Reviewerly/exHarmony huggingface dataset:
Towards Volumetric Video Realism in Extended Reality: Challenges and Opportunities

Advances in volumetric capture, compression, and rendering techniques have enabled the possibilities of telepresence in an extended reality (XR) environment. Live or pre-recorded volumetric video of an avatar can be streamed over the network and rendered at a client's XR environment, creating an illusion of spatial co-presence. In this keynote talk, I will first present a case for the importance of visual realism of volumetric video in such scenarios. I will then present existing approaches toward higher visual realism in volumetric video, dividing them into two categories: (i) approaches to achieve smoother motion through temporal up-sampling and (ii) approaches to obtain better details through spatial up-sampling. The former aims to achieve a rendering frame rate that is close to what human brains perceive; while the latter allows users to move closer to an avatar without losing its realism. The talk will also outline the trade-offs and limitations of the current up-sampling approaches. I will conclude the talk with my personal view on the research challenges and opportunities that the research community should confront to achieve a true-to-life XR experience.
Research on Extraction Method of Surface Information Based on Multi-Feature Combination Such as Fractal Texture

Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types; 2) Undulating terrains; 3) Small fragmented land; 4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion; 2) extraction research on color features; 3) extraction research on vegetation index; 4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.
Application of Gegenbauer Wavelet Method on Initial Value Problem of Fractional Order Fitz Hugh-Nagumo Equation

This paper provides a detailed introduction to the fractional order differentials and the matrix operations of the Gegenbauer wavelet method for solving fractional order differential equations. By comparing the derived trigonometric fractional order differential form solution with the reconstructed Gegenbauer wavelet solution in exploring the initial value problem of the fractional order FitzHugh-Nagumo equation, this paper not only verifies the suitability of the defined fractional order differential form solution for numerical computations but also highlights the high accuracy of the Gegenbauer wavelet method in solving fractional order differential equations. Furthermore, by comparing the exact solution with the reconstructed solution using the Gegenbauer wavelet method, it is concluded that the synchronization rate between neurons in the fractional order FitzHugh-Nagumo model is faster than the corresponding integer-order synchronization rate.
REAL-TIME HAND GESTURE DETECTION AND RECOGNITION USING CONVOLUTIONAL ARTIFICIAL NEURAL NETWORKS

Real-time hand gesture detection is an interesting topic in pattern recognition and computer vision. In this study, we propose the use of a Convolutional Neural Network (CNN) to detect and recognize hands in real-time. Our goal is to develop a system that can accurately identify and interpret user gestures in real-time. The proposed approach involves two main stages, namely hand gesture recognition and gesture recognition. For stage detection, we use the CNN architecture to recognize hands in the video. We train the CNN model using a dataset containing various hand gestures. Once a hand is detected, we extract the relevant hand region and proceed to the gesture recognition stage. The gesture recognition stage involves training and testing CNN models for different hand signal recognition. We use a hand gesture dataset that contains a variety of common hand signals. The experimental results show that the proposed system can detect and recognize hand movements in real-time with satisfactory accuracy. Although there are still some challenges that need to be overcome, this research provides a solid foundation for further development in real-time hand gesture recognition.
KLASIFIKASI JENIS TUMOR OTAK BERDASARKAN CITRA GLIOMA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

This study uses the Support Vector Machine method, where this method aims to obtain a classification model that has high accuracy or small error in classifying an image. The development of the medical world today is closely related and cannot be separated from the development of information technology that continues to grow. To be able to distinguish Magnetic Resonance Image (MRI) images detected by brain tumors, it is necessary to carry out a classification process using the Support Vector Machine (SVM) method. So we need an application for classifying brain tumors to facilitate medical work in determining the type of brain tumor disease. Image processing will use 2 types of brain tumors, namely Glioma and Meningioma.
The “Universal” Rebus Principle and Phonosemantic Compounding

