Domain-Division Map (DDM) to weigh the attack in size. Incorporating our method with seminal baselines (e.g., I-FGSM, MI-FGSM), we achieve superior attack performance on multiple object detectors (e.g., Faster R-CNN, DETR, SSD), which justifies the effectiveness and generality of our method.
Multi-Robot Path Planning Using Nash Equilibrium Based Game Theory

With, human life becomes more and more reliant on robots, accurate path planning of the robot is essential. The utility and significance of a multi-robot system are determined by its ability to reach the desired location and complete the given task. Therefore, the proposed approach using game theory for multi robot path planning holds great potential for improving the efficiency and safety of robotic systems in a variety of applications. The proposed system consists of two robots that will follow a predetermined path. To select the robot, the electrical battery level and mechanical robustness are considered, and the best performing robot will be selected. Based on the performance scores for the battery level and the score for mechanical robustness a Game Theory algorithm is implemented based on Nash equilibrium to achieve optimal path planning, which treats each robot as a player in a game. A Nash equilibrium is a specific condition of Game Theory where no robot can improve its outcome by changing its strategy. The work is implemented on two robots in the laboratory.
Detecting Facial Expressions and Recognition in a Mask Wear Person

In the field of research, the automatic detection and classification of facial expressions holds significant importance for understanding human emotions, essential to effective communication. However, the advent of COVID-19 introduced an unexpected challenge the widespread wearing of face masks, which conceal key facial cues. Addressing this, our proposed technology integrates low-light image enhancement, facial landmark detection via Deep Convolution Neural Networks (DCNN), and the efficiencies of transfer learning using pre-trained MobileNetV3 models. Impressively achieving a 72.3% accuracy on the AffectNet dataset, this innovation promises to transform sectors ranging from healthcare, where understanding patient emotions is vital, to education, retail, and even security. Beyond just enhancing human-machine interfaces, this system fortifies interpersonal communication in our masked era, ensuring that emotions are consistently communicated and understood.
English to Konkani Translator Using Hindi as a Pivot Language

This paper presents a novel approach for converting English speech to Konkani speech by utilizing the Hindi language as a pivot language. Our system employs a Python-based dictionary to map each Hindi word to its corresponding Konkani equivalent. The system is designed as a web application, which takes an English speech input and generates Konkani speech output. The proposed approach has the potential to enable Konkani-speaking individuals to communicate more effectively in various places such as educational, social, and business environments. Our approach provides a practical solution to the lack of resources available for Konkani speech processing. The method employed in our system is intuitive and easy to use, making it accessible for a wide range of users. If taken to mobile platform, it can help many people to converse with English speaking audience. Although there may be room for improvement, our approach offers a unique and viable solution for English-Konkani speech conversion.
A Study of Voice Control Implementation in a Web-Based VR Tourism Application

In recent years, public interest in Virtual Reality (VR) applications has increased. The development of VR applications has reached a point where the application can be developed in the website (web) platform. In that platform, one of the widely used input methods is the gaze. With gaze input, the users interact with the application by moving the device. The device can be a Head-Mounted display (HMD) or smartphone. However, this method still has drawbacks. The users may accidentally enter input while looking the other way. In addition, this input method cannot be used by users who cannot move their bodies due to motoric disabilities. According to other studies, the most natural input method for users to use is voice input. However, there is still very little research in Indonesia about how to determine the best Indonesian word to be used as the input commands. Therefore, this study will analyze how to implement the voice input system for web-based VR applications and calculate the level of user acceptance. This research shows that voice input doesn't necessarily provide a more immersive experience for the user and the user prefers to use short verb sentences as the input command.
Design of School Watching Mobile Application with Geotagging and Context Aware for Schools Reporting and Monitoring in Mount Semeru Disaster Area

Mount Semeru erupted on December 4, 2021, followed by hot clouds falling. The eruption caused significant destruction to one school, moderate damage to five schools, and impacted 19 schools with volcanic ash, resulting in the tragic loss of six students' lives. Subsequently, a disaster mitigation program named "School Watching" was implemented. It involved observing school environments and identifying hazardous elements to prepare for future disasters. Therefore, using the Design Thinking method, the researchers designed the user experience (UX) for the Semeru School Watch application to assist with the school environment's preparedness. This application implements geotagging and context-aware to accommodate information about school location and the impact of the disaster. The final design of the application was evaluated with a usability test on 20 respondents to assess their effectiveness, efficiency, and satisfaction. The results for effectiveness scored 98.89%, the efficiency aspect achieved an overall relative efficiency calculation of 98.5%, and the satisfaction aspect received an SUS calculation of 83 with a grade A (excellent). Additionally, the user experience aspect was tested using the UEQ calculation, yielding a score of 1.99 (very good) for attractiveness, 1.68 (above average) for insight, 1.99 (very good) for efficiency, 2.00 (very good) for dependency, 1.88 (very good) for stimulation, and 1.71 (very good) for novelty. So, the School Watching application design with geotagging and context-aware will provide a model for mitigation reporting with mobile apps.
G-Sparse: Compiler-Driven Acceleration for Generalized Sparse Computation for Graph Neural Networks on Modern GPUs

