Reviewerly/exHarmony huggingface dataset:
RST: Rough Set Transformer for Point Cloud Learning

Point cloud data generated by LiDAR sensors play a critical role in 3D sensing systems, with applications encompassing object classification, part segmentation, and point cloud recognition. Leveraging the global learning capacity of dot product attention, transformers have recently exhibited outstanding performance in point cloud learning tasks. Nevertheless, existing transformer models inadequately address the challenges posed by uncertainty features in point clouds, which can introduce errors in the dot product attention mechanism. In response to this, our study introduces a novel global guidance approach to tolerate uncertainty and provide a more reliable guidance. We redefine the granulation and lower-approximation operators based on neighborhood rough set theory. Furthermore, we introduce a rough set-based attention mechanism tailored for point cloud data and present the rough set transformer (RST) network. Our approach utilizes granulation concepts derived from token clusters, enabling us to explore relationships between concepts from an approximation perspective, rather than relying on specific dot product functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method. Our method establishes concepts based on granulation generated from clusters of tokens. Subsequently, relationships between concepts can be explored from an approximation perspective, instead of relying on specific dot product or addition functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method.
Pengembangan Modul Pembelajaran Berbasis Kearifan Lokal pada Mata Kuliah Keterampilan Menulis dan Membaca SD

The learning module was developed because there was no teaching material in the form of a module based on local wisdom developed by the course lecturer. Therefore, through this research, it is expected to develop a learning module based on local wisdom. This research aims to develop a learning module suitable for use in teaching and to assess students' responses to the development of the learning module. They provide varied teaching materials to facilitate students' teaching and learning processes. This module was developed as a product through the Research and Development (RnD) method with the development process using the 4-D model (Define, Design, Develop, and Disseminate stages). Data collection instruments consist of validation sheets and questionnaires. The validation sheet is used to assess the validity of the product. The questionnaires are used to assess students' responses to the development of the interactive module. This research showed the feasibility of the research results in terms of validation by subject matter experts and language experts. The validation results of the material expert reached 88.57%, with a very feasible category. Meanwhile, the language expert validation results reached 93.33%, with a very feasible category. The survey results on students' interest in using the module reached 92.67%, with a very interested category. Based on the results, the learning module developed for elementary school reading and writing skills is highly suitable and attractive for teaching and learning.
Marketing Mix Strategy in the Metaverse: The Luxury Brands Case Study

In the context of the Metaverse, the fashion industry is undergoing a historic shift. With the emergence of the Metaverse as a new social and transactional attribute platform, many luxury brands have begun to set foot in the Metaverse to carry out distinctive marketing activities. These luxury companies use the Metaverse to reshape the luxury consumption experience and marketing methods, a trend that is driving innovation and disruption in the luxury industry. This article will analyze the influence of Metaverse on the marketing mix strategy of luxury brands under the background of Metaverse consumption culture, combined with the actual cases of luxury brands, and propose new trends and strategies.
Blockchain Application in Financial Shared Service Centers for Multinational Enterprises

The flourishing digital economy in recent years urges the expanding multinational enterprises to transform financial management. To accomplish efficient operation and effective allocation of internal funds, many multinational enterprises worldwide have established a financial shared service center, shortened as FSSC. Based on the literature review, this thesis summarizes existing problems in FSSC of multinational enterprises, such as information asymmetry, data security that cannot be ensured, high labor cost, and low efficiency of accounting business processing. Then the author proposes the application of blockchain in solving these issues and takes Haier as an example to provide feasible methods of optimizing its financial shared service center with the advantages of blockchain.
Nepal’s School Education: Insights of Basic Education in Bagmati Province

Nepal has made significant progress in school education after 1990s. The government of Nepal has been working towards improving access to school education and enhancing the quality education across the country. Regarding the status of school education, this study has been conducted to examine the present growth trends of school education focusing on basic education in Nepal. For this, descriptive research design has been applied to analyze the data. As per the education policy changes, there has found the significance improvement in net enrolment at basic and primary education, expansion of number of schools and skill development of human resources. The study demands that Nepal needs to bring reality base school education policy for better improving school education quality and provide cent percent school education to all school children.
Hybrid attention feature refinement network for lightweight image super-resolution in metaverse immersive display