The general public views hieroglyphs as “picture-writing”. Why does this view persist after Champollion showed that the key to reading hieroglyphs is phonetic? This article argues that this misinterpretation is a historical remnant of the transmission and reception of a single text, the Hieroglyphica of Horapollo. The content of Hieroglyphica is reinterpreted as a conflation of the rebus principle with Egyptian ideas of sound symbolism. The reception of this text in the late Renaissance created the idea of a universal language as an unambiguous pictorial writing system, which has never existed for natural language. Sound symbolism, or phonosemantics is discussed with a particular eye towards the challenges of creating universal languages, including both the difficulty of representing abstract concepts, and the strain on the memory of the reader. The article discusses a solution to these problems of ambiguity in the construction of an artificial rebus principle. A prototype for a set of pictographic universal concepts mapped to characters in the International Phonetic Alphabet (IPA) is then proposed and discussed. These pictograms are based on a swadesh list that was extended by the very large lexicon PanLex, and make use of both hieroglyphs and emoji as glyphs.
APPROBATION OF OBJECTIVE METHODS FOR ASSESSING CELLULAR CHANGES IN THE PAP TEST USING A GRAPHIC PROCESSOR AND A KNOWLEDGE BASE

This paper discusses the primary results of testing objective computerized methods for assessing cellular changes obtained during the Papanicolaou test at the FEFU Laboratory Diagnostic Center. The program analyzes the morphometric data on the image and enters them into the knowledge base, which, based on certain parameters, derives a hypothesis about the cell class according to the Bethesda system.
STOCHASTIC MODEL OF VIRAL INFECTION OF A GROUP OF PEOPLE, TAKING INTO ACCOUNT THEIR SOCIAL BEHAVIOR

A model of SARS-COV-2 infection in a group with infected and susceptible individuals is proposed. A micromodel that correctly accounts for the initial stage of infection and a macromodel of the random movement of individuals in a room with internal obstacles are combined. Results of modeling the dynamics of indoor infestation under different social conditions are presentedv.
A Study on the Plan to Activate Global Startups of Domestic Startups : Derivation of Importance and Improvement Plan Using IPA Analysis

The globalization of startups enables faster innovation and growth through the connection of various resources such as technology, ideas, human resources, and funds, and plays a role in promoting cooperation between countries and expanding the market economy. This study conducted an IPA(Importance-Performance Analysis) analysis to derive measures to activate global startups of domestic startups, and based on the ERIS model, it was derived for each factor of E(Entrepreneurship), R(Resource), I(Industry), and S(Strategy).&#x0D; As a result of the study, Entrepreneurship(1st priority), Product/service differentiation(2nd priority), Investment attraction(3rd priority), Public global outbound funding support(4th priority), and Entrepreneurs’ network(5th priority) appeared.&#x0D; In all factors, the importance was significantly higher than the achievement(current level), and the difference between the importance and the achievement(current level) was large in the order of ①Public global outbound funding support, ②Global startup fund composition, ③Retention of manpower to support startups such as global marketing, ④Investment attraction level, ⑤Reorganization of laws and customs regulations.&#x0D; This means the priority improvement factor for the activation of the global startups of the domestic startups in the future, and the academic and practical implications were derived based on these research results.
A study on the classification of probation subjects using artificial intelligence(AI): Focusing on the Adult Recidivism Risk Assessment Tool(KPRAI-R)

TAs a research method, a literature study was conducted focusing on the 'Recidivism Risk Assessment Tool for Adults Subject to Probation' (KPRAI-R), which is used by the Crime Prevention Policy Bureau of the Ministry of Justice. In particular, the classification of probation subjects was divided into 'initial classification' and 'reclassification', and the limitations of each were derived, and the necessity of using artificial intelligence was divided into initial classification and reclassification and considered. Through this, it was proposed to improve the initial classification and reclassification through the establishment of an 'artificial intelligence (AI)-based automatic classification system'.&#x0D; As for the specific improvement measures, first, in relation to the initial classification, ① a more sophisticated AI-based classification system was derived by combining the information analyzed during the initial classification with the evaluation method of the recidivism risk assessment tool (KPRAI-R), ② the career and A plan to use artificial intelligence to minimize the deviation according to inclination and expertise was presented. Second, in relation to reclassification, ① prepare an artificial intelligence-based automatic reclassification system based on post-mortem information analysis after the start of probation, ② prepare an alarm system to automatically recognize the risk of recidivism and prevent recidivism by analyzing additional information collected during probation presented.
Emoticons Unveiled: A Multifaceted Analysis of Their Linguistic Impact

