Reviewerly/exHarmony huggingface dataset:
NeuroSwarm: Multi-Agent Neural 3D Scene Reconstruction and Segmentation with UAV for Optimal Navigation of Quadruped Robot

Quadruped robots have the distinct ability to adapt their body and step height to navigate through cluttered environments. Nonetheless, for these robots to utilize their full potential in real-world scenarios, they require awareness of their environment and obstacle geometry. We propose a novel multi-agent robotic system that incorporates cutting-edge technologies. The proposed solution features a 3D neural reconstruction algorithm that enables navigation of a quadruped robot in both static and semi-static environments. The prior areas of the environment are also segmented according to the quadruped robots' abilities to pass them. Moreover, we have developed an adaptive neural field optimal motion planner (AN-FOMP) that considers both collision probability and obstacle height in 2D space. Our new navigation and mapping approach enables quadruped robots to adjust their height and behavior to navigate under arches and push through obstacles with smaller dimensions. The multi-agent mapping operation has proven to be highly accurate, with an obstacle reconstruction precision of 82%. Moreover, the quadruped robot can navigate with 3D obstacle information and the ANFOMP system, resulting in a 33.3% reduction in path length and a 70% reduction in navigation time. The developed project with code is available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/Iana-Zhura/NeuroSwarm.
Acceptability and Renown of Digital Immortality Through the Lens of the User

Technology users will eventually face death in the offline world. However, their data may survive for a long time, recorded in a wide variety of digital services and devices. This digital legacy must be handled and legally abide by the owners' wishes, which may vary from inheritance to deletion. By carrying out a literature review on the subject, the authors noticed there is a lack of user studies on these issues. We, therefore, conducted a focus group with technology users of different profiles to understand the volunteers' acceptability of this topic. During the group discussions, an unexpected factor was raised: to what extent might a user's fame influence their digital immortalization? This study aims to discuss digital immortality in terms of its acceptability among users and to contribute to the field of Human-Computer Interaction (HCI) with important insights on death in the digital world. In our findings regarding acceptability, users can feel some comfort in interacting with their deceased loved ones. Regarding fame, while influential individuals may not need digital immortality, there are concerns regarding the utilitarian nature of their legacies.
Fine-Grained Cross-Modal Graph Convolution for Multimodal Aspect-Oriented Sentiment Analysis

Aspect-oriented multimodal sentiment analysis aims to identify the sentiment associated with a given aspect using text and image inputs. Existing methods have focused on the interaction between aspects, text, and images, achieving significant progress through cross-modal transformers. However, they still suffer from three problems: (1) Ignoring the dependency relationships between objects within the image modality; (2) Failing to consider the role of syntactic dependency relationships within the text modality in capturing aspect-related opinion words; (3) Neglecting the inherent dependency relationships between modalities. To address these issues, we propose a fine-grained cross-modal graph convolutional network model (FCGCN). Specifically, we construct intra-modality dependency relationships using syntactic and spatial relationships and fuse the two modalities through semantic similarity calculation. We then design a GCN-Attention layer to capture richer multimodal fusion information. Additionally, an aspect-oriented transformer module is introduced to capture aspect features interactively. Experimental results on the Twitter datasets show that our FCGCN model consistently outperforms state-of-the-art methods.
Gradient Projection Differential Neural Solution for Quadratic Optimization with Quadratic Constraints: An ACP Perspective

In recent years, quadratic optimizations have become increasingly popular in engineering. However, conventional methods that investigate this problem from the perspective of a canonical form with linear constraints are not effective in dealing with the significant challenges posed by quadratic constraints in practice. This paper proposes a solution framework for the quadratic optimization with quadratic constraints (QOQC) based on innovative artificial societies, computational experiments, and parallel execution (ACP) framework. Then, a gradient projection differential neural solution (GPDNS) is proposed to address this. To illustrate the effectiveness of the GPDNS model in solving the QOQC system, numerical simulations are provided. Overall, this paper presents the potential of innovative approaches like the ACP framework to enhance our capabilities in addressing challenging optimization systems.
Research and Analysis of VR in the Field of Education

