Reviewerly/exHarmony huggingface dataset:
METAVERSE AND HOW APPS ARE DEVELOPED IN METAVERSE

The notion of the metaverse has gained major interest in recent years, describing a virtual domain where users can interact with each other and digital content in a seamless and immersive manner. The metaverse is a virtual universe that blends Augmented Reality (AR), Virtual Reality (VR), and other immersive technologies to create a shared space where users can engage in various activities, socialize, and consume digital material. In the metaverse, apps play a vital role in creating the user experience and allowing interactions with the virtual environment. App building in the metaverse means designing applications and experiences that easily interface with the metaverse platform. These apps can range from entertainment and gaming experiences to productivity tools, instructional materials, and beyond. Developers need to address the unique qualities of the metaverse, such as its spatial and immersive nature, as they construct and develop such applications.
Lightweight and Efficient Privacy-Preserving Multimodal Representation Inference via Fully Homomorphic Encryption

Machine learning models are now being widely deployed in clouds, but serious data leakage problems are also exposed when dealing with sensitive data. Homomorphic encryption (HE) has been used in the secure inference on unimodal private data because of its ability to calculate encrypted data. Although the privacy protection of multimodal data is of great significance, there is still no privacy-preserving inference scheme for multimodal data. In this work, we propose a lightweight and efficient homomorphic-encryption based framework that enables privacy-preserving multimodal representation inference. Firstly, we propose an HE scheme based on Tensor Fusion Network, which can perform encrypted multimodal feature fusion. Then we propose a pre-expansion method and a packaging method for multimodal data, which can effectively reduce the time delay and data traffic of homomorphic computing. The experimental results show that our encryption inference method has almost no loss of accuracy and obtains an F1 score of 0.7697, while using less than 220KB of data throughput and about 0.91 s of evaluation time.
An Unsupervised Deep Learning Framework for Anomaly Detection

In recent years, with the evolution of technology and hardware, people can per-form anomaly detection on machines by collecting immediate time series data, thereby realizing the vision of an unmanned chemical factory. However, the data is often collected from multiple sensors, and multivariate time series anomaly detection is a difficult and complex problem because of the different scales and the unclear interaction of each feature. In addition, there usually exist noises in the data, and those make it difficult to predict the trend of the data. Moreover, practically, it’s hard to collect abnormal data, thus the imbalance is an important issue. Recently, with the rapid development of data science, unsupervised methods based on deep learning manner have gradually dominated the field of multivariate time series anomaly detection. In this paper, we propose a 3D-causal Temporal Convolutional Network based framework, namely TCN3DPredictor, to detect anomaly signals from sensors data. Our proposed TCN3DPredictor modifies multi-scale convolutional recurrent encoder-decoder by 3D-causal Temporal Convolutional Network which can learn the interaction and temporal correlation between features and even predict the next data. Based on the results of 3D-causal Temporal Convolutional Network, a new breed of statistical method is proposed in our proposed TCN3DPredictor to measure the anomaly score precisely. Through a series of experiments using dataset crawled from a computer numerical control (CNC) metal cutting machine tool in a precision machinery factory, we have validated the proposed TCN3DPredictor and shown that it has excellent effectiveness compared with state-of-the-art anomaly prediction methods under various conditions.
Finite Libby-Novick Beta Mixture Model: An MML-Based Approach

We propose an unsupervised algorithm for learning the optimal number of clusters in a finite Libby-Novick Beta mixture model. In unsupervised learning, it is crucial to determine the number of clusters that best describes the data. By extending the minimum message length (MML) principle, we are able to determine the number of clusters in Libby-Novick Beta mixtures. Our model has been evaluated on three publicly available and real-world medical datasets.
Tree-Based Unified Temporal Erasable-Itemset Mining

Erasable itemset mining is an important research area for manufacturers, as it aids in identifying less profitable materials in product datasets to facilitate better decision-making for managers. It allows for improved trade-offs between manufacturing and procuring activities. Traditional erasable itemset mining does not account for the time factor, which is critical for time-sensitive industries, with product time periods significantly impacting a company’s profitability. Therefore, we previously proposed an Apriori-based unified temporal erasable itemset mining approach, which could consider different user scenarios to solve this issue. In this work, we design a tree-based algorithm to raise the mining efficiency. It applies a lower-bound strategy to reduce the candidate erasable itemsets and the number of database scanning. According to the experimental results, our proposed algorithm has better performance on execution time and memory usage than the previous work.
A Novel Meta-heuristic Search Based on Mutual Information for Filter-Based Feature Selection

