Reviewerly/exHarmony huggingface dataset:
“I laughed like a madman at the music alone...” Borodin’s operetta heroic warriors in Victor Krylov's letters to Nikolay Savitsky

The article is the first publication of 22 letters by playwright Viktor Krylov to Nikolay Savitsky, Stage Director of the Moscow Opera Сompany. The letters were discovered in 2020 in the State Historical Museum. The preparation of the article included dating and commenting on the letters. All the letters date from 1866 to 1867. Most of them are devoted to Heroic Warriors (1867), an operetta by Alexander Borodin based on the libretto by Krylov. The discovered documents allow to trace back how the operetta was composed, beginning with the emergence of Krylov's idea. The article clarifies the chronology of Krylov's work on the libretto as well as Borodin's work on the music of Heroic Warriors. It also reveals some previously unknown details of the composer's concept. Krylov writes in detail about passing two stages of theatrical censorship. It allows to establish the correct date of the final approval by the Theater and Literature Committee: July 29, not June 29 as previously thought. This fact explains why Borodin did not have enough time to compose fully original music for the operetta as well as why he was so often forced to parody works of other composers.
Components and Events Identification Challenges and Practices in Interactive Prototypes Construction: A Field Study in a Design Course

Prototyping technologies are crucial for sustaining an iterative prototype process that is efficient and adaptable. For designers, building, programming, and troubleshooting an interactive prototypes’ electronic connections, communications, and computing system’s components continues to be a challenging task. This fieldwork study explores, describes, and analyses the practices, tools, and technologies used by design students in the construction of interactive prototypes on a university course. The research investigation reviews the interactive prototypes and presents and analyses excerpts of interviews with students. Our findings shed light on the socio-technical factors that influence the choice of digital components. The study describes the types of digital connections and communications among prototype components and the practices adopted by design students to build them. Last, our results show that practices to identify components and users’ interactions were based on sophisticated time- or event-based strategies. Finally, the implications of the findings of the study are discussed.
Research on the Application of Cultural Information Dissemination Model in Digital Smart Community Based on Metaverse Human-Computer Interaction

From the perspective of Metaverse technology, this paper studies the ternary-subject structure of spatial culture information dissemination in digital community, explores the information dissemination form of digital community and the production of content, and constructs the evaluation model of Metaverse community based on the characteristics of community organization dissemination mechanism. In community, through the entities, virtual digital avatar, and AI(Artificial Intelligence), the deep integration of human and computer will lead to the emergence of various forms of intelligent information Pathogen transmission. Different types of subjects combined with community digital ecology will form the dissemination of cultural information content, dominated by AIGC(Artificial-Intelligence Generated Content). Communities will eventually realize an organizational communication form characterized by decentralization, autonomy and organization, promoting the formation of an efficient information dissemination mechanism; Based on the goal of reliability, availability and efficiency, this paper constructs a dynamic evaluation model of Metaverse community, and seeks more in-depth and complete verification in subsequent research.
Research on the design of children’s picture-looking and speaking system based on artificial intelligence

With the increasing number of images, the machine and describes the image content in image text with people’s reading habits has become an important research content in the field of artificial intelligence. The research goal of this paper is to design and realize a picture speaking system using image text description technology, to apply artificial intelligence technology in the field of education and to assist early childhood education. To achieve this goal, this paper studies the image text description technology.
Prediksi Jumlah Sampel Tes PCR Covid-19 Menggunakan Metode Single Moving Average

Covid-19 has spread all over the world. This virus was first discovered in the city of Wuhan, China. To avoid this virus, the community has implemented several strategies such as wearing masks, washing hands and conducting swab tests to determine whether the results are contaminated with the Covid-19 virus or not. In Indonesia, swab tests have been provided, such as the Antigen swab test and the PCR swab test. However, WHO recommends a PCR swab test because the results are more accurate. Until now, Indonesia has not been able to meet the minimum number of PCR test samples according to the WHO (World Health Organization) version, which is 1 per 1,000 people every week. Therefore, the purpose of this researcher is to create a system to predict the rise and fall of the number of daily PCR test samples at laboratories in West Java in the future. This research was conducted in West Java because the capacity of the number of PCR test samples was not optimal, only relying on manuals with limited human resources. Therefore, the authors are able to predict the number of PCR test samples in the future using the Single Moving Average method. This method takes a group of observations and looks for the average value of the daily development of the number of PCR test samples in West Java, as a forecast in the future. The number of Covid-19 PCR test samples from the MSE (Mean Square Error) calculation is 689,873. With the SMA method, it makes it easier for admins to see predictions for the number of PCR samples for the future.
Lidar-Based Multiple Object Tracking with Occlusion Handling

