Reviewerly/exHarmony huggingface dataset:
Exploring the data of blockchain-based metaverses

In recent years the concept of metaverse has evolved in the attempt of defining richer immersive and interactive environments supporting various types of virtual experiences and interactions among users. This has led to the emergence of various different metaverse platforms that utilize blockchain technology and non-fungible tokens (NFTs) to establish ownership of metaverse elements and attach features and information to it. This article will delve into the heterogeneity of the data involved in these metaverse platforms, as well as highlight some dynamics and features of them. Moreover, the paper introduces a metaverse analysis tool developed by the authors, which leverages machine learning techniques to collect and analyze daily data, including blockchain transactions, platform-specific metadata, and social media trends. Experimental results are reported are presented with a use-case scenario focused on the trading of digital parcels, commonly referred to as metaverse real estate.
Effects on Selective Removal of Adversarial Examples for Noisy X-ray Images

Preprocessing the input images of the deep neural networks (DNN) is a way to prevent adversarial attacks without compromising the classification performance of the targeted model. Especially, considering the perturbation as noise, then applying denoising as the preprocessing method is known to be effective in removing perturbations caused by adversarial attacks. However, perturbation removal can result in excessive image smoothing and finer details of the image can be lost in the denoising process. Here, we experimentally confirm the effectiveness of selectively removing perturbations to maintain image quality after perturbation removal and propose an image reconstructing model based on Res-Unet.
Automatic Shuttlecock Motion Recognition Using Deep Learning

In the field of sports video processing, specifically in the context of motion recognition for shuttlecock match videos, we first propose a method based on attitude estimation to measure the movement extent of the shotting arm, allowing for temporal positioning of shuttlecock shot motions and extraction of corresponding shot-sequences. The shot-sequences is defined in this paper as video segments that exclusively contain the complete shot motion of the mainplayer. During the training phase, we incorporate a lightweight channel-spatial attention mechanism into the Temporal Segment Network (TSN) to classify the extracted shot-sequences into four types, i.e., forehand shot, backhand shot, smash shot, and drop shot. Furthermore, we employ image morphology-based techniques to further distinguish smash shot-sequences as either high clear shots or kill shots based on the shuttlecock’s motion trajectory. The extensive experiment results demonstrate the effectiveness of proposed method in accurately positioning and recognizing shuttlecock shot motions.
Numerical Evaluation of Algebraic Cryptography

Introduction: A secret sharing scheme is a mechanism to divide up shares of a secret among numerous participants such that authorised groups of participants may piece it together but banned groups remain in the dark. Aim of the study: the main aim of the study is Algebraic Cryptography: Its Significance and Issues. Material and method: In this study, only black and white covert pictures are discussed. Although there exist visual cryptographic paradigms that deal with colour pictures and grayscale images, such circumstances are not considered in this study. Conclusion: We have proposed a construction and analysis of a t-(k, n) -VCS for monochrome pictures, where t participants are crucial in a (k, n)-VCS, based on a linear algebraic method.
Model Predictive Control for Uncalibrated Visual Servoing Based on Broyden Estimation

The development of machine vision and related technologies in recent years has led to a widespread use of visual servoing in robotics. However, obtaining accurate parameters of camera and robot often necessitates a complex calibration process, making the determination of the projection relationship between image changes and robot joint movements a laborious undertaking. In order to solve the problem, Broyden estimation is applied in this paper to estimate the combined Jacobian matrix online, and the estimation results are then introduced into a model predictive controller to solve the dynamic visual tracking problem, while the constraints of joint angles and velocities are considered. The simulation results verify the effectiveness of the proposed algorithm.
A Method for Partitioning the Topological Structure of Groups Based on Hypergraphs

In this paper, we propose a method for partitioning the topological structure of a group based on hypergraphs. Compared to traditional graph-based methods that reflect the topological structure of a group, our method is based on hypergraphs, which allows for the consideration of both spatial and temporal dimensions of the structure and captures the changes and interactions of the group in spatiotemporal space. We design a relatively simple algorithm that can quickly partition the hypergraph structure based on the provided pedestrian coordinates. We conducted a series of experiments to validate the necessity and effectiveness of hypergraphs in representing the topological structure of a group, as well as to demonstrate that our method can successfully partition the topological structure of a group in complex scenarios. This lays the foundation for further research on complex collective motion.
Enhanced CGSN System for Machine Reading Comprehension

