Reviewerly/exHarmony huggingface dataset:
Human-Centric Resource Allocation in the Metaverse over Wireless Communications

The Metaverse will provide numerous immersive applications for human users, by consolidating technologies like extended reality (XR), video streaming, and cellular networks. Optimizing wireless communications to enable the human-centric Metaverse is important to satisfy the demands of mobile users. In this paper, we formulate the optimization of the system utility-cost ratio (UCR) for the Metaverse over wireless networks. Our human-centric utility measure for virtual reality (VR) applications of the Metaverse represents users’ perceptual assessment of the VR video quality as a function of the data rate and the video resolution and is learned from real datasets. The variables jointly optimized in our problem include the allocation of both communication and computation resources as well as VR video resolutions. The system cost in our problem comprises the energy consumption and delay and is non-convex with respect to the optimization variables. To solve the non-convex optimization, we develop a novel fractional programming technique, which contributes to optimization theory and has broad applicability beyond our paper. Our proposed algorithm for the system UCR optimization is computationally efficient and finds a stationary point to the constrained optimization. Through extensive simulations, our algorithm is demonstrated to outperform other approaches.
Distance Estimation Based on Computer Vision

Distance estimation is required more and more frequently in today’s life, not only when playing action or FPS games in the virtual environment, but also in autonomous driving in the real world. Many researchers only focus on how to improve the different methods for object detection, while few of them pay attention to distance estimation. This paper provides a method for estimating the distance between two objects or the one between the object and camera based on computer vision and regression models, which is more similar to how human beings estimate distance. To facilitate this research, the dataset is constructed using the game Monster Hunter Rise, and compares and contrasts twenty different models for distance estimating in order to find out the best one.
Deterministic Von Mises–Fisher Sampling on the Sphere Using Fibonacci Lattices

We propose a von Mises–Fisher sampling scheme using Fibonacci lattices to generate high-quality deterministic samples on the sphere. Key idea is an orthogonal inverse transform to map uniform low-discrepancy or quasi-random samples, ideally from Fibonacci lattices, to the sphere. The proposed new sampling method can be applied in assumed density Riemannian particle filters and controllers. Compared to random sampling, it produces well-separated, locally homogeneous samples, yielding superior convergence in numerical applications. The advantage over UKF-like sampling schemes is the free choice of the number of samples.
Analysis of the Burnout Behavior of Mobile Social Network Participants Based on the ISM Algorithm

The Interpretive Structural Modeling (ISM) is a system analysis algorithm for inferring the interrelationships and logical hierarchies among the factors. The burnout behavior of mobile social network users is a network phenomenon worthy of in-depth study. Through the ISM algorithm, the working mechanism of burnout behavior of the social network users can be systematically sorted out from the superficial to the deep. First, on the basis of literature review, the factors that may affect the burnout behavior of mobile social network participants are summarized, and the adjacency matrix is generated by assigning “0” and “1” through the pairwise comparison. Secondly, through the ISM algorithm, the hierarchical decomposition is performed and a directed graph is generated. The conclusion shows that the “information overload” is the direct cause of participants’ burnout behavior, while the “reduced interest” and “system quality” are the underlying reasons for participants’ burnout behavior. The ISM algorithm shows good reasoning ability for the causal mechanism of the mobile social network users’ burnout behavior.
Identification Model of Glass Relics Based on SVM Algorithm

The artistic value of glass relics in China has risen steadily, and the identification of glass relics is also playing an increasingly important role. This paper established an identification model of glass relics based on grid search (GS) optimized SVM classification, which solved the problem of determining whether the cultural relics belong to high potassium glass or lead barium glass through anonymous glass relics data. In the traditional SVM algorithm, this article added the GS optimization parameter-seeking method to improve the accuracy of SVM classification. In this paper, the model simulated and tested the selected dataset, and the model’s accuracy is 94.4%. This model provides a better identification method for the current shortage of scientific and technological identification methods for glass cultural relics identification.
Optimization of Machine Translation Algorithm for English Complex Long Sentences Based on Computer Semantic Relations

Machine translation is a way to convert the original language into another language by using some translation software in the computer. It is one of the branches of computer language and has strong research value and practical significance. Machine translation is different from human translation. Although sometimes the translation content is not accurate enough, it is efficient. In the current era of internationalization, language barriers are the biggest problem of communication, and machine translation is an important tool to solve this problem. Semantic relations, as an important theory in linguistics, are of great significance for studying language relations, word meaning substitution, and so on. Therefore, this paper would optimize machine translation algorithm based on computer and semantic relations. The final practical application result of this article was also successful. The optimized algorithm had a high translation accuracy of 97.99% for several long and difficult English sentences, while the traditional algorithm only had 93.85%.
Distributed Energy Trading Model for Multiple Microgrids Based on Blockchain Technology and the Gale-Shapley Algorithm