VR education is still in the stage of development after a long time of experiments and tests. Many countries have launched VR classes successively, trying to make educational resources more comprehensive and equal, but VR requires an amount of device support, at the same time, the effect of VR input into education is uncertain. VR education needs a lot of time and data to determine the effect. This paper attempts to analyze the positive effects of VR in education. For example, in art education, VR eliminates the geographical restrictions on the dissemination of traditional art and culture shadow puppetry; in medical education, high-precision VR medical model enables medical students to have a more comprehensive understanding of medical knowledge; in game education, VR creates an appropriate educational environment for students to learn more immersive. By analyzing the role and development progress of VR, people can use VR technology to explore more possibilities and practicability in education, and bring more talents to the society by utilizing the advantages of VR.
Semi-Supervised Knowledge Distillation Via Teaching Assistant

As deep neural networks are widely used with computer vision, Model Compression Methods for knowledge distillation are being actively investigated in order to deploy them into smaller devices. However, when there are significant differences between models for students and teachers, a large amount of labeling wastes a great quantity of manpower while the performance of student learning decreases. In this paper, we propose an approach that uses semi-supervised teacher assistants to fuse knowledge distillation with teachers, effectively bridging the large teacher-student gap on a large number of unlabeled datasets and a small number of labeled datasets. We enable unsupervised teacher pre-training, fine-tuning, followed by teacher-assistant offline distillation, student-teacher-assistant oline distillation, and student-teacher offline distillation. We validate the effectiveness of the proposed method for the classification task using CIFAR10, CIFAR-100, and ImageNet.
Analysis and Prospect of Recommendation System on social media

Recommendation system works surprisingly well on providing interesting and informative contents to individual user by matching suitable information with target users, and it tremendously reduces the time users spend for searching relative contents while it is also cutting back the workload of content providers. There are two popular recommendation techniques: content-based and collaborative based filtering. The abilities of each technique is compared and analyzed, and how well they match the need of users on the social media platform. In addition, a typical form of hybrid recommendation that has the advantages of multiple techniques is also taken in the comparison. From the perspective of users in social media, each specific technique achieves some degree of success for the overall user satisfaction. Integrating techniques is a valuable attempt. This paper presents a look the origin and the development of recommender system in the past. The evolution of recommendation provides insight regarding what the future of recommender will become. The architecture of recommender system is also reviewed for understanding the way system performs. The key component inside the recommender architecture including matching, filtering, and objects utilized by technique which are critical factors in determination of prediction are examined. Different techniques used in filtering are analyzed for recognizing the advantages and the disadvantages for the purpose of social media. After all, the effectiveness of the hybrid recommendation technique on dealing with limitations of popular filtering is demonstrated.
On the Issue of the Components of the Competency Model of a Teacher of a Higher Library School in the Conditions of Informatization

The special competencies of a teacher at a higher library school are differentiated into actual pedagogical (methodological) and knowledge of library technology. In recent decades, the technology of working with documents and readers has been reduced in favor of activities such as marketing and management. In recent years, theorists and practitioners of librarianship have been unanimous in the opinion that the lack of such qualifications in the nomenclature of library positions has a negative impact on the status of young specialists and their retention in the library. The purpose of the article is to theoretically substantiate the elements of the competency model of a teacher of a higher library school under informatization. The authors propose a model of teacher professional competence, which is a set of professionally significant competencies developed taking into account the integration processes of education and ensuring not only professional, but also personal growth of the teacher
Improved YoloV5 Model Target Detection Algorithm Based on Temporal Neural Networks

In order to further improve the accuracy of object detection on datasets with time characteristics such as video and live streaming, this paper proposes an object detection algorithm that combines temporal neural networks. In this neural network, Mosaic data augmentation was used at the input end, and then entered the convolution section to obtain sufficient feature maps. These feature maps were continuously convolutionally processed to obtain feature maps of different scales for feature judgment. Features of different dimensions were normalized and sent to the temporal section. In the temporal part, the forgetting gate method was used to output the hidden layer information at the current time, which is a new feature map with preliminary judgment. Separate feature maps through 1 × 1 convolution expands the number of channels to obtain anchors with classification and detection information, and filters these anchors to screen positive and negative samples. After we generate prediction boxes for the target, we often generate a large number of redundant boundary boxes. Therefore, we need to remove boundary boxes with low positional accuracy and retain boundary boxes with high positional accuracy. Obtain the final prediction result. The model proposed in this article was compared with the YOLO and SSD models on the VisDrone dataset, and the improved algorithm achieved good accuracy improvement compared to the YOLO model and higher efficiency compared to the SSD model. The method proposed in this article provides a new approach and strategy for improving the integrity, accuracy, and universality of object detection tasks with temporal data.
Three-Dimensional Visualization of Landau Level Electron Clouds Based on Monte Carlo Simulation

