Reviewerly/exHarmony huggingface dataset:
Analisis Tingkat Kerawanan Bencana Banjir di Kota Bandar Lampung Berbasis GIS (Geographic Information System) dan Citra Landsat 8 Oli

Floods in the city of Bandar Lampung are events that occur every year. This has a detrimental impact on humans and the environment, especially during the rainy season where floods often occur. To reduce the impact of human and environmental losses due to floods, this can be done through mitigating flood- prone areas by mapping flood-prone areas using the GIS 10.3 application with the overlay method by combining the parameters used and weighting each parameter to be produce a flood hazard map. This study aims to determine areas that have high flood potential, the main factors causing flooding and to determine the flood vulnerability class in each area of Bandar Lampung City. The parameters used in this study are Land Form, Rainfall, Soil Type, Land Slope, Land Altitude and Building Density. The analysis technique was carried out by giving weights to each environmental parameter and overlay techniques (tumpeng stacking maps) using the GIS application, analysis of building density levels using Landsat 8 image interpretation OLI Band 5 and Band 6. The results showed that the main factors causing flooding in Bandar City Lampung, namely high and evenly distributed rainfall in the city of Bandar Lampung reaching 3000 mm/year. Districts with a very high level of flood vulnerability are in the Bumi Waras District, covering an area of 13% of the administrative area of the District. Then areas that have a high level of flood vulnerability include Panjang 9% and East Betung Bay 4% of the area.North Betung Bay 9% and East Betung Bay 3.17% of the area. Keywords: ArcGis, Flood, Vulnerability, Weighting.
Training on Using the "Decision Analysis" Module in the POM-QM for Windows Software in Carrying Out Decision Analysis for Final Decision Making

Business professionals often face difficulties in their work or business so they can quickly offer the best solutions when making decisions. Decision analysis is an important process in making effective and efficient decisions in various contexts, both in personal life and in the world of business and organizations. In decision analysis, several aspects that need to be considered include identifying problems or opportunities, collecting relevant data, situation modeling, risk assessment, determining alternatives, and selecting and implementing decisions. POM-QM for Windows is a program used to solve quantitative management problems in the operations and production domains. With POM QM for Windows training, participants are able to solve complex Decision Analysis problems quickly and precisely.
Promoting the Digital Transformation of The Payment Industry Based on Blockchain Technology

In the era of digital economy, the digital transformation of the payment industry has become a general trend. However, digital transformation can bring development opportunities to the payment industry, but it also faces some challenges. Blockchain technology is regarded as the key core technology to support the future development and application of the Internet. The development of blockchain has become the focus of financial technology. Its decentralization, simple process, openness, transparency, and non-tampering characteristics can effectively solve traditional payment problems. problems existing in. It is precisely because of the characteristics of decentralization that the payment method is more information symmetric and transparent, which greatly improves the security of the transaction process. Commercial banks use the blockchain consensus mechanism to ensure that the data of the platform, upstream and downstream, and other participants reach a consensus across the entire network to ensure information symmetry and transparency; the secure interaction of information is achieved through encryption algorithms, which solves the possibility of information being tampered with; blocks The timestamp feature of the chain ensures the integrity of historical data. This topic is based on the research on the role of blockchain technology in the digital transformation of the payment industry. It is mainly used in the following three aspects:1.This technology has the intelligent features of payment settlement and fund management. Ensure the safe interaction of transaction information by improving payment efficiency and the security of fund transactions. 2.This technology has the characteristics of high efficiency and low cost. It efficiently and low-costly obtains true and reliable data in the system, optimizes the credit creation mechanism, forms a new trade financing model, and effectively reduces financing risks. 3.This technology has safe and low-risk characteristics. Improve the level of data governance, improve the intelligence level of KYC, risk control strategies, and anti-money laundering, and reduce the risks of both banks and the industrial Internet.
The Transactional Leadership Style of University Leaders in The Teachers Job Satisfaction in Hunan Province: Basis for Managers Leadership Program

This research study aims to determine the transactional leadership style of university leaders and the teachers’ job satisfaction in Hunan Province which could be use as basis for managers leadership program. Chinese school leaders display distinctive ways in handling complex environment. The Chinese government issued reforms to improve higher education as an international player linked to employing educational leadership. Hence, when school leaders argued that teachers who work together affect policies and practices in several manner. The present study aims to provide insights on the transactional leadership style of school leaders that enhances the job satisfaction among teachers making the educational complex environment a collaborative one. This research study entitled “The transactional leadership style of university leaders in the teachers job satisfaction in Hunan Province: Basis for managers leadership program” will focus primarily on determining the transactional leadership style of university leaders and the teachers’ job satisfaction on such style exhibited which could be use as basis for managers leadership program. Basically, the indicators will be limited to the following interwoven and non-hierarchical facets of teaching expertise teaching and supporting learning, professional learning and development, mentorship, research, scholarship and inquiry, and educational leadership. In addition, the study will also identify transactional leadership style exhibited by university leaders to justify the need of the present study that would help the researcher design action study to enhance the leadership styles towards achieving high job satisfaction.
Leveraging Unlabeled Clinical Data to Boost Performance of Risk Stratification Models for Suspected Acute Coronary Syndrome.