The distributed trading of power among multi-microgrid clusters has the potential to enhance energy mutual aid between microgrids in a region and improve local consumption of renewable energy. However, the primary objective of each microgrid entity is profit maximization, and the conventional mode of transaction based on price matching may result in serious breaches of contract by users. Therefore, this paper proposes a multi-microgrid distributed energy trading model based on blockchain technology and Gale-Shapley algorithm. First, each microgrid entity constructs user preference indicators, such as the maximum available power, user reputation, and price, to evaluate and score both parties involved in the transaction. Second, a bilateral matching mechanism based on the Gale-Shapley algorithm is proposed, which ensures stable matching results for buyers and sellers in the two-way selection of the trading market. Third, to guarantee the secure operation of the distribution network, a method for verifying the transaction matching results using a distribution network security constraint checking technique is proposed. Finally, the rationality of the transaction model is demonstrated through verification on the Matlab simulation platform.
Efficient QSDC with Identity Authentication

Although it is typically assumed that both communicating parties in quantum secure direct communication(QSDC) are trustworthy, it is essential to consider the authentication issue. An effective QSDC strategy based on identification authentication is proposed to address the issues of identity legitimacy and communication efficiency in QSDC. The proposed method authenticates and transmits messages using only four single-photon polarization states, detects eavesdropping, and authenticates the receiver's identification with a shared identity key, enabling secure message delivery while validating the receiver's identity. The approach is simple and efficient as it requires no sophisticated entanglement resources. A single-photon sequence transmitted at a time can encode the same number of message blocks of length N, i.e., every single photon contains N times its information and distinguishes them using markings. Moreover, it can be shown that the proposed QSDC with identity authentication is more resistant to typical attacks, thus more appropriate for practical implementations.
Design and Development of Artificial Intelligence Teaching Assistant System

The intelligent teaching assistant designed in this study is mainly aimed at the normal English classroom, in order to coordinate with English teachers to carry out intelligent and collaborative English teaching, and accelerate the intelligent process of English teaching. To enhance the adhibition of intelligent technology in the English classroom and web teaching platform development to provide a certain reference and help.
Track Classification for Random Finite Set Based Multi-Sensor Multi-Object Tracking

The state-of-the-art of random finite set (RFS) based approaches for multi-sensor multi-object setups solve the classification and track estimation jointly in a Bayesian style. This is computationally demanding and often requires additional modeling and parameter estimation. Additionally, these approaches are not designed to make use of direct class estimations, e.g., from machine learning detectors, but estimate the class based only on the kinematic features. This work applies a separated track classification, which uses direct class estimations, to RFS-based trackers. The proposed approach can be implemented for various RFS-based multi-sensor multi-object tracking algorithms without altering their structure and without additional modeling effort. For the update of the class estimation of a track, three different methods are presented. The three approaches are demonstrated and evaluated in combination with a labeled multi-Bernoulli filter on simulated and real-world data.
Machine Learning Based Online English Teaching Monitoring Task System

Traditional English listening teaching mainly relies on teachers personally providing oral explanations and demonstrations, and students improve their listening skills by imitating the teacher's pronunciation and speaking speed. However, this method has many limitations, such as limited time and energy for teachers, which makes it difficult to meet the personalized needs of all students. This paper designs a monitoring task system for online English teaching based on machine learning. By collecting data related to English listening learning, preprocessing and feature extraction are carried out on these data to obtain data sets for training and evaluating machine learning models. The system can generate customized listening learning tasks for students according to their individual needs and learning progress. When the system is applied to teaching, it is found that the average score of students has increased by about 8.2%. The application of the system can improve students' enthusiasm and their satisfaction. The online English teaching monitoring task system has brought substantial improvements to English listening teaching and can help students learn better.
A New CycleGAN-Based Style Transfer Method

Image style transfer is a research hotspot in computer graphics and computer vision. Especially in recent years, style transfer has been applied in many fields such as art creation, film and television production, and social networking. Therefore, in view of the problem that the image texture generated by the existing style transfer network CycleGAN is blurred, and the style transfer cannot only be performed for a specific foreground, a new CycleGAN-based style transfer method by adding a self-attention layer and semantic segmentation is proposed. The workflow of the network is to first process the image through the optimized CycleGAN to obtain the generated result, send the generated result to the semantic segmentation network to obtain the mask image, perform AND operation based on the mask image to obtain the foreground and background, and finally merge to obtain the output result. The experiments are carried out on two public image datasets, horse2zebra and MNIST. The results show that compared with the original CycleGAN network, this method has a faster texture learning speed and generates clearer textures, and moreover, it solves the problem that the original CycleGAN network cannot perform style transfer only for the foreground.
Entropy-based Selective Homomorphic Encryption for Smart Metering Systems