This study presents a comprehensive methodology for the three-dimensional visualization of electron clouds in Landau energy levels using Monte Carlo simulation techniques. Initially, this research deduces the wave function of electrons in Landau energy levels from the Schrödinger equation, solving it to obtain the eigenvalue equation for a one-dimensional harmonic oscillator containing Hermite polynomials, and then utilizes Mathematica for computational execution, resulting in the generation of intricate three-dimensional visual representations of electron clouds within these Landau levels. By integrating this approach into quantum mechanics education and research, we surpass the constraints inherent in traditional educational resources. This facilitates a visual insight, allowing students to gain a more tangible understanding of electron spatial behavior and a profound comprehension of the electron cloud's genuine physical significance. Concurrently, this study presents a comprehensive technique for rendering the three-dimensional representation of the electron cloud for generic atomic systems, employing the Monte Carlo simulation method and computational programming. This offers an innovative avenue for the advancement of visual educational tools in quantum mechanics.
Comparison of Models for Predicting Outcomes in Patients for Heart Disease

Over one quarter of all global deaths are caused by heart disease. Heart disease takes the lives of nineteen million people every year, making it the major cause of death. The risks of cardiovascular disease have been steadily rising across the globe over the past thirty years and COVID-19 has only exacerbated the predicament. Curing heart disease is a very expensive task, and thus a prediction is necessary. This study seeks to review the accuracy and accessibility of three models that are commonly used to predict decisions, which are Logistic Regression, Decision Tree, and Random Forest. Each method is used to fit models using data with 70000 cases and eleven variables. The accuracy rates for the Logistic Regression, Decision Tree, and Random Forest are 0.54, 0.56, and 0.51, respectively. Through a comparison, the Random Forest model is superior to the other models with the smallest misclassification error. Nevertheless, it does not have the accessibility that the other two regressions possess.
Distance Weighted Refining Segmentation Method for Visual Quality Improvement in V-PCC

In this paper, we propose an innovative method for refining segmentation method that improves the visual quality of Video-based Point Cloud Compression (V-PCC) encoder. Recently standardized as an international standard by MPEG, V-PCC standard provides state-of-the-art performance in compressing dynamic and dense point cloud object. However, lossy V-PCC encoder has an unavoidable problem of visual quality degradation due to lost points. When converting a 3D point cloud to 2D patches in the V-PCC encoder, some points constituting a point cloud are not converted. In particular, in the refining segmentation of the 2D patch generation process, points that are changed the projection plane due to over-smoothing can be discarded. We propose a distance weighted refining segmentation method that reduces the number of missed points to improve visual quality. Experimental results show a noticeable improvement in visual quality with minor coding gain.
GEO based Adaptive Dynamic Key Generation Scheme for Speck-R in IoT Applications

By integrating lightweight properties with lightweight cryptography in Intenet of Things (IoT) applications, information confidentiality and dependability can be guaranteed. In order to cut down on the number of rounds and speed up execution, a dynamic key-substitution layer was included to the original Speck's structure to create the ultra-lightweight encryption algorithm Speck-R.In this paper, we propose to design Golden Eagle Optimizer (GEO) based adaptive dynamic key generation scheme for Speck-R. In this algorithm, the block size and key size are adaptively chosen based on the IoT application and data size. Then the dynamic key (DK) is generated using GEO algorithm in which a fitness function is derived in terms of the metrics LPF, DPF and BIC. Then the key with optimum fitness is chosen as the dynamic key for further encryption /decryption process of Speck-R. By simulation results, we show that the proposed technique enhances the security.
Semantic Segmentation of 3D Liver Image Based on Multi-Path Features Attention Mechanism

It is challenging to precisely segment the liver from surrounding organs in medical images because of the poor contrast between them. A method of semantic segmentation of 3D liver images based on multi-path features attention mechanism is proposed to address this issue. It integrates three-dimensional spatial information and feature information from several paths in the model to automatically segment the liver area. The model in this paper uses the LiTS dataset for training, testing, and ablation experiments, and compares the results with previous models. The experimental results demonstrate that the model in this paper has reached 0.965 in the DICE similarity coefficient, and has also improved in evaluation indicators such as volume overlap error (VOE) and root mean square symmetric surface distance (RMSD). It also has better segmentation performance when tested on the CHAOS dataset. Cross-validation was carried out on the clinical MRI dataset of a hospital, and the DICE similarity coefficient reached 0.971. The results show that the model has good performance on the multi-modal datasets of CT and MRI.
Where Characteristics Effect Night-to-Day Translation Performance?

