Reviewerly/exHarmony huggingface dataset:
Multimodal intelligent logistics robot combining 3D CNN, LSTM, and visual SLAM for path planning and control

Introduction In today's dynamic logistics landscape, the role of intelligent robots is paramount for enhancing efficiency, reducing costs, and ensuring safety. Traditional path planning methods often struggle to adapt to changing environments, resulting in issues like collisions and conflicts. This research addresses the challenge of path planning and control for logistics robots operating in complex environments. The proposed method aims to integrate information from various perception sources to enhance path planning and obstacle avoidance, thereby increasing the autonomy and reliability of logistics robots. Methods The method presented in this paper begins by employing a 3D Convolutional Neural Network (CNN) to learn feature representations of objects within the environment, enabling object recognition. Subsequently, Long Short-Term Memory (LSTM) models are utilized to capture spatio-temporal features and predict the behavior and trajectories of dynamic obstacles. This predictive capability empowers robots to more accurately anticipate the future positions of obstacles in intricate settings, thereby mitigating potential collision risks. Finally, the Dijkstra algorithm is employed for path planning and control decisions to ensure the selection of optimal paths across diverse scenarios. Results In a series of rigorous experiments, the proposed method outperforms traditional approaches in terms of both path planning accuracy and obstacle avoidance performance. These substantial improvements underscore the efficacy of the intelligent path planning and control scheme. Discussion This research contributes to enhancing the practicality of logistics robots in complex environments, thereby fostering increased efficiency and safety within the logistics industry. By combining object recognition, spatio-temporal modeling, and optimized path planning, the proposed method enables logistics robots to navigate intricate scenarios with higher precision and reliability, ultimately advancing the capabilities of autonomous logistics operations.
Quantum Neural Networks in the Problem of Pattern Recognition

Three computational models are considered: classical, hybrid (NISQ) and quantum computational models, their pros, cons, possibilities of implementation in modern realities and the problem of image classification and its solution using neural networks in these computational models. Three computational experiments were carried out using the described image recognition approaches with visualization of the learning process and a comparison of the final metrics was carried out aimed at clarifying the prospects of the applied approach based on quantum computing.
Nexus between Inflation and Economic Growth in Sub-Saharan Africa Countries.: An Empirical Study using VECM

Using time series data from 1970 to 2021, this study analyzed the relationship between inflation and economic growth in Sub-Saharan African countries. The Vector Error Correction Model (VECM) and Granger Causality techniques were used in the study, and the variable was the consumer price index. Inflation and economic growth are statistically significant and adversely connected in the long run but positively related in the short run, according to the research. Furthermore, the research causality test demonstrates a bidirectional link between economic growth and inflation, as well as a unidirectional relationship between economic growth and FDI. According to the report, Tanzanian policymakers adopt good monetary policies, tools, and institutions to ensure effective and controlled inflation by establishing a steady inflation goal while ignoring comprehensive fiscal measures. The study request urges the Tanzanian government to improve its priorities while strengthening policies such as cost cutting and budgetary consolidation. It also emphasizes that government spending should primarily include development projects that are launched and completed on time to avoid cost escalation.
Impact Of Artificial Intelligence AI in Digital Marketing

In recent years, the field of digital marketing has seen a substantial impact from artificial intelligence (AI). Digital marketing methods that are more successful and efficient because of the use of AI have changed how firms communicate with their clients. The ability to collect and analyse vast volumes of data using AI-powered tools and technologies has improved marketers understanding of consumer behaviour and preferences. More personalized marketing initiatives have resulted from this, which are more likely to connect with consumers and increase conversion rates. Furthermore, AI has made it possible for companies to automate some marketing processes, including lead generation and customer support, freeing up resources and enabling marketers to concentrate on more complex duties. Aside from privacy issues, the use of AI in digital marketing also prompts worries about the possibility of biases being incorporated into the decision-making process. It will be crucial for marketers to carefully assess the ethical implications of AI use as it develops and try to reduce any bad effects. In general, AI has had a favourable impact on digital marketing, allowing companies to contact and interact with their target consumers more successfully.
A Study on the Possibilities of Using Metaverse in Mathematics Education