Recently, single image super-resolution (SISR) has been an activate research topic for decades, which is a classical fundamental problem in low-level computer vision tasks. With the growth of digital twins technology, metaverse is constructed successfully in many areas. Then with the development of VR/AR equipment, a more efficient SISR network urgently needs to be proposed to meet the application needs of consumer electronics. However, in most SISR methods, the heavy computation and parameters are detrimental to the application in mobile edge computing. And the most lightweight methods lack the ability to retain long-range information and account to a nasty degradation of SR results. To alleviate above problems, we design a hybrid attention feature refinement network (HAFRN) to achieve multi-level information with a few parameters. Moreover, we also propose a newly local concatenate learning strategy, which can use coarse feature maps of shallow layers to maximum the authenticity of SR images step-by-step. In order to capture more feature details in channel, spatial and pixel, an efficient attention mechanism is performed on the network to make sure the interaction among different dimensions. Extensive experiments on five benchmark datasets have proven that the advantages of our method over the state-of-the-art approaches.
Learnable Model Augmentation Contrastive Learning for Sequential Recommendation

Sequential Recommendation (SR) methods play a crucial role in recommender systems, which aims to capture users' dynamic interest from their historical interactions. Recently, Contrastive Learning (CL), which has emerged as a successful method for sequential recommendation, utilizes various data augmentations to generate contrastive views to mine supervised signals from data to alleviate data sparsity issues. However, most existing sequential data augmentation methods may destroy semantic sequential interaction characteristics. Meanwhile, they often adopt random operations when generating contrastive views leading to suboptimal performance. To this end, in this paper, we propose a Learnable Model Augmentation Contrastive learning for sequential Recommendation (LMA4Rec). Specifically, LMA4Rec first takes the model-based augmentation method to generate constructive views. Then, LMA4Rec uses Learnable Bernoulli Dropout (LBD) to implement learnable model augmentation operations. Next, contrastive learning is used between the contrastive views to extract supervised signals. Furthermore, a novel multi-positive contrastive learning loss alleviates the supervised sparsity issue. Finally, experiments on public datasets show that our LMA4Rec method effectively improved sequential recommendation performance compared with the state-of-the-art baseline methods.
Bayesian Neural Networks for Enhanced Predictive Performance in Regression Problems

Abstract: Bayesian statistics has a lot of influence on neural networks and deep learning for artificial intelligence (AI). The inference and learning of Bayesian statistics is based on prior, likelihood and posterior. The prior is the current belief of data field and the posterior is the updated belief after learning from observed data. By repeated learning using prior and posterior distributions, Bayesian statistics provides advanced data learning for AI. In this paper, we compare the previous Bayesian inference and learning methods for AI and propose a model based on Bayesian inference and learning for neural networks and deep learning
Optimization of Takagi-Sugeno-Kang Fuzzy Model Based on Differential Evolution with Lévy Flight

In this article, a novel evolutionary algorithm called differential evolution with Lévy flight (DEFL) algorithm was proposed to optimize the Takagi-Sugeno-Kang fuzzy model (TSK fuzzy model) by finding the optimal hyper-parameter combination. DEFL consists of the conventional differential evolution (DE) algorithm as the primary search method and Lévy flight which is adopted to improve the early convergence problem of DE by its more changeable step size. Moreover, an adaptive soft-switch factor is designed to achieve the balance between exploration and exploitation according to the fitness of parent individuals, which can enhance the searching ability of DEFL. To verify the high performance of our proposed DEFL, two simulations are conducted. First, the five test functions: Ackley, Rastrigin, Sphere, Dixon & Price, and Perm are performed to verify the searching ability of DEFL and other three evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and DE are adopted for comparison. Then, TSK fuzzy model optimized by DEFL is adopted on eight datasets for classification tasks which are compared with the other nine methods. The first simulation shows that DEFL has the best searching ability compared with other algorithms. The accuracy and ranks of the optimized TSK fuzzy model on eight tasks demonstrate the high performance of the model improved by DEFL.
An Improved Framework for Pedestrian Tracking and Counting Based on DeepSORT

