Reviewerly/exHarmony huggingface dataset:
Visual Question Answering System for Skeletal Images Based on Feature Extraction Using Faster RCNN and Kai-Bi-LSTM Techniques

The human life cycle is becoming more intelligent in the present era of technology, and it is expanding quickly in many areas, particularly in healthcare. In The HealthCare industry, we can see lots of significant transformation with the help of different cutting-edge technologies such as telemedicine, electronic medical records, drone technology, digital tools, and Artificial Intelligence. The main goals of AI are to complement medical expertise and enhance patient interaction. Most of the patients may not be aware of their health while revealing imaginary reports. Answering questions from visual reports is very challenging for doctors and patients. So the design of a VQA (Visual Question Answering) system for skeletal images based on feature extraction methods comes into play for the Healthcare realm to solve the complex problems against the visual images for users’ queries. In fact, it is a challenging task as it constrains the interaction and complementation of both image feature extraction and text feature extraction. This VQA system that can comprehend medical images may also aid in clinical decision-making, clinical education, and patient health literacy. Clinical questions are more challenging, but due to the importance of health and safety, answers must be highly accurate. Different techniques were used in this design to extract and categorize the textual and visual data. Faster R-CNN and Kai-Bi-LSTM are the design models for feature extraction that are suggested here. Deep Conversational neural networks are used in image feature extraction to find things. In order to combine past and feature context information, the BiLSTM neural network is made up of LSTM units that function in both directions. In order to forecast precise responses for given users’ inquiries for the image that they placed into the system, we combine the two methodologies mentioned above and classify.
Generalised 3D Medical Image Registration with Learned Shape Encodings

Due to the high variability in medical images, task-specific solutions have often prevailed in the field of Deep Learning Image Registration (DLIR). Contrary to classical approaches, these are hardly transferable to other tasks or datasets. To overcome these limitations we propose to exploit easy-to-acquire anatomical segmentations, which provide strong semantic features as a common representation across different datasets and image modalities. We, therefore, present a new module for medical image registration based on generalised label encodings that can be applied to arbitrary label maps. We demonstrate the generalisability of our approach by employing the module trained on skeletal and pulmonary vascular structures to the registration of abdominal segmentations, yielding an average increase in Dice scores of 24% and compare their encoding qualities to classical Euclidean Distance Maps. Furthermore, we leverage the modular design to assess the generalisability of convolutional and vision-transformer based registration architectures. Source code and trained models are released on GitHub.
Baseline Models for Action Recognition of Unscripted Casualty Care Dataset

This paper presents a comprehensive framework of datasets and algorithms for action recognition in scenarios where data is scarce, unstructured, and unscripted. The long-term objective of this work is an intelligent assistant to the medic, a surrogate buddy, that can tell the medic what needs to get done in every step of trauma resuscitation. As an essential part of this objective, we collected datasets and developed algorithms suitable for emergent contexts, such as casualty care in the field, disaster response and recovery scenarios, and other related high-risks/high-stakes scenarios where real-time decision-making is crucial. The proposed framework enables the development of new algorithms by providing a standardized set of evaluation metrics and test cases for assessing their performance. Ultimately, this research seeks to enhance the capabilities of practitioners and emergency responders by enabling them to better anticipate and recognize actions in challenging and unpredictable situations. Our dataset, referred to as Trauma Thompson, includes Tourniquet Application, Tracheostomy, Tube Thoracostomy, Needle Thoracostomy, and Interosseous Insertion procedures. The proposed algorithms based on the relative position embedding for the Vision Transformer referred as to ReVit, can achieve competitive performance with the state-of-art algorithms on our dataset.
Efficient Semantic Segmentation of Nuclei in Histopathology Images Using Segformer

Segmentation of nuclei in histopathology images with high accuracy is crucial for the diagnosis and prognosis of cancer and other diseases. Using Artificial Intelligence (AI) in the segmentation process enables pathologists to identify and study the unique properties of individual cells, which can reveal important information about the disease, its stage, and the best treatment approach. By using AI-powered automatic segmentation, this process can be significantly improved in terms of efficiency and accuracy, resulting in faster and more precise diagnoses. Ultimately, this can potentially lead to better patient outcomes, making it a vital tool for healthcare professionals. In this paper, a novel method is proposed for semantic segmentation of nuclei using Segformer-b0 and Segformer-b4 on the PanNuke dataset. To achieve the most efficient and accurate segmentation, Segformer architecture is used as it combines the advantages of transformers and convolutional neural networks. To evaluate the performance of the models, dice evaluation metric is used. The proposed method achieved state-of-the-art results on the PanNuke dataset, with Segformer-b4 achieving a mean dice score of 0.845, and Segformer-b0 achieving a mean dice score of 0.82. The findings demonstrate the high efficiency of the Segformer architecture for semantic segmentation in histopathology images, as it also highlights the importance of choosing the appropriate model architecture for the task at hand. The proposed method provides a promising approach for accurate and efficient segmentation of histopathology images. The proposed approach outperformed state of the art methods by achieving better results, as it achieved 85.45% average Dice score which is more accurate compared to the RCSAU-Net which achieved 84.82% average Dice Score.
Improving Motivation to Support Physical Activity Through Fitness App in China: A Co-design Approach