The purpose of this study was to explore the possibilities of using the Metaverse in mathematics education. The use of the Metaverse started for commercial purposes, and now its use is expanding to all areas, including education. For this study, the researcher analyzed preceding studies related to the Metaverse and the domestic Metaverse platforms for mathematics education in Korea. As a result of the study, the use of Metaverse for mathematics education is still in its beginning stage, and most of the content is limited to mathematical games. However, there are a lot of opportunities and possibilities for mathematics education with Metaverse, and we need to develop the Metaverse platforms specialized for mathematics education with high-quality mathematics content. The researcher suggested to build infrastructure and operate a national level educational Metaverse platform, develop math-specific Metaverse platforms and mathematical content based on field-tested research on the use of the Metaverse platforms. The researcher also emphasized the necessity of teacher education programs for teachers to strengthen the utilization capacity of the Metaverse for mathematics education.
STEM education and personnel training: systematic review

Abstract This article focuses on the generalization of foreign experiences in implementing STEM education and places particular emphasis on the areas of professional training for future teachers of STEM disciplines. By conducting bibliometric analyses of sources from Scopus (93 sources) and Web of Science (42 sources) scientometric databases, four clusters were identified using VOSviewer: 1) STEM education and training; 2) teacher training and primary education; 3) surveys on STEM education; and 4) e-learning and computational thinking in the training of future teachers of STEM disciplines. The research results demonstrate the importance and relevance of STEM education in various fields, and the leading role of the cluster “STEM education and training” has been determined based on the number and strength of links within the network of keyword links of sources on professional training of future STEM teachers. The discussion of the research findings highlights views on the prospects for STEM development in different countries. It is determined that: a) the professional training and social status of teachers are strategically important links in STEM education; b) teachers of “traditional” STEM disciplines (mathematics and natural sciences) require an increased level of knowledge in the field of information technology, particularly through the integration of programming as a component of STEM education; and c) social virtual reality platforms, hyperrealistic modeling, virtual laboratories, educational robotics, augmented, and mixed reality technologies are recommended to increase the motivation of pupils and students to study STEM disciplines.
AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis

Background: Glioblastoma (GBM) is one of the most common malignant primary brain tumors, which accounts for 60–70% of all gliomas. Conventional diagnosis and the decision of post-operation treatment plan for glioblastoma is mainly based on the feature-based qualitative analysis of hematoxylin and eosin-stained (H&amp;E) histopathological slides by both an experienced medical technologist and a pathologist. The recent development of digital whole slide scanners makes AI-based histopathological image analysis feasible and helps to diagnose cancer by accurately counting cell types and/or quantitative analysis. However, the technology available for digital slide image analysis is still very limited. This study aimed to build an image feature-based computer model using histopathology whole slide images to differentiate patients with glioblastoma (GBM) from healthy control (HC). Method: Two independent cohorts of patients were used. The first cohort was composed of 262 GBM patients of the Cancer Genome Atlas Glioblastoma Multiform Collection (TCGA-GBM) dataset from the cancer imaging archive (TCIA) database. The second cohort was composed of 60 GBM patients collected from a local hospital. Also, a group of 60 participants with no known brain disease were collected. All the H&amp;E slides were collected. Thirty-three image features (22 GLCM and 11 GLRLM) were retrieved from the tumor volume delineated by medical technologist on H&amp;E slides. Five machine-learning algorithms including decision-tree (DT), extreme-boost (EB), support vector machine (SVM), random forest (RF), and linear model (LM) were used to build five models using the image features extracted from the first cohort of patients. Models built were deployed using the selected key image features for GBM diagnosis from the second cohort (local patients) as model testing, to identify and verify key image features for GBM diagnosis. Results: All five machine learning algorithms demonstrated excellent performance in GBM diagnosis and achieved an overall accuracy of 100% in the training and validation stage. A total of 12 GLCM and 3 GLRLM image features were identified and they showed a significant difference between the normal and the GBM image. However, only the SVM model maintained its excellent performance in the deployment of the models using the independent local cohort, with an accuracy of 93.5%, sensitivity of 86.95%, and specificity of 99.73%. Conclusion: In this study, we have identified 12 GLCM and 3 GLRLM image features which can aid the GBM diagnosis. Among the five models built, the SVM model proposed in this study demonstrated excellent accuracy with very good sensitivity and specificity. It could potentially be used for GBM diagnosis and future clinical application.
Fine-Grained Cross-Modal Fusion Based Refinement for Text-to-Image Synthesis