Due to the rapid growth in the demand for crowd analysis and monitoring methods, there is an urgent need for pedestrian tracking and counting methods that are more efficient, accurate, and reliable. However, the existing trackers and counters rarely have the three characteristics of efficiency, accuracy and reliability simultaneously. Therefore, we propose a framework named FR-DeepSORT for tracking and counting pedestrians based on DeepSORT. FR-DeepSORT first selects the YOLOv5 network as the object detector. Then the Re-ID information is combined with IoU to construct a cost matrix to improve the tracker by introducing FastReID. Finally, FR-DeepSORT introduces vector cross product and combines the tracking results to monitor the pedestrian crossing dynamics. The experimental results on the MOT tracking benchmark datasets show that the accuracy of our tracker is 89.38%, and the IDF1 value is 82.45%, which are competitive and more reliable to existing tracking methods. The counter error rate is also superior in the real-time dynamic statistical accuracy of the crowd.
Leveraging Dual Encoder Models for Complex Question Answering over Knowledge Bases

Knowledge-based question answering is a hot topic in Natural Language Processing (NLP), especially in addressing complex questions. Existing methods, which transform complex questions into query graphs, often struggle with low-quality graphs. To improve this, we propose a dual-encoder model for generating and ranking query graphs. We incorporate beam search and a scoring function for high-quality graph generation, and use a dual-encoder model with attention mechanism for graph ranking. By extracting semantic structures from complex questions, we further refine the ranking process. Our experiments on benchmark datasets show competitive results, suggesting practical applications in complex question answering.
Weather-Optimized Individual Routes in a Global Scenario

I wish to thank our conference committee members, the track and session chairs, and our
A Simple Yet Effective Data Augmentation for Self-Supervised Multi-stage Centrifugal Pump (MCP) Anomaly Detection

Anomaly detection tasks benefit from self-supervised learning techniques, where models predict or reconstruct parts of augmented input data without relying on explicit human-provided labels. However, many existing self-supervised learning methods employ data augmentation techniques without sufficient justification for their effectiveness. In this study, we present a simple but novel data augmentation method under the context of anomaly detection, obviating the requirement for additional unjustified augmentation techniques. In this method, we leverage data from other operating conditions as augmentation for a specific operating condition, aiming to cluster normal samples across diverse operating conditions. Our approach incorporates the BYOL method as the self-supervised learning framework, coupled with our proposed data augmentation technique for pretraining. Subsequently, we employ the one-class support vector machine (OCSVM) in an anomaly detection experiment involving a Multi-stage Centrifugal Pump (MCP). The evaluation of our method yields promising results, achieving an accuracy of over 83% in two tested operating conditions. This highlights the effectiveness of our approach in accurately detecting anomalies in different operating conditions.
Image Dataset of Pune City Historical Places for Degradation Detection, Classification, and Restoration

Historical and ancient murals hold clues to life in the past. This depicts the culture, worship styles, and social life of the community. This fascinates researchers and scholars who study historical bindings to showcase the modern world. Over time, most of these historical monuments have not been preserved in their original state. Most of these are affected due to natural climatic conditions, civil wars, and natural disasters. It remains clueless and imaginary about the size, shape, and color of the distorted historical monuments. This results in limitations for historical studies, archaeological research, and geographical surveys. In this paper, we have studied the historical places around Pune city. Identified the locations where the monuments have been distorted and reconstructed recently. The construction age, type, color, shape, and size of the monuments are the major parameters of our study. Based on these criteria, we have captured images of these objects through different angles and camera lens. We have collected and categorized these images into folders with the names of historical places. This image dataset contains both captured and augmented images with distinct angles, scales, and directions. It also includes images captured in the daytime and evening with artificial lighting. This image dataset contains a variety of distinct image patterns that are useful as input to train computer-based supervised learning. The machine learning and deep learning algorithms perform efficiently if the input image dataset is large and distinct. Based on the predictive results generated by the machine learning and deep learning models, it is possible to virtually recreate the original monument. This would add a key value to historical research and studies.
A secure and robust color image watermarking method using SVD and GAT in the multiresolution DCHWT domain

This paper introduces an innovative and robust watermarking technique for safeguarding the copyright of color digital images. The method operates within the domain of SVD-based multiresolution discrete cosine harmonic wavelet transforms. In this approach, the pre-processing phase employs successive generalized Arnold transforms to encrypt the RGB watermark layers, significantly enhancing the security of the watermarking algorithm. Subsequently, the blue layer of the host image undergoes R-level 2D-DCHWT processing. The encrypted watermark is embedded by altering the singular values of the host image's approximation coefficients. Additionally, a reliable extraction algorithm is devised to recover the watermark from potentially compromised watermarked images without requiring access to the original image. Through extensive experiments and comparisons with other relevant watermarking algorithms, the findings demonstrate that this scheme effectively embeds a color digital image watermark into the host image. This not only ensures high levels of invisibility but also establishes robustness, making it well-suited for the protection of digital image copyrights.
FEATURES OF ROLE-PLAYING GAMES IN EDUCATION OF INFORMATION TECHNOLOGY FOR SECONDARY SCHOOL STUDENTS