This paper introduces the system proposed by the "Guess Right or Not (Ours)" team for NLPCC 2023 Shared Task 2 ( https://github.com/Yottaxx/NLPCC23_SciMRC )--Multi-perspective Scientific Machine Reading Comprehension. This task requires participants to develop a reading comprehension model based on state-of-the-art Natural Language Processing (NLP) and deep learning techniques to extract word sequences or sentences from the given scientific texts as answers to relevant questions. In response to this task, we use a fine-grained contextual encoder to highlight key contextual information in scientific texts that is highly relevant to the question. Besides, based on existing advanced model CGSN [7], we utilize a local graph network and a global graph network to capture global structural information in scientific texts, as well as the evidence memory network to further alleviate the redundancy issues by saving the selected result in the previous steps. Experiments show that our proposed model performs well on datasets released by NLPCC 2023, and our approach ranks 1st for SMRC Task 2 according to the official results.
The Mental Health and Life Education Curriculum Development Based on Kolb's Experiential Learning Theory

This research aimed to 1) study the factors influencing the development of subjective well-being, 2) develop a mental health and life education curriculum based on Kolb’s experiential learning theory, 3) compare students' subjective well-being before and after the implementation of a mental health and life education curriculum based on the Kolb’s experiential learning theory. The sample group was thirty undergraduate students from Guangxi Vocational Normal University in the first year of Artificial Intelligence Class 1 (Vocational Normal Teacher). The research instruments were 1) a questionnaire on factors influencing the development of subjective well-being, 2) an interview form on factors influencing the development of subjective well-being, 3) lesson plans, 4) a subjective well-being scale, 5) a teaching opinion interview form, and 6) an observational record of student behavior. The research results indicated that 1) three factors affect students' subjective well-being: life satisfaction, positive affect, and negative affect. 2) The mental health and life education curriculum consisted of six components: (1) concept, (2) contents and time, (3) objective, (4) learning processes according to Kolb's experiential learning theory, (5) learning resources and (6) evaluation, and 3) students’ subjective well-being improved after the implementation of a mental health and life education curriculum based on the Kolb’s experiential learning theory. According to the results of the study, it is suggested that universities should implement management and teaching based on students' emotional and affective experiences and add the content of life education to universities' ideological and political theory education curriculum and students' professional curriculums. Students should put the knowledge and skills they have learned in life education into practice and maintain an optimistic attitude.
Pseudo dense counterfactual augmentation for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained text classification task, and the cutting-edge ABSA models have achieved outstanding performance. Unfortunately, the robustness of these ABSA models is neglected. ABSA models must face numerous challenges to be robust, and we concentrate on one of these challenges caused by negation words, such as “not”, “un-”. In the actual context, these negation words intuitively result in two problems: negative sensitivity and spurious correlation. First, a negation word tends to reverse the sentiment polarity of a sentence. Meanwhile, in the ABSA datasets, most sentences containing negation words express Negative polarities, which will lead the predictive model to learn the spurious correlation between negation words and polarities. To resolve these ambiguous issues, we are inspired by causal inference and propose a novel data augmentation framework, namely Pseudo Dense Counterfactual Augmentation (PDCaug) for ABSA. Specifically, we initialize a pseudo sequence and employ a multi-head multi-layer attention network to achieve counterfactual augmentation for a vanilla sentence in the hidden space. This pseudo sequence will be adversarially trained. PDCaug is a plug-and-play method for various ABSA models, so we evaluate it on discriminative models and generative prompt-based models. Our extensive experiments show that our PDCaug can significantly and consistently outperform several data augmentation methods and ABSA models.
A Spider Monkey Optimization Algorithm Combining Opposition-Based Learning and Orthogonal Experimental Design

As a new bionic algorithm, Spider Monkey Optimization (SMO) has been widely used in various complex optimization problems in recent years. However, the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant. Thus, this paper focuses on how to reconstruct SMO to improve its performance, and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design (SMO<sup>3</sup>) is developed. A position updating method based on the historical optimal domain and particle swarm for Local Leader Phase (LLP) and Global Leader Phase (GLP) is presented to improve the diversity of the population of SMO. Moreover, an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values. Also, a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time. Furthermore, an extended SMO<sup>3</sup> named CSMO<sup>3</sup> is investigated to deal with constrained optimization problems. The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems. Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems.
Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-Based Tracking

