Reviewerly/exHarmony huggingface dataset:
Spatial Pyramid Attention Enhanced Visual Descriptors for Landmark Retrieval

Landmark retrieval, which aims to search for landmark images similar to a query photo within a massive image database, has received considerable attention for many years. Despite this, finding landmarks quickly and accurately still presents some unique challenges. To tackle these challenges, we present a deep learning model, called the Spatial-Pyramid Attention network (SPA). This network is an end-to-end convolutional network, incorporating a spatial-pyramid attention layer that encodes the input image, leveraging the spatial pyramid structure to highlight regional features based on their relative spatial distinctiveness. An image descriptor is then generated by aggregating these regional features. According to our experiments on benchmark datasets including Oxford5k, Paris6k, and Landmark-100, our proposed model, SPA, achieves mean Average Precision (mAP) accuracy of 85.3% with the Oxford dataset, 89.6% with the Paris dataset, and 80.4% in the Landmark-100 dataset, outperforming existing state-of-theart deep image retrieval models.
Networking AI-Driven Virtual Musicians in Extended Reality

Music technology has embraced Artificial Intelligence as part of its evolution. This work investigates a new facet of this relationship, examining AI-driven virtual musicians in networked music experiences. Responding to an increased popularity due to the COVID-19 pandemic, networked music enables musicians to meet virtually, unhindered by many geographical restrictions. This work begins to extend existing research that has focused on networked human-human interaction by exploring AI-driven virtual musicians’ integration into online jam sessions. Preliminary feedback from a public demonstration of the system suggests that despite varied understanding levels and potential distractions, participants generally felt their partner’s presence, were task-oriented, and enjoyed the experience. This pilot aims to open opportunities for improving networked musical experiences with virtual AI-driven musicians and informs directions for future studies with the system.
Exploring the use of Mobile Devices as a Bridge for Cross-Reality Collaboration

Augmented and Virtual Reality technologies enable powerful forms of spatial interaction with a wide range of digital information. While AR and VR headsets are more affordable today than they have ever been, their interfaces are relatively unfamiliar, and a large majority of people around the world do not yet have access to such devices. Inspired by contemporary research towards cross-reality systems that support interactions between mobile and head-mounted devices, we have been exploring the potential of mobile devices to bridge the gap between spatial collaboration and wider availability. In this paper, we outline the development of a cross-reality collaborative experience centered around mobile phones. Nearly fifty users interacted with the experience over a series of research demo days in our lab. We use the initial insights gained from these demonstrations to discuss potential research directions for bringing spatial computing and cross-reality collaboration to wider audiences in the near future.
Collaborative Score Transformations in Online Music Lessons: the MusiCoLab Toolset

In the world of music teaching and learning, music notation artifacts serve as indispensable material that facilitate effective communication, interpretation, and manipulation of musical compositions. Through these artifacts, educators can guide students towards achieving the objectives of a music lesson, nurturing not only technical proficiency but also fostering creativity and artistic expression. This article presents the development of VHVCoLab, a versatile web application that addresses the needs of music education, by seamlessly integrating practices for synchronous collaboration and asynchronous interaction. The article presents our efforts for collecting the requirements of music instructors, with respect to music notation artifacts and their transformative capabilities, our design and implementation approach for supporting established and novel application affordances as well as an informative pilot assessment guiding our ongoing and future developments. As the domain of online music practicing has attracted plenty of interest in the last decade, we aspire that the VHVCoLab application exemplifies the possibilities of harnessing online platforms to enrich the musical experience and propel the growth of aspiring musicians worldwide.
Performance Evaluation of Object Detection Deep Learning Model for Paralichthys olivaceus Disease Symptoms Classification

Paralichthys olivaceus accounts for a large proportion, accounting for more than half of Korea's aquaculture industry. However, about 25-30% of the total breeding volume throughout the year occurs due to diseases, which has a very bad impact on the economic feasibility of fish farms. For the economic growth of Paralichthys olivaceus farms, it is necessary to quickly and accurately diagnose disease symptoms by automating the diagnosis of Paralichthys olivaceus diseases. In this study, we create training data using innovative data collection methods, refining data algorithms, and techniques for partitioning dataset, and compare the Paralichthys olivaceus disease symptom detection performance of four object detection deep learning models(such as YOLOv8, Swin, Vitdet, MvitV2). The experimental findings indicate that the YOLOv8 model demonstrates superiority in terms of average detection rate (mAP) and Estimated Time of Arrival (ETA). If the performance of the AI model proposed in this study is verified, Paralichthys olivaceus farms can diagnose disease symptoms in real time, and it is expected that the productivity of the farm will be greatly improved by rapid preventive measures according to the diagnosis results
Solving Complexity Dataset in e-Ticketing using Machine Learning to Determine Optimum Feature