Occlusion remains an issue in multiple object tracking, which could cause ambiguity in object detection, such as incorrect or missing detection. Under occlusion, a track could experience an early termination, resulting in identity switches and/or fragmentation. To recover from different lengths of occlusions, the track should be maintained by considering its occlusion status. To address the issues mentioned above, we propose an indicator that can model the track's occlusion extent via geometric information provided by LiDAR data. Through incorporating the indicator into the track management and data association process, it is feasible to prevent tracks from premature termination. The proposed method is evaluated on the collected dataset which undergoes frequent and severe occlusions. Compared to the state-of-the-art probabilistic tracking approach, our approach achieves improvements of 3.26% in MOTA and 5.36% in IDF1. Additionally, we obtain 9.89% improvements in IDF1 specifically for objects experiencing severe occlusions.
Saúde mental e programa comunitário para pessoas acima de 60 anos

Introduction: The programs that develop actions focused on the health of the older adult in their integrality, provide improvements in aspects of quality of life (physical, functional, social, emotional). With the spread of an acute respiratory infection caused by the coronavirus SARS-CoV-2 (Covid-19), internationally eligible as "severe" and highly transmissible, there was an extensive period of incarceration at home by the majority of the population, here, in this case, Brazilian, resulting in a large part of the older adult population emotional, social sequelae and declines in functional capacity. Among them, we can highlight anxiety and depression, psychopathological symptoms that are related as risk factors for quality of life affecting the performance of their daily activities. Objective: To compare, through the Geriatric Depression Scale GDS-15 and GDS-5 and the Geriatric Anxiety Inventory (GAI), possible changes in psychopathological parameters in older people after 3 months of participation in the activities developed in the “Living Better” project. Methods: In this section 35 participants of the Better Living Project, carried out at the Community Health Center - CSC, Deise de Fátima Araújo de Paula, health unit of the City Hall of Palmas (TO), answered the questionnaires GAI and GDS-15 and GDS-5 at the beginning and after 3 months of activities offered (stretching, memorization, lectures). Descriptive statistics, normality test (Shapiro-Wilk), and T-student test for dependent samples were performed using the Jamovi 2.2 software. We adopted a significance level of p ≤ 0.05 for all variables. Results: There were no data related to depression during pre and post-collections (GDS-15 and GDS-5). When comparing the pre-and post-GAI data, one observed that there was a reduction in anxiety parameters, statistically significant according to p= 0.0471 in an interval of 3 months of participation in the activities offered by the project. Conclusion: It is perceived the need for a more cautious look at the older population, which was highlighted to the increase in anxiety symptoms presented by the research participants, pointing to the participation in physical and social exercise programs as fundamental factors for the maintenance of integral health.
Impact of corporate social responsibility and corporate governance on the performance of nonfinancial companies

This research paper focuses on the growing importance of corporate social responsibility (CSR) in the business world, particularly in the Gulf Cooperation Council (GCC) region. The main aim is to bridge the gap by assessing the impact of CSR and corporate governance on financial performance. Ultimately, this paper emphasizes the strategic importance of CSR for improving financial performance and promoting trustworthiness and public image. This paper applied the ordinary least squares (OLS) and panel regressions (fixed and random) to investigate the impact of CSR, board size, independent directors, company size, and leverage as independent variables on the financial performance as the dependent variable (return on assets — ROA). The data were collected from Refinitiv Eikon platform for 210 listed nonfinancial companies for the last ten years (2013–2022). The results suggested that the higher the company’s involvement in CSR, the more the number of board members and the more independent directors the higher the performance. In addition, the higher the leverage in the GCC the less is the profitability of firms. Finally, the larger the company the better is the performance. Such results imply that more board of directors should be hired and increase compliance with the CSR principles to achieve better performance.
Real-time gesture-based control of UAVs using multimodal fusion of FMCW radar and vision