Smart metering systems (SMS) are popular in industrial and residential areas but can risk privacy by revealing user behaviors. Homomorphic encryption (HE) is a technique that protects data privacy by enabling calculations on encrypted data. However, the high computational costs of HE can hinder real-time or resource-constrained applications. Our paper presents a framework to encrypt only selected SMS data for improved efficiency without compromising privacy significantly. By encrypting data blocks with higher entropy values, we can mitigate the leakage of key information to adversaries who may conduct privacy attacks, such as membership inference attacks (MIA). We evaluate our framework using two real-world datasets (i.e., electricity and water) to assess privacy and performance trade-offs. The results indicate a 47% performance increase while still providing a sufficient level of privacy when adopting an encryption ratio of 0.4. They demonstrate the effectiveness of the proposed framework, considering the trade-off between privacy and performance, where the user can determine the appropriate security level for privacy protection and enhance the performance using HE in practical SMS settings.
Ballet Gesture Recognition and Evaluation System (Posé Ballet): Dynamic Improvement from Laboratory to Art Gallery

This paper elaborates the continual development of “Posé Ballet,” our ballet gesture recognition and evaluation system using MS Kinect camera. After the technical side of the system is explained, we describe our “design thinking” concepts. With these concepts, our new and improved third-version software as well as GUI are developed to serve the visitors at Bangkok Art and Culture Centre (BACC). We also mention previous versions of hardware, software, and GUI such that readers get a sense of how the system is continually developed and transformed in accordance with requirements from specific user groups. At the end, some results and feedback are collected. These give us insight on how to improve our system further toward the goal of being standardized or commercialized.
SecCT: Secure and scalable count query models on encrypted genomic data

Recently, due to the continued reduction in DNA sequencing cost, large-scale genetic samples are being gathered for accelerating predispositions to specific diseases, tailoring treatment of efficient drugs and therapies, etc. Massive genetic samples are encrypted-and-then-delegated to a public cloud to both save investment and maintenance costs and prevent the potential leakage of sensitive information. However, such a manner compromises the serviceability of a public cloud, since encryption inevitably breaks the semantic information of genetic samples. Secure count query of single-nucleotide polymorphisms (SNPs), as a kernel component for GWASs and related genomic analysis, is attracting much more attention.Existing methods lack provable security, suffer low efficiency caused by multiple interactions with the cloud, etc. In this paper, a secure virtual CT-Tree (secure vCT-Tree) is carefully constructed to confuse the tree structure by introducing a hash function and a Paillier system. Furthermore, by delegating the secure vCT-Tree to the cloud, concrete models (i.e., SecCT and SecCT+) are presented to resolve secure count query problems on-the-fly. Both models advance the provable security of genetic research and are proven to be secure under the adaptive chosen keyword (query) attack (IND-CKA2) model. Furthermore, massive experiments are evaluated on realistic data to show the superiority of SecCT.
A Method for Mining and Analyzing Time Series Patterns Related to Aerial Target

A method for mining and analyzing associated time series patterns of aerial targets is proposed to address the issues of high computational complexity and complex algorithm design under the condition of multiple elements of aerial targets. Firstly, preprocess the historical data of aerial targets and conduct preliminary screening based on a single element to reduce the data size. Then, by associating multiple elements, frequent subsequences are obtained and an associated dataset is established. Finally, mining the sequence patterns of aerial target association relationships based on rules. Through simulation experiments, this method can fully utilize the big data information of aerial target tracks and mine the activity patterns of targets in the time dimension.
Research on Vehicle Pricing Factors of Auto Insurance Based on Machine Leaning

In this paper, the claim data and vehicle configuration data of insurance companies are taken as the research object, and the vehicle pricing factors of auto insurance are screened based on GLM and four machine learning algorithms. By calculating the importance of each factor and sorting it and taking the intersection, the last 14 influence factors were obtained. According to the MSE results, the fitting effect of XGBoost model is superior. The research outcomes and model algorithm will provide an important methodological reference for the selection of auto insurance pricing elements in China, as well as a significant foundation for the personalization of auto insurance.
A Text Similarity Calculation Method with Feature Weighted Fusion