Unhealthy lifestyles and habits are negatively affecting people across the globe. The fitness and wellbeing industry has been evolving rapidly since the COVID-19 outbreak in China. While health and fitness applications (apps) serve as platforms to support people in becoming more physically active, these apps are primarily instructional and may lack personalized features that motivate users to achieve and maintain their fitness goals. Persuasive design strategies, such as social influence, personalization, and entertainment, have clearly shown benefits in promoting engagement. However, the consideration of intrinsic motivation, which drives individuals in self-improvement, is not commonly featured in fitness app design. This proposed research aims to explore the design of fitness app features from User Experience (UX) perspectives, which focuses on motivating people and maintaining a healthy exercise routine. By utilizing a co-design process, the research outcome includes a prototype fitness app that emphasizes cultivating long-term motivation for users to engage in physical activities.
Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State of the Art and Future Prospects

Introduction: Myelodysplastic syndrome (MDS) is a diverse group of hematological malignancies characterized by dysfunctional pluripotent stem cells, leading to abnormal hematopoiesis and cytopenia. Early diagnosis is crucial for improving patient outcomes. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools in healthcare for disease diagnosis and monitoring. This review aims to summarize the current state of AI application in the diagnosis of MDS, discussing various ML models, their advantages, disadvantages, and performance metrics. Methods: A comprehensive search strategy was developed using Medical Subject Headings (MeSH) terms and relevant keywords related to MDS and AI/ML. Pubmed, Embase, and scopus were searched, and duplicates were removed. Only original research articles studying ML algorithms in the diagnosis of MDS in human subjects were included. Studies on animals, reviews, non-original articles, and non-English papers were excluded. For each article, data on the area under the receiver operating curve, sensitivity, specificity, and accuracy for the best preforming model were collected. Results: The search yielded 313 articles, and after screening, 16 articles met the inclusion criteria. These articles were grouped based on the data utilized in the training of their models. Five articles utilized bone marrow smears for the training of their data. Their models aimed to detect dysplastic cells and blasts in bone marrow smears. The majority of these studies utilized convolutional neural network (CNN), a ML algorithm, for image recognition. One study utilized decision tree models. The AUC of these models ranged between 0.8 and 0.996. All the models were internally validated but were not externally validates. Three studies utilized peripheral blood smears for the training of their ML models. Two of these studies also utilized CNN for the detection of dysplasia and subsequent diagnosis of MDS. One study utilized CART to create a decision tree algorithm. The AUC for these models ranged between 0.982-0.99. Four studies utilized flowcytometry data from bone marrow samples for the training of their AI models. These models achieved AUC ranging between 0.935-0.97. Two of these models were both internally and externally validated, one of which was on a prospective cohort. Conclusion: The integration of AI and ML in MDS diagnosis holds promise for early detection and improved patient care. The reviewed studies showed encouraging results, indicating that AI-based approaches can complement traditional diagnostic methods and aid in making more informed clinical decisions especially when it comes to analysing blood/bone smears. However, many of these models need to be externally validated in large prospective cohorts to establish AI's role in routine clinical practice.
Pengaruh Implementasi Media Pembelajaran Terhadap Hasil Belajar PKn Siswa SMP Bina Jaya Banguntapan Yogyakarta

Learning media is a tool used in learning and has an important role in increasing teacher efficiency and effectiveness in teaching. This research aims to determine the effect of learning media on Civics learning outcomes. This research uses a quantitative descriptive approach. The sampling technique used was Stratified Random Sampling, while data collection was carried out through questionnaires, observation and documentation techniques. The results of the regression analysis in this study show the equation Y = 112.537+(-0.686X). This equation means the coefficient value. In addition, it is also known that the coefficient of determination R2 (RSquare) is 0.102, which means the magnitude of the influence of the independent variable (use of learning media (x) on the dependent variable (civic learning outcomes (y) is 10.2%, while the remainder is (89 .8%) is influenced by other variables outside of learning variables that do not use learning media.
STUDENTS’ PERSPECTIVE ON ROLE-PLAY AND PREVIEW /REVIEW TEACHING METHODS IN LEARNING VOCABULARY: A COMPARATIVE STUDY