Recent research has shifted its focus towards feature selection to improve model performance and reduce training costs, as high dimensional and noisy datasets have become ubiquitous. Filter-based feature selection, which uses external criteria to filter features based on relevance scores, has become increasingly popular in this research area. However, existing studies that use a greedy search strategy to find optimal feature subsets suffer from suboptimal solutions, as the selected features cannot be changed or removed later. In this paper, we propose a novel method for filter feature selection that combines a local search strategy with meta-heuristic techniques to improve the exploitation of the most promising candidates for the global search. We aim to use evolutionary computing with mutual information-based criteria instead of relying solely on classification performance like existing state-of-the-art methods. We conducted empirical experiments on various benchmark datasets that differ in volume and dimensionality to evaluate the performance of our proposed approach. Our findings demonstrate that our method outperforms other state-of-the-art work by requiring less time to reach the optimal solution.
Robot System Modeling

Robot system modeling is the foundation for verifying a robot’s motion control and simulation. The 3D robot model in ROS is implemented through a Unified Robot Description Format (URDF) file, which is an XML file describing the robot as well as its parts structure, joints, and degrees of freedom (DOF). The 3D robot in the ROS system has a corresponding URDF file. This chapter covers several common-wheeled robot motion models and robot modeling with URDF. The robot motion control is performed through ROS to achieve synchronized motion between the simulated and real robots. Then, the typical sensor in robot systems, light detection and ranging (LiDAR), is discussed. Finally, readers can observe the scene profile point cloud data to experience environment sensing and obstacle detection from the robot’s perspective.
Mechanism for designing the content of the professional competence of teachers in higher education institutions

In the article, the article in the article on the development of modern approaches, and leading and leading factors in the context of the development of vocational competitors of higher education institutions, the professional competence of heads and internal training, and the professional competence of the degree and agrological approaches The issues of improving the organizational and governance mechanisms of the development are covered and the development of the professional competence of the professional competence, and the practical introduction of didactic mechanisms based on innovative and interactive technologies.
Mechanism and Influencing Factors of Motion Sickness Induced by Virtual Reality Devices

Objective Virtual reality (Virtual Reality, VR) technology is widely used in the fields of simulated driving, but the impact of motion sickness (Motion Sickness, MS) on users can not be ignored. Method By analyzing the mechanism and influencing factors leading to MS of virtual reality, it can better understand the influence characteristics of different imaging technologies on simulated flight. Result VR hardware, content and personal user characteristics all have an impact on MS. Conclusion This study is helpful to provide a reference for the design and development of mixed reality (Mixed Reality,) imaging technology, and improve the usability of MR products in the future.
Analog modeling of education for sustainable development in a unified educational space

The paper considers the phenomenon of educational space unification as a reflection of globalization in education. The role of education for sustainable development as a global vector of world educational process in unification of educational space was substantiated. Analysis of peculiarities of education for sustainable development different from academic subjects revealed its cross-disciplinary and cross-cut curricular features. Problems of implementing cross-disciplinary and cross-curricular education in the conditions of subject matter education are stated. An assumption is made about the possibility of using the functional system model to overcome such difficulties. Functional system is a system of control in living organisms, developed in the process of evolution, which combines variability and integrity, direct and feedback, flexible and rigid links of regulation, and provides an orientation of all components of the system to achieve a common result. Stages of "work" of functional system are presented as stages of education for sustainable development embedding into unified educational process. The advantages of educational process and monitoring its results on the basis of functional system are justified. Forecast of perspectives of artificial intelligence application to design education for sustainable development as a functional system is made.
Research on Multi-UAVs Exploration Method for Unknown Environment

With the development of intelligent technology, the research of robots has expanded from the industrial field to military applications such as aerospace, and some environments are not suitable for human participation, such as mine exploration, cave rescue, which requires robots to complete various tasks autonomously in these unknown environments. The use of UAV instead of humans to explore unknown environments can exempt people from the threat of unknown environments, and is a hot research field in the future of civil military UAV technology. Efficient environment exploration is the premise and key for UAV to establish environment models and complete various complex tasks, and the collaborative execution of multi-UAVs has obvious advantages over a single UAV, but the formulation and selection of multi-UAVs environment exploration strategies will be more difficult. Based on the application background of multi-UAVs collaborative exploration of unknown environment, this paper studies the exploration method of multi-UAVs in unknown environment, and adopts ant colony algorithm to solve the problem of target allocation and area coverage of multi-UAVs.
An Automatic Method for Generating Symbol Sequence with Markov Chain Coding for Test of Cognitive Information Processing Abilities