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions.Previous approaches generate an initial low-resolution image and then refine it to be high-resolution.Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex.We propose a novel finegrained text-image fusion based generative adversarial networks (FF-GAN), which consists of two modules: Finegrained text-image fusion block (FF-Block) and global semantic refinement (GSR).The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details.And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process.Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.
Regional Inversion of Soil Heavy Metal Cr Content in Agricultural Land Using Zhuhai-1 Hyperspectral Images

With the development of hyperspectral imaging technology, the potential for utilizing hyperspectral images to accurately estimate heavy metal concentrations in regional soil has emerged. Currently, soil heavy metal inversion based on laboratory hyperspectral data has demonstrated a commendable level of accuracy. However, satellite images are susceptible to environmental factors such as atmospheric and soil background, presenting a significant challenge in the accurate estimation of soil heavy metal concentrations. In this study, typical chromium (Cr)-contaminated agricultural land in Shaoguan City, Guangdong Province, China, was taken as the study area. Soil sample collection, Cr content determination, laboratory spectral measurements, and hyperspectral satellite image collection were carried out simultaneously. The Zhuhai-1 hyperspectral satellite image spectra were corrected to match laboratory spectra using the direct standardization (DS) algorithm. Then, the corrected spectra were integrated into an optimal model based on laboratory spectral data and sample Cr content data for regional inversion of soil heavy metal Cr content in agricultural land. The results indicated that the combination of standard normal variate (SNV)+ uninformative variable elimination (UVE)+ support vector regression (SVR) model performed best with laboratory spectral data, achieving a high accuracy with an R2 of 0.97, RMSE of 5.87, MAE of 4.72, and RPD of 4.04. The DS algorithm effectively transformed satellite hyperspectral image data into spectra resembling laboratory measurements, mitigating the impact of environmental factors. Therefore, it can be applied for regional inversion of soil heavy metal content. Overall, the study area exhibited a low-risk level of Cr content in the soil, with the majority of Cr content values falling within the range of 36.21–76.23 mg/kg. Higher concentrations were primarily observed in the southeastern part of the study area. This study can provide useful exploration for the promotion and application of Zhuhai-1 image data in the regional inversion of soil heavy metals.
A Secure Content Trading for Cross-Platform in the Metaverse with Blockchain and Searchable Encryption

In a rapidly evolving metaverse, where the physical and virtual realms naturally merge, users are actively participating in interactive experiences, content creation, and content trading, transcending spatial and temporal constraints. However, with the spread of the metaverse, security concerns have been raised about privacy and the integrity of digital transactions. Several studies have thus focused on enhancing the security and privacy in metaverse. However, there is still a lack of security research on content trading between metaverse platforms. Therefore, this paper proposes a secure content trading system for cross-platform interactions within the metaverse. Leveraging the blockchain technology, the proposed system delivers an ecosystem that ensures secure content management, data integrity, and verifiable transactions. We use smart contracts that enable reliable and automated purchase methods, empowering users and building their trust. In addition, we incorporate searchable encryption to further enhance the user experience within the metaverse by allowing avatars to seamlessly search for and obtain the desired content across different metaverse platforms. The security of the proposed scheme is comprehensively assessed via vulnerability analyses, including BAN logic and Scyther, to identify potential threats and vulnerabilities in various content trading scenarios. The security and performance of the proposed system are compared with those of the related schemes. Result reveals that the proposed scheme is robust and can be applied to content trading systems in dynamic and ever-expanding metaverse environments.
An Empirical Study of Multilingual Scene-Text Visual Question Answering

In recent years, the focus on multilingual modeling has intensified, driven by the necessity to enable cross-lingual Text-based Visual Question Answering (TextVQA), which requires the understanding of questions and answers across diverse languages. Existing research predominantly revolves around the fusion of multimodal information and the processing of OCR data. This paper undertakes an empirical investigation into multilingual scene-text visual question answering, addressing both cross-lingual (English <-> Chinese) and monolingual (English <-> English and Chinese <-> Chinese) tasks, with a primary emphasis on accuracy-based metrics. Our study not only elucidates the impact of varying OCR feature extractors and distinct visual feature extractors on a selection of state-of-the-art models but also delves into the broader landscape of multilingual TextVQA. The experimental outcomes underscore the capability of multilingual pretrained models to effectively handle text-based questions. Moreover, they underscore the importance of leveraging visual features from OCR data and images in enhancing answering performance.
Design and Dynamics of Multicavity Hyperchaotic Maps with Cylinder Attractors