Abstract Gesture-based control has gained prominence as an intuitive and natural means of interaction with unmanned aerial vehicles (UAVs). This paper presents a real-time gesture-based control system for UAVs that leverages the multimodal fusion of Frequency Modulated Continuous Wave (FMCW) radar and vision sensors, aiming to enhance user experience through precise and responsive UAV control via hand gestures. The research focuses on developing an effective fusion framework that combines the complementary advantages of FMCW radar and vision sensors. FMCW radar provides robust range and velocity measurements, while vision sensors capture fine-grained visual information. By integrating data from these modalities, the system achieves a comprehensive understanding of hand gestures, resulting in improved gesture recognition accuracy and robustness. The proposed system comprises three main stages: data acquisition, gesture recognition, and multimodal fusion. In the data acquisition stage, synchronized data streams from FMCW radar and vision sensors are captured. Then, machine learning algorithms are employed in the gesture recognition stage to classify and interpret hand gestures. Finally, the multimodal fusion stage aligns and fuses the data, creating a unified representation that captures the spatial and temporal aspects of hand gestures, enabling real-time control commands for the UAV. Experimental results demonstrate the system‘s effectiveness in accurately recognizing and responding to hand gestures. The multimodal fusion of FMCW radar and vision sensors enables a robust and versatile gesture-based control interface.
Impact of Mobile Technology on Learning and Library Policies

This paper presents interim results of a study conducted in 2021 and being currently continued. The study aims to analyse the up-to-date demands of information and knowledge users in regard to library and information services. Its main task is to determine to what degree information users use mobile devices to read, learn and access information resources. A considerable part of the survey is dedicated to the analysis of the implementation of mobile digital services and learning in library service as well as to the users’ awareness and support of international policies for mobile access to learning and library services and resources. The study has been conducted as part of a research project entitled “Information portal for mobile learning and mobile access to library services and resources”, won in the Competition for financial support for projects of junior basic researchers and postdocs – 2019, organized by the National Science Fund at the Ministry of Education and Science of the Republic of Bulgaria, Contract №KP-06-M35/2 from 18.12.2019, led by Chief Assist. Prof. Elisaveta Dimitrova Tsvetkova, PhD. The main goal of the project is through a systematic study of the issues related to mobile learning and mobile access to library services and resources to achieve new scientific knowledge, combined and presented in a common information Internet portal, to reveal and summarise the positive changes that modern mobile technologies provide as opportunities for access to training and information.
Reducing LLM Hallucinations using Epistemic Neural Networks

Reducing and detecting hallucinations in large language models is an open research problem. In this project, we attempt to leverage recent advances in the field of uncertainty estimation to reduce hallucinations in frozen large language models. Epistemic neural networks have recently been proposed to improve output joint distributions for large pre-trained models. ENNs are small networks attached to large, frozen models to improve the model's joint distributions and uncertainty estimates. In this work, we train an epistemic neural network on top of the Llama-2 7B model combined with a contrastive decoding feature enhancement technique. We are the first to train an ENN for the next token prediction task and explore the efficacy of this method in reducing hallucinations on the TruthfulQA dataset. In essence, we provide a method that leverages a pre-trained model's latent embeddings to reduce hallucinations.
X Modality Assisting RGBT Object Tracking

Learning robust multi-modal feature representations is critical for boosting tracking performance. To this end, we propose a novel X Modality Assisting Network (X-Net) to shed light on the impact of the fusion paradigm by decoupling the visual object tracking into three distinct levels, facilitating subsequent processing. Firstly, to tackle the feature learning hurdles stemming from significant differences between RGB and thermal modalities, a plug-and-play pixel-level generation module (PGM) is proposed based on self-knowledge distillation learning, which effectively generates X modality to bridge the gap between the dual patterns while reducing noise interference. Subsequently, to further achieve the optimal sample feature representation and facilitate cross-modal interactions, we propose a feature-level interaction module (FIM) that incorporates a mixed feature interaction transformer and a spatial-dimensional feature translation strategy. Ultimately, aiming at random drifting due to missing instance features, we propose a flexible online optimized strategy called the decision-level refinement module (DRM), which contains optical flow and refinement mechanisms. Experiments are conducted on three benchmarks to verify that the proposed X-Net outperforms state-of-the-art trackers.
Research on English Customized Distance Assisted Curriculum System Based on Computer "Internet +" Technology

This manuscript introduces a new English customized distance education teaching method based on computer network technology. This manuscript first discusses the theory of distance teaching in English classroom based on "Internet +" technology. Establish three levels of online education platforms in English courses. This software is developed based on Mutagen Creator. This manuscript proposes a network "Internet +" technology based on digital image processing technology. A virtual teaching platform that can interact with people has been established using the virtual technology of 3DSMAX. The functions of the simulation device were implemented through VRML programming, and the triggering, data transmission, and page conversion of the demonstration program were implemented based on the response function. Thus, the design of a teaching system based on virtual reality has been achieved. Practice has proven that this method has better human-computer interaction effects than traditional English remote multimedia teaching, and has increased students' learning enthusiasm.
Segmentation-Based Masked Sampling for text-to-animated image synthesis in disaster scenarios