The test of cognitive information processing abilities (TCIPA) plays a significant role in psychology, education, military science, professional training and so on. The available system of TCIPA are based on stimulus-response model and human-machine interaction, in which symbol sequences are utilized for the design of graphical user interface (GUI). However, the symbol sequence is fixed and designed manually. In this paper, we propose an automatic method for generating symbol sequence with Markov chain coding for TCIPA. The core idea lies in generating symbol sequence with Markov chain, goodness-of-fit test, symbol coding and symbol atlas formatting. The method is full of inspiring significance and can be applied in the design of GUI for HMI system.
Adding competence of students of higher educational institutions on the basis of creative approach to the development of technology

This article deals with the development of creative competence of future teachers on the basis of acmeological approach, the qualities of creativity of future teachers, approaches based on the development of creative competence of future teachers, pedagogical conditions for the development of creative competence and the possibility of organizing creative activities. The skill groups that are given are shown. An analysis of the results of experimental work is also presented.
Formation of social competences of elementary school students in extracurricular activities

This article reveals the essence of social competence, its definitions, pedagogical aspects of the formation of social competences of younger students in extracurricular activities. When a student comes to school, he is faced with the task of mastering the school curriculum, as well as personal development. The outcome of personal development is the willingness and ability of students to develop themselves and to determine their personal self; leads to the formation of axiological relations that reflect their motivation for education and targeted cognitive activity, a system of important social and interpersonal relations, personal and civil positions in the activity.
Improving the independent creative activity of students on the basis of competent approach

The article is aimed at defining the stages of organization of independent creative activity of students through observation and analysis of the educational process and the organizational and pedagogical conditions for independent learning to improve the quality of the educational process. independent work instructions,development of teaching and didactic support and methods of organizing independent creative activity, as well as criteria and indicators for determining the level of improvement of independent creative activity of students on the basis of a competency approach.
Systematic use of mathematical concepts in professional training of students

The article provides the mathematical concepts in the process of vocational guidance of students in higher education, the specifics of teaching higher mathematics based on the characteristics of the educational direction. Today, the higher education system is characterized by the development of intellectual potential, independent thinking and decision-making, and the creation of new generations of science programs is important to achieve this goal.
Real-Time Detection of Natural Scene Assamese Texts Using Deep Learning

Text detection and identification are crucial elements in comprehending semantics in a natural scene. As a result of human ideas and manipulation, text in a natural scene carries meaningful information. In the state of Assam, Assamese is the official language and has over 15 million speakers worldwide. But, since no major research or application is being developed on a real-time natural scene Assamese text detection system, it is a driving force for us to develop and contribute the same. The current state-of-the-art Optical Character Recognition (OCR) engine can only detect document-based images and is unable to detect text in natural scene images of different languages, colors, fonts, sizes, etc. Also, traditional text detection methods like Sliding Window produce huge numbers of weights and make them unusable for real-time text detection. Aiming at the problems of scene text detection, we proposed several versions of the YOLO-DarkNet algorithm for achieving real-time detection, and YOLOv5s is trained on discrete GPUs. Several hyper-parameter tuning methods are applied to optimize our proposed model. The hyper-parameter optimizer Stochastic Gradient Descent (SGD) minimizes the extremely high computing power by attaining faster iterations in exchange for a reduced convergence rate. After training, our proposed model based on YOLOv5s optimized with SGD generates a weight of 13.6 MB with 7.01 million parameters, and the performance on custom datasets achieves a detection speed of 13.3 ms and precision (mAP) of 94.3%.
Educational cluster environment as a criteria for professional competence development of students

In this article, the factors related to the improvement of the methodology of developing the professional competence of students on the basis of personalized educational technologies are analyzed from a scientific point of view, and the professional competence of future history teachers in the environment of the educational cluster is analyzed. Development on the basis of oriented educational technologies, analysis of didactic possibilities, improvement of the subject model based on new approaches, improvement of evaluation criteria and improvement of the level of development of students' professional competence are discussed.
Image Tampering Recognition Algorithm Based on Improved YOLOv5s