Based on the mathematical model of the elliptical cylinder, we design a new hyperchaotic map with an elliptical cylinder or a cylinder attractor. The dynamical analysis results indicate the proposed system is globally hyperchaotic, and has large Lyapunov Exponents (LEs), and high Permutation Entropy (PE) complexity. Interestingly, the hyperchaotic system exhibits the offset boosting coexistence attractors with respect to the system parameters. In addition, three Multicavity Hyperchaotic Maps (MHCM) are constructed by introducing a symmetric staircase function, which expands greatly the phase space of the system. The MHCM have more complex topological structures and maintain the chaotic performance of the original map. To illustrate the feasibility of the hyperchaotic systems further, we apply them to design a Pseudo-Random Number Generator (PRNG), and implement them on the DSP platform.
Antiderivatives and Integration

Summary In this paper, we introduce indefinite integrals [8] (antiderivatives) and proof integration by substitution in the Mizar system [2], [3]. In our previous article [15], we have introduced an indefinite-like integral, but it is inadequate because it must be an integral over the whole set of real numbers and in some sense it causes some duplication in the Mizar Mathematical Library [13]. For this reason, to define the antiderivative for a function, we use the derivative of an arbitrary interval as defined recently in [7]. Furthermore, antiderivatives are also used to modify the integration by substitution and integration by parts. In the first section, we summarize the basic theorems on continuity and derivativity (for interesting survey of formalizations of real analysis in another proof-assistants like ACL2 [12], Isabelle/HOL [11], Coq [4], see [5]). In the second section, we generalize some theorems that were noticed during the formalization process. In the last section, we define the antiderivatives and formalize the integration by substitution and the integration by parts. We referred to [1] and [6] in our development.
Social Software in the Student Audience: Motivation for Learning

Social software is considered in the article, first of all, as a philosophical and methodological strategy that focuses contemporary educational practices on solving the main problem of Russian education: motivating students for self-education, independent expansion of the sphere of knowledge and competence. According to the authors, the main reason why the motivation of students has be­come a problem (and not only in Russian education) is the lag of educational practices from the rapidly transforming technological, and therefore, socio-eco­nomic demands of modern society. In this situation, a certain contribution to the solution of this problem can be made by the orientation of students to­wards the active assimilation of knowledge in the course of their creative and constructive participation in educational programs that are built using the strategy of social software. The application of this strategy allows the teacher to clearly define the place and role of each participant in the joint activity aimed at achiev­ing the educational goal “programmed” for this team, and the student is able to meaningfully adjust the curriculum. The educational process organized in this way ensures the active assimilation of the relevant body of knowledge and al­lows making such changes in the collaborative structure of the program that im­ply a cognitive expansion of the content component of education. Accordingly, albeit within the framework of the learning community, students are aware of the cultural and historical status of personal cognitive efforts aimed at self-educa­tion. Thus, within the framework of social software, there is a personal increase in the student’s competence, taking into account the immanent social and cul­tural-historical contexts of education.
QUAV attitude control based on linear active disturbance rejection control

Unmanned Aerial Vehicle (UAV) are currently gaining popularity. This paper proposes a method to apply Linear Active Disturbance Rejection Control (LADRC) to Quadrotor Unmanned Aerial Vehicle (QUAV) controller to optimize the traditional PID controller. Firstly, the application and shortcomings of traditional PID control in UAV are introduced, and the LADRC method is proposed. Then linear simplification and Parameter Setting of ADRC are carried out. In the Simulink environment, according to the mathematical model of the QUAV, a QUAV dynamics simulation platform is established. Finally, according to different control channels, different control algorithms are designed, and tracking models are introduced in various attitudes to simulate and verify the control effect of LADRC. The results show that the LADRC controller is effective, the LADRC can be effectively combined with the traditional PID control. The application in the QUAV can realize more precise QUAV speed tracking control and the stable flight of the QUAV. Compared with the traditional PID controller, under the experimental conditions of this paper, the LADRC controller has more precise control accuracy and more efficient control efficiency. Finally, this paper summarizes the design of LADRC and makes a brief outlook on the development of UAVs.