Generative AI has demonstrated significant capabilities in text-to-video synthesis, using advanced models characterized by extensive parameters. Given that current disaster alert services are mostly text-based and can be less accessible to many, this paper proposes a new approach. We aim to provide animated disaster images that precisely mirrors text prompts, thereby enhancing the efficiency and accessibility of disaster alerts. Our methodology combines the strengths of segmentation models and pre-trained Vision Transformer (ViT) mechanisms. By using a unique image selection based on CLIPScore and processing it with the CLIPSeg segmentation model, we generate an animated representation of disaster scenarios. This offers a simple, fast, and effective solution to the challenges of the current disaster alert systems.
Low-light Image Enhancement in Complex Outdoor Power Operation Environments

Nighttime power operation environments pose challenges to safety production monitoring due to issues such as backlighting, dim and uneven lighting and metal reflection. This leads to failure of experts and visual based safety monitoring methods. On the other hand, while there are researches on illumination enhancement algorithms based on Retinex theory and deep learning, most of them are conducted and evaluated on simple environment and general tasks, such as face and nighttime pedestrian detection. Aiming to address the image quality issues in nighttime power operation environments, this paper collects data from real substation and explores the performance of state-of-the-art low-light image enhancement algorithms under real scenario in recent years, and conducts quantitative analyses on peak signal-to-noise ratio and structural similarity.
Security Evaluating Security of Insurance Agency Portal: An ISO/IEC 25023 Quality Model Approach

Information systems play a pivotal role in business operations, especially for insurance agency portals that facilitate interactions between agencies and companies, providing services like registration, training, and access to essential reports. This study delves into assessing the quality of these portals by employing the internationally recognized SQuaRE series standards, concentrating specifically on security aspects as measured by ISO/IEC 25023. The choice of ISO/IEC 25023 is strategic, given its comprehensive approach to security metrics and its widespread acceptance, ensuring a thorough security assessment. ISO/IEC 25023 testing is divided into several stages: defining security characteristics, defining attributes measurement and measuring every metric name based on sub criteria. The study evaluates and analyzes based on the results measurements and provide recommendations. The agency portal website must improve integrity and non-repudiation that have low scores, currently compromised by inadequate array access validation and the absence of digital signatures. based on the recommendations provided to achieve a high- quality system
UPSNet: A Unified Panoptic Segmentation Network

In this paper, we propose a unified panoptic segmentation network (UPSNet) for tackling the newly proposed panoptic segmentation task. On top of a single backbone residual network, we first design a deformable convolution based semantic segmentation head and a Mask R-CNN style instance segmentation head which solve these two subtasks simultaneously. More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification. It first leverages the logits from the previous two heads and then innovatively expands the representation for enabling prediction of an extra unknown class which helps better resolving the conflicts between semantic and instance segmentation. Besides, it handles the challenge caused by the varying number of instances and permits back propagation to the bottom modules in an end-to-end manner. Extensive experimental results on Cityscapes, COCO and our internal dataset demonstrate that our UPSNet achieves state-of-the-art performance with much faster inference. Code has been made available at: https://github.com/uber-research/UPSNet.
Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud

Monocular 3D scene understanding tasks, such as object size estimation, heading angle estimation and 3D localization, is challenging. Successful modern-day methods for 3D scene understanding require the use of a 3D sensor. On the other hand, single image-based methods have significantly worse performance. In this work, we aim at bridging the performance gap between 3D sensing and 2D sensing for 3D object detection by enhancing LiDAR-based algorithms to work with single image input. Specifically, we perform monocular depth estimation and lift the input image to a point cloud representation, which we call pseudo-LiDAR point cloud. Then we can train a LiDAR-based 3D detection network with our pseudo-LiDAR end-to-end. Following the pipeline of two-stage 3D detection algorithms, we detect 2D object proposals in the input image and extract a point cloud frustum from the pseudo-LiDAR for each proposal. Then an oriented 3D bounding box is detected for each frustum. To handle the large amount of noise in the pseudo-LiDAR, we propose two innovations: (1) use a 2D-3D bounding box consistency constraint, adjusting the predicted 3D bounding box to have a high overlap with its corresponding 2D proposal after projecting onto the image; (2) use the instance mask instead of the bounding box as the representation of 2D proposals, in order to reduce the number of points not belonging to the object in the point cloud frustum. Through our evaluation on the KITTI benchmark, we achieve the top-ranked performance on both bird's eye view and 3D object detection among all monocular methods, effectively quadrupling the performance over previous state-of-the-art. Our code is available at https://github.com/xinshuoweng/Mono3D_PLiDAR.