Inspired by the huge success of generative adversarial networks (GANs), GAN-based night-to-day translation methods have achieved excellent results. However, these methods have not been well visualized and understood, and thus cannot find the characteristics about their night-to-day translation performance. To this end, we present a simple clustering-based parsing approach to effectively understand the internal representations of the GAN-based night-to-day translator. In particular, we first cluster the internal representations of specific layers of the translator into a number of classes. Then, according to the proposed selection strategy, the class that has the most significant impact on the translation performance can be identified. Through experiments on three publicly available datasets, we find the answer of the question (title) is that the characteristics at the junction of bright and dark regions affect the performance of night-to-day translation.
Design of Haptic Experience Recording for Guide-dog Training

In recent years, the need for guide dogs has increased, and a more efficient methodology for training guide dog trainers is accordingly required. One challenge is that haptic information, which is a significant part of guide-dog training, is difficult to explain and visualize. Virtual reality (VR) systems have attracted attention owing to their ability to support high-level immersive interactions with the presence of multisensory experiences. A haptic-enabled VR system could address the limitations of the conventional method and support novice trainers in practicing in an immersive and remote transmission manner. This study presents a handle-based sensing setup for recording haptic experiences of trainers during guide dog training for use in a VR system. Considering trainers perceive and apply forces via handle to obtain and control dog motion status, the applied forces are decoded as haptic information and recorded using corresponding sensors. While accuracy of proposed setup for collecting required data is checked to be high(average errors of validating forces and yaw angles are 0.4N and 0.93° respectively), we believe that the proposed recording system is able to measure haptic experiences for further haptic experience re-establishments in proposed VR guide-dog training system and could finally facilitate the education of a large number of dog trainers.
Formal Training of University Students with the Application of the Arduino Platform for Engineering Problem Solving

The aim of the work is to consider the formal training of university students using the Arduino platform for engineering problem solving. The other purpose of the work is the research is to use interactive technologies and to improve the practical experience of students in the process of educational internship at the end of the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> year of study of a higher educational institution, to deepen their scientific and practical skills through the use of the Arduino platforms. The article considers the issue of improvement of students’ practical experience in the course of educational internship using the Arduino microcontroller for engineering problems solving. The proposed method to conduct the educational internship is an element of interactive learning techniques that will allow students to deepen their knowledge, acquire and consolidate their practical experience, and to turn the student into an active participant in the learning process, showing their initiative and revealing creative and intellectual potential. The computerized soil irrigation system developed by students during their internship will allow to analyze the quality of plants growth and prevent their disease. The article compares the operation of a YL69 sensor and a capacitive humidity sensor. The created automated system can be expanded both systematically (for any agrarian enterprises) and functionally (to increase the number of sensors for the development by students in the process of internship), as well as to use this technology for other specialized tasks. Development of any automated systems based on microcontrollers is of a huge potential for IT professionals. All this will provide a great deal of practical experience for future IT professionals who will do real practical work, as well as improve the quality and yield of cultivating different crops which will bring significant profit.
Using Bearing-Only Measurements of an Underwater Observer Robot for Passive Tracking of a Highly Maneuvering Target Using Interactive Multiple-Model Cubature Kalman Filter

Tracking of underwater moving targets by underwater robots requires the continuous use of passive observation of the target angle over time. In this case, the improvement of target position estimation depends on the movement of the submarine robot to increase the field of view. In order to protect the observer bot from being seen by the other party, passive BOT measurements are used in this paper. Since the motion of a target may have several models, one of the best choices is the interactive multiple model (IMM) filter. On the other hand, due to the non-linearity of the measurement equations and the target motion equations, the Cubature Kalman filter (CKF) can be used to improve the tracking accuracy. By combining these filters, in this paper, the IMM- CKF filter is used to track a highly maneuverable target in a situation where there is only one measurement from the observing robot at any time.