e-ticketing is one of the common applications used in technical support in Information Technology (IT) and has been used worldwide in any field of company. The benefits of e-ticketing can reduce the human efforts, increase the sufficiency of system and provides the benefits and efficiency to the customers. Also, e-ticketing ticketing system can enhance worker safety, improving productivity, increasing project efficiency and cause the good impact on the performance of the business in terms of profitability. The main objective of this study is defining the model performance in each important feature by analysing the complex dataset by using logistic regression as a Machine Learning (ML) algorithm. In evaluation the performance of classifier, the dataset is injected to Python programming and split into 90% as training set and 10% for the testing set. From the analysis, the study found that only 3 out of 11 independent features in dataset that are relevant chosen to proceed the ML analysis. From the result, the accuracy for sct_short_description, sct_cmdb_ci, and sct_assignment_group is 41.65%, 48.77% and 96.49%, respectively. It showed that the accuracy’s result for the sct_assignment_group resulted that the model is very good accuracy and indicate that the model is well performing. Meanwhile, the value of F1-score is 96.11% in each feature. This result indicates that the model has a good balance of precision and recall in its binary classification predictions. Hence, the study considers the sct_assignment_group as a best features to proceed the analysis. The future study will consider dealing the combination of complexity features by implementing more analysis on ML such as Support Vector Machines and Naïve Bayes.
Comparative Analysis of Improved Dirichlet Process Mixture Model

Due to the development of information technology, large amounts of data are generated every day in various industries such as engineering, healthcare, finance, anomaly detection, image recognition, and artificial intelligence. This massive data poses the challenge of analyzing accurately and appropriate classifications. The traditional clustering methods require specifying the number of clusters and are mostly based on distance, which cannot effectively consider the correlations between different indicators of high-dimensional and multi-source data. Moreover, the number of clusters cannot automatically adjust when new data is generated. In order to improve the clustering analysis of high-dimensional and multi-source data in a big data environment, this study utilizes non-parametric mixture models based on distribution clustering, which does not require specifying the number of clusters and can auto update with the data. By combining Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and the non-parametric Bayesian method called Dirichlet Process Mixture Model (DPMM), the Bayesian non-parametric PCA model (PCA-DPMM) and Bayesian non-parametric t-SNE model (TSNE-DPMM) are proposed. The Chinese restaurant process of DPMM is used for sampling by introducing a finite normal mixture distribution. The clustering results on the iris dataset are compared and analyzed. The accuracy of DPMM and TSNE-DPMM reaches 0.97, while PCA-DPMM achieves a maximum accuracy of only 0.94. When different numbers of iterations are set, TSNE-DPMM maintains an accuracy ranging from 0.92 to 0.97, DPMM ranges from 0.66 to 0.97, and PCA-DPMM ranges from 0.73 to 0.94. Therefore, the proposed TSNE-DPMM ensures accuracy and exhibits better model stability in clustering results. Future research can explore the improvement of the model by incorporating deep learning algorithms, among others, to further enhance its performance. Additionally, applying the TSNE-DPMM model to data analysis in other fields is also a future research direction. Through these efforts, we can better tackle the challenges of analyzing high-dimensional and multi-source data in a big data environment and extract valuable information from it.
Interdisciplinary Talent Cultivation Model for Theatre Performance Based on Big Data Analysis

Abstract The first part of this paper examines the issues of interprofessional talent cultivation for theatrical performances and develops a model of interprofessional talent cultivation for theatrical performances based on the competency model. Secondly, to evaluate the effectiveness of the talent cultivation model, a quality evaluation index system is constructed. Then, the index weights are calculated by using the ordinal relationship analysis method in big data analysis, and the object element topology law is introduced to comprehensively evaluate the quality of talent cultivation. Finally, an empirical analysis was conducted to verify the effectiveness of the talent cultivation model and the evaluation system. The results show that the quality of professional teaching has the greatest influence on the cultivation of interdisciplinary talents in theatrical performance, with a weight of 0.3508, and the comprehensive correlation can effectively realize the ranking of the cultivation level of professional talents in theatrical performance. This shows that big data analysis in theater performance inter-specialty talent cultivation can provide effective data support for the development of university disciplines and help universities innovate and reform the theater performance specialty.
DualStream: Spatially Sharing Selves and Surroundings using Mobile Devices and Augmented Reality