Text similarity calculation is an important component of many text mining techniques. Most of the existing studies only consider model fusion of textual features or semantic features, which are not suitable for long text processing scenarios due to the time consumption of topic extraction for words of the whole document. This article introduces related work of text similarity calculation, including TF-IDF algorithm, lexical features and LDA(Latent Dirichlet Allocation) model. Then a text similarity calculation method that combines text word features and text implicit topic features is proposed, making full use of the surface and hidden information of text to calculate text similarity. The proposed method first improves traditional word mover's distance (WMD) algorithm by introducing lexical features of text, and then calculates text similarity by linear weighting of the improved WMD algorithm and LDA model algorithm. Experimental results indicate that compared with traditional WMD algorithm and LDA model algorithm, the proposed method obtains better text similarity calculation results.
Multi-user Search on the Encrypted Network: a Lattice-based Proxy Re-encryption with Keyword Search

Proxy re-encryption with keyword search (PRES) allows users to access encrypted data through different keys on the encrypted network. The function of PRES is the combination of public key encryption with keyword search (PEKS) and proxy re-encryption (PRE). PRES can be created by directly combining the two components, but this method is not practical since the resulting scheme is no longer proven secure. Moreover, most PRES schemes use classic security assumptions that are not able to address the issues of quantum computers. In this paper, we present a lattice-based PRES scheme. It is proven to be secure under the LWE hardness assumption.
Few-Shot Learning Based on Deep Learning for Image Classification

This paper focuses on metric-based few-shot learning, in which the model adjusts its parameters according to the model performance on the meta-tasks encountered during training to obtain a robust model parameter distribution, and the final model performance may lead to overfitting problems due to the small number of samples used to define the categories. Given such problems, a parameter adaptive adjustment mechanism is introduced in the paper to determine the main source of the current model error and to adjust the gradient backpropagation strength in this way to prevent overfitting problems due to sampling errors during the meta-learning training phase. In the paper, a few-shot image classification algorithm based on cross-attention subspace metric is proposed in the framework of existing few-shot image classification algorithms, using a cross-attention mechanism to correlate information between support set sample features and a query set sample features, and then project them into space separately. Different subspaces represent different categories, and the classification accuracy of the model can be improved by measuring the distance between the samples to be measured and the subspaces while increasing the distance between different subspaces to distinguish the samples of different categories. To verify the effectiveness of the method, the proposed method is validated on Mini-ImageNet and tiered-ImageNet, the common datasets for few-shot images, and compared with the classical few-shot image classification algorithm, and the experimental results show that the classification accuracy is improved on both datasets.
Disentanglement of Speaker Identity for Accented Speech Recognition

Automatic speech recognition (ASR) systems have achieved good performance on standard English speech. However, recognition accuracy significantly degrades for non-native speakers with accented speech. Existing multi-accent ASR models utilize accent embeddings as extra input to handle diverse accents, but these embeddings often intertwine other speech attributes, impeding model performance. In this paper, we propose a speaker identity disentangled accent modeling approach, which extracts the accent embedding without any speaker-specific information, and uses it to improve the performance of the multi-accent speech recognition systems. Experiments conducted on the CommonVoice dataset demonstrate that our proposed method attains a 15% reduction in word error rate (WER) over baselines.
Wild Horse Optimizer with Deep Learning Based Leukemia Diagnosis on Microscopic Blood Smear Images

Leukemia is generally analysed by inspecting the smears of blood and bone marrow utilizing microscopes and difficult Cytochemical tests. Then these approaches are expensive, slow, and affected by knowledge and ability of the professionals concerned. Leukemia is identified with the use of image processing-related approaches to analyse microscopic smear images for detecting the occurrence of leukemic cells and such approaches are easy, quick, low-cost, and not biased by professionals. Several researchers are concentrated on machine learning (ML) based automatic classification and detection of acute leukaemia and its subtypes for promoting allow extremely accurate analysis. This paper presents a Wild Horse Optimizer with Deep Learning based Leukemia Diagnosis on Microscopic Blood Smear Images (WHODL-LDBSI) technique. The aim of the WHODL-LDBSI technique is to diagnose different kinds of leukemia on blood smear images using DL model. In the initial stage, the contrast enhancement process is carried out as a preprocessing step. Next, MobileNetv2 model was utilized for deriving features in the blood smear images and its efficacy can be improvised by WHO based hyperparameter optimizer. For leukemia classification, the deep residual network (DRN) model is applied with whale optimization algorithm (WOA) as hyperparameter tuning technique. The experimental outcome of the WHODL-LDBSI system takes place on open access dataset and the obtained values portrayed outperformed other DL models.