In order to improve the extraction effect of the features of the tampered region, the model usually relies on a specific feature defined by hand to optimize the model. However, the effect of the model is not ideal when the tampered mode is unknown. In this paper, the Neck layer of the YOLOv5s model is reasonably embedded into the CBAM attention module, so that the model can more accurately capture the features of the tampered region. In addition, the boundary frame loss is optimized by using EIOU loss function, which separately dissolves the impact factors of aspect ratio to calculate the model. The experimental results show that the image tampering recognition algorithm proposed in this paper based on the improved YOLOv5s can not only effectively identify a variety of tampering modes, but also increase the average accuracy of recognition by 1.57% compared with the benchmark algorithm. The recognition speed is higher than that of LSTM and U-Net algorithms, and the recognition speed also reaches 13.89 images per second, maintaining a high level.
Evaluating a Mechanism for Explaining BDI Agent Behaviour

Explainability of autonomous systems is important to supporting the development of appropriate levels of trust in the system, as well as supporting system predictability. Previous work has proposed an explanation mechanism for Belief-Desire-Intention (BDI) agents that uses folk psychological concepts, specifically beliefs, desires, and valuings. In this paper we evaluate this mechanism by conducting a survey. We consider a number of explanations, and assess to what extent they are considered believable, acceptable, and comprehensible, and which explanations are preferred. We also consider the relationship between trust in the specific autonomous system, and general trust in technology. We find that explanations that include valuings are particularly likely to be preferred by the study participants, whereas those explanations that include links are least likely to be preferred. We also found evidence that single-factor explanations, as used in some previous work, are too short.
An Assessment of Deep Learning Versus Massively Parallel, Non-Linear Methods for Highly-Efficient MIMO Detection

Multiple-user, multiple-input, multiple-output (MU-MIMO) systems supporting a large number of concurrent streams have the potential to substantially improve the connectivity and throughput of future wireless communication systems.Towards this goal, deep learning (DL)-based techniques have recently been proposed for MIMO signal detection.Good performance results have been reported when compared to conventional detection methods, but it is unclear how they measure against state-of-the-art detection techniques.In this work, for the first time, we perform a critical evaluation of DetNet, MMNet, GEPNet, and RE-MIMO, four prominent model-based DL techniques based on different working principles, and assess their reliability, complexity, and robustness against the practical Massively Parallel Non-Linear processing (MPNL) detection approach.The results show that the model-based DL approaches offer promising results but have difficulty adapting to channel models that differ from those on which they were trained.They also exhibit lower reliability and higher complexity than MPNL, even without considering the training stage.We find that, at present, the human-designed MPNL outperforms the DL-based detection methods in virtually all the metrics.Nevertheless, DL-based solutions are rapidly advancing, and further research intended to address their current shortcomings may one day offer advantages over human-designed detection methods.
Enhancing Vector Comparison Using HMMs

Vectors are massively used in many domains.Several techniques have been proposed for comparing two vectors, but they only perform the comparison according to the exact values of the vector components.Additionally, existing techniques used for comparing two vectors having different dimensions are limited by many factors.Furthermore, the problem of comparing two finite sets of vectors has not yet been specifically addressed.This paper attempts to overcome all these limitations by proposing a new technique based on hidden Markov models which enhances existing techniques by giving them the ability to compare two finite sets of vectors, each containing vectors having different dimensions, while precising the set of targeted properties on which the comparison should be performed.Classification experiments conducted on three online available custom datasets demonstrated that when the suitable set of targeted properties is selected, the proposed approach outperforms existing techniques with accuracy gains reaching +82.3%.
User Behavior Detection Using Multi-Modal Signatures of Encrypted Network Traffic

With the development of the network environment and the emergence of new applications, network traffic has become increasingly complex. This paper focuses on user behavior detection based on encrypted traffic analysis. User behavior information plays a critical role in network management and security, leading to extensive research in this domain. This paper introduces two main contributions. Firstly, we present a categorization method for application types and a behavior definition approach for user behavior detection research. This enables consistent behavior definition for each application type, facilitating objective performance comparison with other studies in the field. Secondly, a behavior detection method based on Multi-Modal Signatures is introduced. The multi-modal signatures represent the multiple signatures extracted from encrypted traffic, including header, SNI, and PSD signatures, which are subsequently defined as a rule. To validate the effectiveness of our proposed method, we conducted 4 experiments on 5 SaaS applications. As a result of the experiments, the proposed method achieves an F-measure of 94~99%, and can detect other types of application behaviors with high performance. As this study conducts user behavior detection research based on encrypted traffic analysis, the proposed method can be applied to other research areas that utilize encrypted traffic.