In-person human interaction relies on our spatial perception of each other and our surroundings. Current remote communication tools partially address each of these aspects. Video calls convey real user representations but without spatial interactions. Augmented and Virtual Reality (AR/VR) experiences are immersive and spatial but often use virtual environments and characters instead of real-life representations. Bridging these gaps, we introduce DualStream, a system for synchronous mobile AR remote communication that captures, streams, and displays spatial representations of users and their surroundings. DualStream supports transitions between user and environment representations with different levels of visuospatial fidelity, as well as the creation of persistent shared spaces using environment snapshots. We demonstrate how DualStream can enable spatial communication in real-world contexts, and support the creation of blended spaces for collaboration. A formative evaluation of DualStream revealed that users valued the ability to interact spatially and move between representations, and could see DualStream fitting into their own remote communication practices in the near future. Drawing from these findings, we discuss new opportunities for designing more widely accessible spatial communication tools, centered around the mobile phone.
General-Purpose Planning Algorithms in the Card Game Duelyst II

Duelyst II is an online collectible card game (CCG) that features a 9x5 grid board, making it a cross between the popular CCG Hearthstone and chess. It is a partially-observable stochastic game (POSG) with a large branching factor and the ability to take several actions in a time-limited turn, making it a challenging domain for AI. The existing "starter AI" in the game is an expert-rule-based player that is limited to using certain decks and is weak against humans. We develop simple general-purpose planning algorithms that are able to consistently beat the starter AI using little domain knowledge and no learning. The most complex of these is a variant of Monte Carlo tree search (MCTS), for which we show that a novel action factoring method is helpful under certain conditions.
SCAF-DG: A Multi-site Medical Image Denoising With A Domain-Generalized Spatial-Channel Attention Fusion

Images from multiple medical sites usually contain varying noise levels that can affect the generalization performance of the denoising models. Domain generalization (DG), which seeks to learn a model that can generalize to an unseen test domain, has not been extensively explored in medical image denoising for multi-site images. We present an approach for multi-site medical image denoising, which addresses the issue of varying noise levels across different imaging sites. Our proposed method, called SCAF-DG (Spatial-Channel Attention Fusion for Domain Generalization), employs two attention units to extract both domain-specific channel features and mutually-invariant spatial features from the noisy images. These features are then combined to form a rich feature space. Additionally, we utilized skip connections to preserve structural information during the denoising process. We evaluate SCAF-DG on publicly available brain magnetic resonance image (MRI) datasets from three different imaging sites, and demonstrate an average increase of 1.01 in peak signal-to-noise ratio (PSNR) and 0.016 in structural similarity index measure (SSIM) for denoising images. Our results demonstrate that SCAF-DG has the potential to generalize well to new, unseen test domains, and outperforms existing state-of-the-art denoising models on multi-site medical images.
ANALISIS BANJIR SUNGAI MENGGUNAKAN MODEL HEC-RAS DI WILAYAH DAS TABANIO KABUPATEN TANAH LAUT PROVINSI KALIMANTAN SELATAN

This study aims to determine the amount of water runoff and water depth in the Tabanio Watershed, Tanah Laut Regency. The study was conducted using the HEC-RAS software to generate a flood simulation model. Data analysis included Digital Elevation Model for National Scale (DEMNAS) data, flood discharge data, rainfall data, river bathymetry, and tidal data. DEMNAS data was obtained from the website of the National Geospatial Information Agency (BIG), rainfall data was obtained from the Center for Hydrometeorology and Remote Sensing (CHRS) website. Rainfall data analysis involves selecting the maximum daily rainfall value to determine the flood discharge. The hourly flood discharge is calculated using the Synthetic Unit Hydrograph method with the Nakayasu approach. River bathymetry data is processed as additional data for DEM, with interval recording done every 0.2 seconds. Tide data was obtained through prediction based on the location constant closest to the research area and analyzed using the Pasut.exe software for a period of 6 days. Based on the results of flood analysis research using the HEC-RAS model in the Tabanio Watershed, Tanah Laut Regency, South Kalimantan Province, it can be concluded that the model results show the water depth and area of water runoff at maximum discharge. as follows: less than 0.5 meters with a very low category covering an area of 4,168.86 hectares, 0.51 to 1.5 meters with a low category covering an area of 6,417.79 hectares, 1.51 to 2.5 meters with a medium category covering an area of 1,987, 25 hectares, 2.51 to 3.5 meters with a high category of 741.47 hectares, and above 3.51 meters with a very high category of 113.69 hectares. So that the total water runoff reaches 13,429.05 hectares.
Quantum control based on three forms of Lyapunov functions

Abstract This paper introduces the quantum control of Lyapunov functions based on state distance, Lyapunov functions based on the mean of imaginary quantities, and Lyapunov functions based on state errors. In this paper, the specific control laws under the three forms are given, and the stability analysis is analyzed by the LaSalle invariance principle, and the numerical simulation is carried out in the 2D test system. For the Lyapunov function based on the average of virtual mechanical quantities, the particle swarm algorithm and simulated annealing algorithm are combined with the analysis in the calculation process. Finally, a unified form of control law under the three forms is given.
Human activity recognition with machine learning