This research delves into the challenges faced by students at STKIP Paracendekia NW Sumbawa in their pursuit of English language proficiency, with a specific focus on vocabulary acquisition, a fundamental aspect of language learning. Through a qualitative lens, this study delves into the effectiveness of two teaching methods: Role-Play and Preview/Review. The findings underscore the advantages of the Role-Play method in bolstering vocabulary comprehension, while simultaneously shedding light on the hurdles related to pronunciation and self-assurance. In a contrasting vein, the Preview/Review approach, leveraging students' native language and supplementary materials, emerges as a facilitator for efficient vocabulary acquisition. This research places a notable emphasis on the imperative need to address pronunciation challenges, boost students' self-confidence, and introduces a promising hybrid teaching strategy. The recommendations outlined encompass targeted interventions, the amalgamation of teaching methods, the promotion of effective learning strategies, and the dissemination of these findings as a valuable educational resource. In sum, this study significantly contributes to the realm of English language education at STKIP Paracendekia NW Sumbawa. It not only offers practical insights for educators and institutions but also strives to enhance the overall vocabulary learning experience for students, thereby adding a valuable dimension to the educational landscape.
Behaviors Modeling and Analysis for Cyber-Physical Systems

Cyber-physical systems (CPS) are complex systems, which integrate computational and physical components with the control loops. And an early modeling and quantitative analysis of CPS behaviors can reduce development difficulties and improve system reliability. However, CPS behaviors, especially the behaviors of the control loops and time behaviors, lack a systematic description and quantitative analysis. For this end, this paper proposes an approach called OODA-CPS, which integrates the modeling processes of visual modeling, formal modeling and quantitative analysis of CPS behaviors. The OODA-CPS approach is created based on the OODA (observe, orient, decide, act) system architecture, and incorporates the SysML model and the Network of Priced Timed Automata (i.e., NPTA) model. Concretely, OODA-CPS: 1) presents an explicit description on CPS behaviors by extending the SysML sequence diagram, 2) provides an automatic transformation from the CPS visual model (i.e., the extended SysML sequence diagram) to the CPS formal model (i.e., the NPTA model), and 3) provides a quantitative analysis on the OODA control loop behaviors and the time behaviors of CPS. To evaluate the approach, we present an example application, and the results demonstrate that the approach can effectively reduce modeling and analysis difficulties of the CPS behaviors.
Full-State Planning of Nonholonomic Mobile Robots via Dynamic Velocity Vector Fields

As a rigid body, the nonholonomic mobile robot contains both states of position and orientation. In order to plan these states simultaneously, this paper investigates the full-state planning problem of nonholonomic mobile robots, in the sense that the robots should reach the specified positions and meanwhile point to the desired orientations at the terminal time. To this end, we propose a velocity vector field which guides the mobile robots to the goal points. Particularly, the dynamics of the robot orientation is brought into the vector field, so that the attitude angle of the robot can converge to the specified value following the orientation dynamics. Furthermore, we study the obstacle avoidance and mutual-robot-collision avoidance by proposing another velocity vector field, which guides the robots moving along the tangential direction of the dangerous areas. Finally, several numerical simulation examples are provided to support the theoretical results.
Day-Ahead Correction of Numerical Weather Prediction Solar Irradiance Forecasts Based on Similar Day Analysis

Solar forecast based on Numerical weather prediction (NWP) is widely recognized and applied for a safer and more sufficient usage of solar energy sources at present. However, there are many negative cases of forecasts due to the unescapable inherent errors of numerical techniques. Thus, aiming to correct the error of NWP, this paper proposes a day-ahead correction model, which makes use of actual observation, for correcting NWP global horizontal irradiance (GHI) forecast. A dynamic time-window is adopted to select the similar day, whose size can be adjusted adaptively. The similar days of the corrected day constitute the training set of a generalized regression neural network (GRNN) model. This work is well demand-oriented, which can improve the day-ahead correction results of GHI forecast with 15-minute interval. The data under different weather conditions are selected to test the model performance, evaluating with root-mean-square error (RMSE) and mean absolute error (MAE). The results clearly demonstrate that the accuracy has been effectively improved by combining the actual observation and NWP data. Moreover, the proposed correction model allows more accurate forecast of photovoltaic (PV) power generation.
Distributed Filter Based on SICI Data Compression over Sensor Networks

The distributed filtering problem based on data compression is studied for linear discrete time-varying systems over sensor networks. At each sensor node, sequential inverse covariance intersection (SICI) fusion algorithm is used to compress the estimates received from neighbor nodes. A distributed recursive filter is presented based on compressed data. The filtering gain (FG) and consensus gain (CG) are solved by locally minimizing an upper bound of the filtering error covariance matrix (FECM). It avoids the calculation of cross-covariance matrices (CCMs) and reduces the computational burden. Simulation results verify the effectiveness of the algorithm.
Dynamic gesture tracking and recognition algorithm based on deep learning