Graph Neural Network (GNN) learning over non-Euclidean graph data has recently drawn a rapid increase of interest in many domains. Generalized sparse computation is crucial for maximizing the performance of GNN learning, while most recent GNNs primarily focused on optimizing coarse-grained parallelism associated with nodes, edges, and additional feature dimensions. However, efficiently implementing generalized sparse computation is challenging. The performance optimization of generalized sparse computation lacking in-depth architecture-aware design is seldom supported by existing Domain-Specific Languages (DSLs) and is hard to be tuned by experts, which involves substantial trial and error. In this work, we propose G-Sparse, a new compiler framework that extends the popular Halide compiler to enable effective acceleration for generalized sparse computations for GNNs through compiler-driven optimizations and auto-tuning. To facilitate generalized sparse computations, G-Sparse separates algorithms from schedules and introduces several novel sparse computation optimization techniques for modern GPUs, including two-dimensional shared memory optimizations and efficient cost-driven design space exploration and auto-tuning. Extensive evaluation against highly-optimized state-of-the-art sparse computation kernels and on end-to-end GNN training and inference efficiency has demonstrated that our proposed G-Sparse achieves up to a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4.75\times$</tex> speedup over the state-of-the-art sparse kernels, and a training and inference speedup of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.37\times\sim 2.25\times$</tex> over three popular GNN frameworks including GCN, GraphSAGE, and GAT. The source code of G-Sparse is publicly available at https://github.com/TuGraph-family/tugraph-db/tree/master/learn.
Recognition of Masked Facial Expressions Based on Convolutional Neural Networks and Data Augmentation

People sometimes intentionally change their facial expressions to mask their real emotions. Currently, there is relatively little research on the recognition of masked expressions. The effective identification of masked expressions helps to identify deception and lies and has important application value. Traditional machine learning methods require the manual design of feature extraction algorithms and have poor performance in facial expression recognition. This study performs masked expression recognition based on convolutional neural networks, including VGGNet, GoogLeNet, ResNet, and MobileNet. Data augmentation and regularization are used to improve the performance of the network. The recognition rate of masked expressions has been significantly improved. Using leave-one-subject-out cross-validation, the recognition accuracy is 63.62% for required expressions (6R), 39.97% for experienced emotions evoked by video clips (6E), and 22.11% for 36 mixed expressions (6Ex6R), which is improved by 33.19%, 48.99%, and 96.52% compared to traditional methods, respectively.
Multi-Level Attention Network for Cross-Modal Hashing Retrieval

Nowadays, cross-modal hash retrieval has been widely and successfully used in multi-modal similarity search applications. However, to achieve highly discriminative representations of different modalities in a common Hamming space, it is crucial to fully explore and preserve the information provided by the training data. We proposed a multi-level attention and weighted similarity cross-modal hashing retrieval method (MAWSH). MAWSH incorporates self-attention and cross-attention mechanisms into the representation learning process to improve intra-modal and inter-modal semantic correlation. We also employ label weighting to better use label information to obtain clearer semantic connections between data when learning. Our extensive experiments on three widely used cross-modal datasets demonstrate that our MAWSH method achieves remarkable accuracy improvements for image-to-text and text-to-image retrieval tasks, outperforming five state-of-the-art approaches.
A Neighborhood-Based Speciation Brain Storm Optimization with Evolution Strategy for Multimodal Optimization

Finding multiple optimal solutions is challenging for solving multimodal optimization problems (MMOPs). In this paper, a neighborhood-based speciation brain storm optimization with evolution strategy (NS-BSO-ES) is proposed to solve MMOPs, which combines the advantages of better exploration of the neighborhood-based speciation brain storm optimization (NS-BSO) and more robust exploitation of the evolution strategy with covariance matrix adaptation (CMA-ES). In NS-BSO-ES, NS- BSO is used to generate candidate solutions to maintain the diversity of the population, CMA-ES is adopted to enhance the local search ability and locate optimal solutions accurately, and the archive is used to store inferior solutions to fully utilize the valuable information contained in these solutions as potential directions towards the optimal solution. To test the performance of NS-BSO-ES for solving MMOPs, compared with related algorithms on the 20 benchmark MMOPs in CEC-2013 Functions. Experimental results indicate NS-BSO-ES outperforms the other compared algorithms on most tested benchmark functions.