Road congestion, air pollution, and accident rates have all increased as a result of rising traffic density and worldwide population growth. Over the past ten years, the total number of automobiles has increased significantly over the world. In this paper, a novel method for intelligent traffic surveillance is presented. The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes. To improve the results, mean-shift clustering was applied to the segmented images. Afterward, the pixels given the label for the vehicle were extracted and blob detection was applied to mark each vehicle. For the validation of each detection, a vehicle verification method based on the structural similarity index is proposed. The tracking of vehicles across the image frames is done using the Identifier (ID) assignment technique and particle filter. Also, vehicle counting in each frame along with trajectory estimation was done for each object. Our proposed system demonstrated a remarkable vehicle detection rate of 0.83 over Vehicle Aerial Imaging from Drone (VAID), 0.86 over AU-AIR, and 0.75 over the Unmanned Aerial Vehicle Benchmark Object Detection and Tracking (UAVDT) dataset during the experimental evaluation. The proposed system can be used for several purposes, such as vehicle identification in traffic, traffic density estimation at intersections, and traffic congestion sensing on a road.
Targeted Module Design In Low-light Image Enhancement

Capturing images in low-light conditions often leads to poor visual effects and failures of vision systems. Low-light images typically suffer from poor visibility and a lack of detailed features. MIMO-UNet is originally proposed for image deblurring and researchers have developed different modules to improve the performance of it.We think the structure of MIMO-UNet also has potential in low-light image enhancement. We proposed three modules to achieve this: FSFU, SAOM, and an aiming loss function. The FSFU module uses information from both the space domain and the frequency domain to implement up-sampling. The SAOM module introduces an attention mechanism to improve the multi-scale feature fusion. The aiming loss function is more suitable for this task than traditional loss functions. Extensive experiments demonstrate the effectiveness of our proposed modules and we offer a way of thinking that we can design modules targeting one specific task.
Improved Global Context Graph Neural Networks for Session-based Recommendation

Session-based recommendation is designed to make recommendations for anonymous or non-logged users based on insession data. The existing research work usually only models a single item in a conversation as the smallest unit, ignoring the representation of the item. This research aimed to optimize the use of context information across the global spectrum by analyzing the interaction patterns of items within a session, along with any potential correlation between the various conversational adjacencies. The proposed model would learn from both the session graph and the global graph, and then combine this data to obtain an embedded representation of the items. The attention mechanism would be applied to attain the embedded representation of the session in order to predict future interaction behaviors. To compare with current session-based recommendation algorithms, the model in this paper is recommended to perform better than the previous model.
An Improved YOLOX Loss Function Applied to Maritime Video Surveillance

With the development of artificial intelligence technology, ship target detection based on maritime video surveillance has been well developed. However, there are still problems of inaccurate detection and wrong classification. In this work, we consider improving the accuracy of existing ship target detection methods. We propose a loss function called Theta-EIOU Loss, which improves the learning and representation capabilities of our network by reconstructing the bounding box regression loss function, improving the background partition function, and refining the sample partition function. We have done extensive experiments on public ship datasets, and the experimental results of different ship detection networks show that our method outperforms the original YOLOX network.
Facial Expression Recognition Based on An Improved Data Augmentation Method and A Multichannel Convolutional Neural Network Model

With the development of artificial intelligence and human-computer interaction, the recognition and analysis of facial expressions have recently received increasing attention. Considering the excellence of the convolutional neural network (CNN) model in the field of image recognition, this paper applies the CNN model to recognize facial expressions. The deep neural network is inseparable from large-scale data. In the real world, it is expensive to obtain large scale data sets with labels. A reliable method of expanding the number and diversity of the samples must be found. The generative adversarial networks (GAN) model can generate more images and solve the imbalance of data volume. However, the experiments indicated that the image sharpness generated by the traditional GAN model was insufficient. The model was challenging to train stably and prone to over-fitting or under-fitting. Given these situations, this paper optimized the structure of the GAN model by combining the advantages of the DCGAN and StarGAN models, and improved the training strategy. Moreover, a new multichannel CNN model is proposed and applied in this paper to obtain more comprehensive facial expression features. The experimental results reveal that the proposed method significantly improves the performance of the evaluated baseline facial expression recognition methods.
Improving Particle Filters with Adaptive Bayesian Resampling for Real-Time Filtering