This study aims to thoroughly investigate the linguistic impact of emojis and emoticons in modern written communication. This work attempts to shed light on these digital symbols’ linguistic structure, social implications, and evolution through a multidisciplinary research method. A mixed-methods approach was used in the research, integrating interdisciplinary viewpoints with quantitative and qualitative analyses. A vast amount of data was gathered from various digital communication channels, such as email exchanges, messaging apps, and social media. A sizable collection of text messages with emojis and emoticons was assembled. Emotional cons and emojis were categorized using linguistic analysis, breaking down their structure into visual components and repeating patterns. Their grammatical and syntactic effects on written language were also evaluated in this investigation. The findings indicate that using emojis in writing positively correlates with emotional expressiveness. This implies that when people wish to express emotions and subtlety in their messages, they typically utilize more emoticons. They enhance digital etiquette, reduce misunderstandings, improve emotional connection, and foster cross-cultural understanding. Emoticons are a global visual language that helps people communicate, create empathy, and navigate digital communication.
Does Financial Development in Relation to Remittances influence Economic Growth in Sub-Saharan Africa?

Abstract This study investigates the influence of financial development on the relationship between remittances and economic growth within the Sub-Saharan Africa region. The study utilised panel data encompassing 42 countries, spanning the time frame 1980 to 2021. The research employed a cross-sectional augmented panel unit root test, as well as the Im-Pesaran-Shin unit-root test, to conduct the unit root analysis. The regression estimations employed the Pooled Mean Group and Mean Group/ARDL methodologies. The research findings demonstrate a significant association between the development of financial systems and sustained economic growth over an extended period. The findings from the empirical analysis indicate that there exists a substitutive relationship between financial development and the association between remittances and economic growth. The study proposes that it would be advantageous for financial policies in the Sub-Saharan Africa (SSA) region to give priority to the implementation of efficient financial reforms with the goal of improving the overall depth of the financial sector. Keywords: Remittances, Financial development, ARDL, Economic growth, Sub-Saharan Africa.
Multi-modality dense graph convolution network for skeleton-based action recognition

Skeleton-based human action recognition has emerged as a vibrant area of research in recent years. In action recognition based on skeleton, the method based on graph convolution network is through modeling space to explore physical dependence between body joints, has obtained the remarkable performance, however, most GCN method uses the stratification to aggregate a wide range of field information, which makes the joint characteristics in long distance transmission is weakened. In this paper, dense graph convolutional networks are proposed to overcome these shortcomings. To enhance the informative skeleton features and create a compact representation at an early fusion stage, an MMF is developed. Then, our proposed method utilizes a dense graph convolution operation to improve the local context information of each joint. Finally, feature channels and action classes are specific, In our model, it is crucial to assign weights to important features and focus on the relevant information. Therefore, to improve the identification of features in our model, we introduce a channel joint attention module. A large number of experiments on three large datasets (NTU-RGB+D, NTU-RGB+D120 and Kinetics) have demonstrated the performance of the proposed MMDGCN for skeleton-based action recognition.
Quick links: Apprenticeship project

Abstract This project was designed to support people to find information, resources, and evidence independently within Health Education England (now NHS England Workforce Training and Education) and allow the Knowledge Management team more time to focus on other services offered. This project aimed to pull together a number of resources in to one simple ‘Quick links’ page, with relevant information, resources and further training, providing people with opportunities to build on their own knowledge and skills.
A two-robot collaborative exploration approach for unknown environments

A two-mobile robot collaborative autonomous exploration method is proposed for map exploration and target search in unknown environments. The method first uses a boundary exploration strategy based on the RRT algorithm to achieve autonomous movement of the first mobile robot, while using the Gmapping-SLAM algorithm to complete the map construction, then the second robot subscribes to the completed map based on the ROS distributed communication method, and then sets the target location for it. The robot uses the A* algorithm in combination with the TEB algorithm to plan the path and navigate to the target location. Finally, the practicality and effectiveness of the exploration strategy is verified through simulation experiments
A text classification model for hypergraph convolutional neural networks with multi-feature fusion