The huge advancement of the Internet of Things and sensors technology encourages researchers to work on sensor-based applications such as activity recognition. Although a lot of work has been done in the last decade still there are considerable challenges that can affect the performance of the activity recognition system in real-time scenarios. In the last decades, machine learning has confirmed its usefulness in enhancing the efficiency of the system. Several machine learning algorithms have been inspected to address the existing issues in activity recognition systems. This study presents a real-time implementation of human activity recognition. The four machine learning algorithms; decision tree, KNN, SVM, and random forest were implied to verify the accuracy of the system. The random forest gives the highest accuracy at 99.7%.
Web3 Üzerine Yazılan Makalelerin Bibliyometrik Analizi

In this study, it is aimed to analyze the articles on Web3 and present the general situation about Web3 to researchers. Within the scope of this purpose, the trends of the studies published on Web3 according to years, the trends of the journals in which they were published, the institutions and countries that contributed the most, the keywords used in the studies, the topics and themes based on the studies, and the distribution of research areas were revealed. The research is based on bibliometric analysis. A total of 280 articles published in WoS and SCOPUS databases were analyzed. WoSViewer and Bibliometrix programs were used in data analysis. The findings were analyzed and interpreted separately in WoS and SCOPUS. As a result of the research, there was a significant increase in studies on Web3 in 2022, and the journals with the highest number of publications in WoS and SCOPUS differ. The countries that contributed the most to Web3 were China, The USA, India, England, Germany. The most cited countries are China, the USA, India, England, Iran and Canada. In general, it can be said that countries and institutions have conducted studies on Web3 by addressing many issues related to Web3. Within the scope of the results, Web3 studies address many different disciplines with many topics. However, there is a need to deepen the studies. The policies, practices and even the laws created by countries on Web3 are important for studies on Web3. Blockchain is one of the most studied topics, but it is understood that there are some hesitations about blockchain security. For this reason, Web3 studies can be conducted to increase blockchain security.
The Idea of Digital Currency for India based on Blockchain Technology: A Conceptual Study

This paper aims to explain the idea of digital currency, its prospects in the Indian context, its role in digital transformation, and how it would be beneficial to our country, businesses, and different user segments. As in the phase of digital transformation, a constant improvement in technology is happening day by day globally. In today's virtual world, where everything is on its way forward, why should we not develop our economy accordingly so that we will be able to strengthen ourselves globally? And we had to begin the work of transformation by converting our currency to digital mode, i.e., digital currency, which is accessible in virtual or electronic form rather than physical.&#x0D; The paper opted for an exploratory study using existing literature, case studies, books, electronic resources, speeches of various officials of the central bank, and experts on national as well as international platforms, which gave clarity on some of the core elements involved in digital currency, like the concept of blockchain with its kinds and segments like wholesale &amp; retail and also with use case data in different counties across the globe, strengths, and weaknesses; initial coin offerings (ICO); and central bank digital currency (CBDC).&#x0D; In this paper, data were complemented by documentary analysis, in which literature like the concept note of the Reserve Bank of India, the discussion paper of the Bank of England, and existing literature regarding centrally backed currency were studied for the clarity of the above work through figures like the money flower issued by the BIS (Bank for International Settlements). The paper concludes with discussion, suggestions, and future directions, with the conclusion that the user segments for the virtual currency backed by a central bank are classified.
Exploring Privacy-Preserving Methods via Perturbation Data Mining Employing Diverse Noise Strategies

Knowledge discovery from data, commonly referred to as data mining. it involves the extraction of significant information, which may be previously unknown, concealed, or relevant, from extensive data sets or databases through the utilization of statistical methodologies. With the introduction of enhanced hardware technologies, there has been a proliferation in the storage and recording of personal data pertaining to individuals. Sophisticated organizations employ data mining algorithms to uncover hidden patterns or insights within data. Data mining techniques find application in diverse fields such as marketing, medical diagnosis, forecasting system, and national security. However, in scenarios where data privacy is paramount, mining certain types of data without violating the privacy of data owners presents a formidable challenge, sparking growing concerns among privacy advocates. To address these concerns, it is imperative to advance data mining procedures that are complex to individual privacy considerations. Perturbation of data plays a pivotal role in Privacy-Preserving Data Mining (PPDM). Additive data safeguard data privacy. In contrast, multiplicative data perturbation involves a series of transformations, including rotation, translation, and the addition of noise components to the perturbed data copy.