Particle filters (PFs) are a set of simulation-based methods, which recursively estimate the posterior densities by a set of weighted samples. Due to their sample-based representation, PFs are well suited to estimate the state of non-linear dynamic systems. The increased representational power of PFs, however, comes at the cost of higher computational complexity. Thus, it has been challenging to apply PFs in real-time applications such as target tracking. In this paper, we propose the design of PFs with two novel Bayesian resampling methods which are well suited for parallel execution. The resampling algorithms are further improved for speed consideration to allow for real-time filtering. We then propose the design of PFs with adaptive resampling performed during the filtering, in order to increase the estimation accuracy as well as the speed. The proposed method is evaluated with a well-known tracking problem. Experimental results confirm that PFs with the proposed resampling algorithms achieve similar localization accuracy compared to the traditional resampling methods, while improving the speed considerably. PFs with the adaptive resampling by the computation of effective sample size (ESS) can further improve the accuracy, up to 12% in comparison with traditional filtering.
Adaptive Feature Selection on Graph

Graphs have become a widely-used tool to model data with relationships in real life for a long time. To discover the important contents in the graph, many graph neural networks (GNNs) have been come up with. Nevertheless, these models tend to adopt ReLU as their activation function for its nonlinearity, effectiveness, and efficiency. Owing to its own deficiency, it would cause many generated feature elements to be zero, which would miss part significant features. To overcome this problem, an adaptive weight vector to tune the features was provided. By limiting the elements of the weight vector, it can be a better substitute for ReLU. Besides, such a weight vector adaptively measures the importance of each feature element to work as a feature selection operator. To show the function of the weight vector, we examine a GCN with the weight vecotor in node classification, and it exhibits overall improvements over three well-known citation networks.
Fabric Electrodes for Physiological Sensing in a VR HMD

This paper explores the development and testing of fabric electrodes to collect a range of physiological measures. The aim is to integrate these sensors into a Virtual Reality (VR) headset to collect physiological and muscular motion data that will help detect emotion, cognitive load and facial expressions. As part of an on-going project, we have already developed prototypes of the EMG and GSR sensors. A head phantom has been developed for the purpose of testing and validating electrode performance.
Language acquisition of children aged 2 years in "Kimono mom" YouTube channel

This research discusses the language acquisition of 2-year-old children in terms of phonology and syntax. The purpose of this study is to describe the language acquisition in terms of phonology and syntax of 2-year-old children in Kimono Mom YouTube channel episode 1 to 4. The data source of this research is "Kimono Mom" YouTube channel, episode 1 to episode 4 and the data in the study are utterances expressed by a Japanese child named Sutan who lives with his father and mother in Tokyo, Japan. In his daily life Sutan often communicates with his parents, especially with his mother named Moe. This research is descriptive qualitative research with the method of note taking method and library research. The theory used is the theory of language acquisition in terms of phonology and syntax. The results show that in terms of phonology Sutan can pronounce all vowel sounds except the vowel sound [ɯ]. Sutan can pronounce all consonant sounds perfectly except consonant sounds [ŋ], [ʦ] and [ʣ], as well as consonant sound [ʤ] located at the beginning of the word. In addition, Sutan can pronounce all the special phoneme sounds of Japanese except the double vowel sound (chou boin). In terms of syntax, Sutan can pronounce declarative, imperative, interrogative, and exclamative sentences according to the context even though they are in the form of short sentences and still rarely use particles and the sentence only reaches the verb.
Features and Application of Stochastic Analytical Modeling

The chapter deals with the main features of stochastic modeling and its application in the investigation of probabilistic processes in computer processing. At the beginning, a brief overview of the possibilities offered by the probabilistic analytical modeling was made with the definition of basic characteristics and short comments. The relationship of discrete and continuous random variables to stochastic modeling is discussed in the first part, focusing on Markov processes with application examples presented. The second part is devoted to the stochastic analytical approximation. The third part is directed to the discussion of the stochastic modeling by means of the theory of Markov processes, concentrating on random processes with discrete states and discrete time (Markov Chain) and on analogous processes, but with continuous time. Each of these two sub-parts is illustrated with numerous examples of sample computer processing situations with analysis of the results and appropriate tabular and graphical illustration of the resulting estimates.