To address the problem, existing graph neural network-based text classification models can only effectively learn pairwise binary relationships between words while ignoring the multivariate higher-order relationships between phrases and their inadequate representation of semantic information and local features in the text context. This paper proposes a text classification model for hypergraph attention networks with multi-feature fusion. Firstly, this paper learns multivariate higher-order relations between words by introducing a hypergraph structure instead of the original graph structure. Second, this paper constructs sequential hypergraphs, syntactic hypergraphs, and semantic hypergraphs based on textual information to enrich the textual representation of the graph neural network, thus compensating for the inadequate information representation of the graph neural network. A dual graph attention neural network is then used to learn the embedding representation of word nodes in the hypergraph and the embedding representation of relational hyperedges, respectively. At the same time, an attention-based text pooling module is used to extract discriminative and critical word nodes to help the graph neural network capture the deep local feature information of the text so that the model can better represent the text information. Finally, an adaptive fusion method fuses the three different types of text features to generate a final text feature representation for more effective text classification. The accuracy of this paper's approach on the publicly available datasets Ohsumed, R8, R56, MR, and 20NG reached 71.22%, 98.42%, 96.55%, 79.82%, and 88.32%, respectively, and the experimental results all outperformed the compared baseline models.
Multi label image classification learning with weak labels

A new multi-label similarity semantic learning(ML-SSL) model was proposed to solve the problem of label missing in existing multi-label image classification methods, It can produce better classification results by effectively recovering missing label information in training data. The model considers the characteristics of label structure and instance features, and recovers the missing label information in training data by using the label correlation within images and the similarity between images. After label recovery, the new training set is used to train the classification model, and the model is used to predict the test set. The experimental results show that the model has better performance improvement in image classification tasks under weak labeling.
The trafficability prediction for an intelligent planetary rover

The surface of the near-Earth planets, such as Mars, is covered with fine-grained regolith and other objects, such as rocks, craters, and sand dunes. In addition, the gravity of Mars is weaker than that of Earth, making rover operations on Mars more challengeable, such as higher slip, wheel sinkage, and lower tractive force, etc. In this paper, we use the AlexNet to establish a model for identifying the types of the rover rut on Mars. The model is used to predict trafficability based on the images of the rover rut which comprise the wheel-soil interaction. The types of the rut are evaluated by the level of slip ratio. According to the value of the slip ratio, the rut is classified as three types: small, middle, and large range. The dataset used in this work includes 203 samples, which was split into the training and the validation sets consisting of 143 and 60 data samples, respectively. The experimental results show that the AlexNet based on the transfer learning can accurately predicts the type of the rut. The training and validation accuracies obtained by the proposed method are 94.44% and 91.46%, respectively. The proposed technique can be used to improve the ability of environment perception, risk assessment, and trafficability estimation. This work provides the knowledge and strategy in developing future rovers for deep space planetary explorations.
Focusing on representative examples for inductive program synthesis

Inductive program synthesis is the process by which a computer automatically constructs a program that satisfies all the examples based on a given set of input and output examples. To address the problem of incomplete description of the program statute by examples, we proposes a two-stage program synthesis method and proposes an MA-FRENet model based on a multi-headed attention mechanism, which calculates an attention score based on the similarity of the example state to the state of the example executing a program that satisfies some of the examples by introducing a multi-headed attention mechanism, thus enhancing the discrimination of representative example features with complete and accurate constraint descriptions. Compared with the baseline synthesis accuracy, the experimental results show that the method is effective.
The cloud verification platform of power system simulation AI model

At present, the power system Al model has been initially applied, but the model lacks standard system verification. In order to improve the quality of the power system simulation Al model, this paper builds a set of Al model verification platform based on the performance test method of cloud computing. This platform is based on the B/S structure, and uses the powerful features of cloud computing to achieve the test and evaluation of Al models, improving efficiency and accuracy.
EVALUATING MONOCULAR DEPTH ESTIMATION METHODS

Abstract. Depth estimation from monocular images has become a prominent focus in photogrammetry and computer vision research. Monocular Depth Estimation (MDE), which involves determining depth from a single RGB image, offers numerous advantages, including applications in simultaneous localization and mapping (SLAM), scene comprehension, 3D modeling, robotics, and autonomous driving. Depth information retrieval becomes especially crucial in situations where other sources like stereo images, optical flow, or point clouds are not available. In contrast to traditional stereo or multi-view methods, MDE techniques require fewer computational resources and smaller datasets. This research work presents a comprehensive analysis and evaluation of some state-of-the-art MDE methods, considering their ability to infer depth information in terrestrial images. The evaluation includes quantitative assessments using ground truth data, including 3D analyses and inference time.