Vision-based dynamic gesture tracking and recognition has always been a difficult point in the field of computer vision. It involves multiple vision-related technologies and is an intersecting topic in multiple visual directions. The most critical dynamic gesture tracking technology and modeling recognition technology are still not mature and perfect. At present, dynamic gesture tracking technology is in the stage of transitioning from traditional tracking methods to deep learning tracking frameworks. The existing deep learning tracking frameworks are not well applicable to the visual target tracking with high degree of freedom and small space ratio such as gestures. On the other hand, it is difficult to establish an effective general gesture model for recognition due to the flexibility of dynamic gestures and complex motion processes. These problems propose a dynamic gesture recognition framework based on improved real-time multi-domain convolutional neural network tracking. The real-time multi-domain convolutional neural network is introduced to realize real-time gesture tracking, and an attention module is added to calibrate the spatial importance of features, which can clarify the discriminative area of the tracking target, and greatly improve the accuracy of gesture tracking. On this basis, the characteristics of gestures are fully considered, and the motion information, appearance information and time information of the gestures are used to establish the frame difference spatiotemporal graph model of the gestures. Finally, the deep learning network is used to recognize dynamic gestures. The CGD International Public Library of Gestures has achieved better recognition results, which offers a workable solution for dynamic gesture tracking and recognition in complex scenarios.
Attitude Adaptive Robust Strong Tracking Kalman Filter Based on Unmanned Surface Vehicle Navigational Radar Target Tracking

The information such as the target’s position and velocity as detected by the radar carried on the unmanned surface vehicles (USVs) are sensitive to the state disturbance and the sensor noise generated from USVs attitude change. The Kalman filter (KF), as a traditional target tracking algorithm, still needs to be improved in terms of accuracy, effectiveness and stability due to its inability to adaptively adjust the noise covariance matrix. In this paper, an attitude-adaptive robust strong tracking Kalman filter (A-ARSTKF) algorithm is proposed, which can solve the problems of inaccurate radar measurements caused by USV attitude changes and improper filter gain estimation to improve the filtering accuracy of the algorithm. Firstly, an attitude improvement method that uses the turning angle information to correct the sensor noise covariance in real time is proposed. Then, a weakening factor is introduced into the A-ARSTKF algorithm for updating the fading factor to better regulate the one-step prediction noise covariance matrix. Finally, the adaptive factor is employed to adjust the Kalman gain and the state estimation error covariance matrix, which enhances the reliability of the A-ARSTKF algorithm. The simulation results show that the A-ARSTKF algorithm effectively eliminates the influence of the state disturbance and the sensor noise caused by the USV's own attitude change, and has strong effectiveness, stability and accuracy.
Efficient Transferability of Generative Perturbations with Salient Feature Disruption and Adversarial Transformation

Transferability of adversarial examples aims at fooling unknown models, which means that adversarial attacks can be conducted in practical scenarios. However, existing adversarial attacks usually exhibit weak transferability owing to the overfitting on the surrogate model and falling into the model-specific local optimum. To alleviate the overfitting issue, a Salient Feature Disruption Adversarial Transformation Generator (SFD-AT-Gen) based attack method is proposed in this paper, which generates highly transferable adversarial examples efficiently. Specifically, the generative model is leveraged to learn generating adversarial perturbations for disrupting the salient object-aware features that dominate the model output. Based on the framework, an adversarial transformation network is further introduced to eliminate the adversarial perturbations and require the generated adversarial examples to be resistant to the introduced transformation, which makes the adversarial examples more robust and transferable. Extensive experimental results on the CIFAR-10 dataset show higher attack success rates are achieved in both white-box and black-box settings than some advanced attacks.
A Novel Scenario-based Path Planning Method for Narrow Parking Space

Automatic parking is an important field in autonomous driving research, and path planning is a crucial step in its realization. However, traditional path planning algorithms tend to fail when confronted with narrow parking space. In this paper, a novel scenario-based path planning Method is proposed for narrow parking space. First, the scenario decision algorithm is used to determine the parking scenario category: parallel parking or perpendicular parking. Then suitable intermediate state in configuration space is selected through the backtracking method and the local path of intermediate-goal is generated. After that, the initial-intermediate path planning is performed. Finally, the global path is obtained by concatenating the two local paths. Experiments are conducted to evaluate the performance of the proposed method and the results show that it can not only quickly generate a path that satisfies vehicle kinematic constraints in narrow parking space.