One of the ways to develop and enhance the social skills of modern school students is to engage them in tasks within the framework of role-playing games. Despite numerous studies on the impact of role-playing games and their potential for the development of students in general education, insufficient attention is paid to the development of social skills specifically within the context of learning informatics. Therefore, the relevance of this work remains evident. This article aims to explore the influence of applying role-playing games in the process of teaching informatics, particularly the fundamentals of programming, on the development of social skills. Several research methods were employed, including the analysis and synthesis of scientific literature on the influence of role-playing games on the development of social skills, the definitional analysis of fundamental research concepts, and theoretical generalization. The article examines the specific features and advantages of role-playing games in schoolchild education. It characterizes the pedagogical potential of gaming technologies in informatics education, highlighting that role-playing games serve as an effective means of practical experience acquisition, can fulfill career guidance functions, contribute to the formation and development of social skills, and play an integrative role in shaping students comprehensive understanding of the content of informatics. The article also outlines the requirements for tasks that can be implemented through the organization of role-playing games. It provides an example of a role-playing game related to the fundamentals of programming called "IT Company," describing the overall content of the game, revealing the participants' activities at each stage of the game, and presenting the key outcomes.
RESEARCH-BASED STUDENT PRACTICE IN GERMAN UNIVERSITIES

Higher education in Germany is characterized by a tendency to form an educational space of an organic combination of student learning and scientific research activities. Involvement of applicants into scientific researches contributes to the formation in them of a stable interest in the future profession, an active position in life, motives for realizing one's potential, self-development and self-improvement throughout life. The experience of Germany is of particular interest for domestic research, since this country has developed methodological and technological potential, as well as effective forms and methods for organizing student research activities, including during practice. The purpose of the article is to analyze the peculiarities of practical training of students in higher education in Germany as a means of forming their research competence. Research methods – analysis of scientific literature, systematization (to clarify the key concepts of the study), comparative analysis (to clarify the peculiarities of practical education in higher education in Germany and its research component), generalization (to form the author's conclusions). It has been established that research training of future teachers during practice is carried out by ensuring its consistency and modularity - orientation practice takes place on the basis of the university, professional field practice (referendariat) takes place at the Center for Practical Training of Future Teachers. Interaction between the university and the Center occurs in the following areas: agreement on the basic principles of student learning in practice; defining a practical training strategy; exchange of experience in practical training of future teachers. It has been determined that the German model of practical training of students includes five components (blocks): 1) subject-institutional (cooperation council, Centre for Practical Training of Future Teachers, Centre for Pedagogical Research, practical semester working group, coordinators, university teachers, school teachers, students); 2) functional-practical (determines the functional responsibilities of subjects of practical training); 3) content-targeted (represented by the stages and goals of practical training, defined by the humanistic-constructivist concept); 4) procedural-technological (involves the formation of research skills through the use of such forms and methods as: seminar, consultation, discussion, case analysis, individual task, reflection of performance results, portfolio); 5) control and evaluation (involves comparing the learning outcomes obtained by students in practice with the regulatory requirements for the professional activities of teachers).
Multimedia Multilevel Security by Integrating Steganography and Cryptography Techniques

Multimedia-based Steganography is famous for its security purpose, These steganography techniques were used by our ancestors and it is still carried out now with better and vast technology. The aim is to provide better security in an effective and secure manner where the original data is covered and hidden by some of the media covers like video, audio, image, text, etc. so that only the sender who sends it and the receiver who receives it can only see the secret data inside it. In this project, we are combining all the steganography types and giving the user the advantage of selecting his/her own choice of multimedia cover to hide the real content. The options of the different algorithms will be provided so that the users who are comfortable with that algorithm have the privilege to select it and the file will be processed with that algorithm. This steganography technique is used by the government to send information that supports national security, The company uses it for digital copyrighted media, and military services also use this technology to send it to the army who are on a mission. We aim to provide the same with more security and a rich user experience. In this survey, a variety of methods is used to provide the same facility and recent research in the field.