The performance of deep learning models in the health domain is desperately limited by the scarcity of labeled data, especially for specific clinical-domain tasks. Conversely, there are vastly available clinical unlabeled data waiting to be exploited to improve deep learning models where their training labeled data are limited. This paper investigates the use of task-specific unlabeled data to boost the performance of classification models for the risk stratification of suspected acute coronary syndrome. By leveraging large numbers of unlabeled clinical notes in task-adaptive language model pretraining, valuable prior task-specific knowledge can be attained. Based on such pretrained models, task-specific fine-tuning with limited labeled data produces better performances. Extensive experiments demonstrate that the pretrained task-specific language models using task-specific unlabeled data can significantly improve the performance of the downstream models for specific classification tasks.
CORE: Co-planarity Regularized Monocular Geometry Estimation with Weak Supervision

The ill-posed nature of monocular 3D geometry (depth map and surface normals) estimation makes it rely mostly on data-driven approaches such as Deep Neural Networks (DNN). However, data acquisition of surface normals, especially the reliable normals, is acknowledged difficult. Commonly, reconstruction of surface normals with high quality is heuristic and time-consuming. Such fact urges methodologies to minimize dependency on ground-truth normals when predicting 3D geometry. In this work, we devise CO-planarity REgularized (CORE) loss functions and Structure-Aware Normal Estimator (SANE). Without involving any knowledge of ground-truth normals, these two designs enable pixel-wise 3D geometry estimation weakly supervised by only ground-truth depth map. For CORE loss functions, the key idea is to exploit locally linear depth-normal orthogonality under spherical coordinates as pixel-level constraints, and utilize our designed Adaptive Polar Regularization (APR) to resolve underlying numerical degeneracies. Meanwhile, SANE easily establishes multi-task learning with CORE loss functions on both depth and surface normal estimation, leading to the whole performance leap. Extensive experiments present the effectiveness of our method on various DNN architectures and data benchmarks. The experimental results demonstrate that our depth estimation achieves the state-of-the-art performance across all metrics on indoor scenes and comparable performance on outdoor scenes. In addition, our surface normal estimation is overall superior.
MULLER: Multilayer Laplacian Resizer for Vision

Image resizing operation is a fundamental preprocessing module in modern computer vision. Throughout the deep learning revolution, researchers have overlooked the potential of alternative resizing methods beyond the commonly used resizers that are readily available, such as nearest-neighbors, bilinear, and bicubic. The key question of our interest is whether the front-end resizer affects the performance of deep vision models? In this paper, we present an extremely lightweight multilayer Laplacian resizer with only a handful of trainable parameters, dubbed MULLER resizer. MULLER has a bandpass nature in that it learns to boost details in certain frequency subbands that benefit the downstream recognition models. We show that MULLER can be easily plugged into various training pipelines, and it effectively boosts the performance of the underlying vision task with little to no extra cost. Specifically, we select a state-of-the-art vision Transformer, MaxViT [50], as the baseline, and show that, if trained with MULLER, MaxViT gains up to 0.6% top-1 accuracy, and meanwhile enjoys 36% inference cost saving to achieve similar top-1 accuracy on ImageNet-1k, as compared to the standard training scheme. Notably, MULLER’s performance also scales with model size and training data size such as ImageNet-21k and JFT, and it is widely applicable to multiple vision tasks, including image classification, object detection and segmentation, as well as image quality assessment. The code is available at https://github.com/google-research/google-research/tree/master/muller.
PODIA-3D: Domain Adaptation of 3D Generative Model Across Large Domain Gap Using Pose-Preserved Text-to-Image Diffusion

Recently, significant advancements have been made in 3D generative models, however training these models across diverse domains is challenging and requires an huge amount of training data and knowledge of pose distribution. Text-guided domain adaptation methods have allowed the generator to be adapted to the target domains using text prompts, thereby obviating the need for assembling numerous data. Recently, DATID-3D presents impressive quality of samples in text-guided domain, preserving diversity in text by leveraging text-to-image diffusion. However, adapting 3D generators to domains with significant domain gaps from the source domain still remains challenging due to issues in current text-to-image diffusion models as following: 1) shape-pose trade-off in diffusion-based translation, 2) pose bias, and 3) instance bias in the target domain, resulting in inferior 3D shapes, low text-image correspondence, and low intra-domain diversity in the generated samples. To address these issues, we propose a novel pipeline called PODIA-3D, which uses pose-preserved text-to-image diffusion-based domain adaptation for 3D generative models. We construct a pose-preserved text-to-image diffusion model that allows the use of extremely high-level noise for significant domain changes. We also propose specialized-to-general sampling strategies to improve the details of the generated samples. Moreover, to overcome the instance bias, we introduce a text-guided debiasing method that improves intra-domain diversity. Consequently, our method successfully adapts 3D generators across significant domain gaps. Our qualitative results and user study demonstrate that our approach outperforms existing 3D text-guided domain adaptation methods in terms of text-image correspondence, realism, diversity of rendered images, and sense of depth of 3D shapes in the generated samples.
SQAD: Automatic Smartphone Camera Quality Assessment and Benchmarking

Smartphone photography is becoming increasingly popular, but fitting high-performing camera systems within the given space limitations remains a challenge for manufacturers. As a result, powerful mobile camera systems are in high demand. Despite recent progress in computer vision, camera system quality assessment remains a tedious and manual process. In this paper, we present the Smartphone Camera Quality Assessment Dataset (SQAD), which includes natural images captured by 29 devices. SQAD defines camera system quality based on six widely accepted criteria: resolution, color accuracy, noise level, dynamic range, Point Spread Function, and aliasing. Built on thorough examinations in a controlled laboratory environment, SQAD provides objective metrics for quality assessment, overcoming previous subjective opinion scores. Moreover, we introduce the task of automatic camera quality assessment and train deep learning-based models on the collected data to perform a precise quality prediction for arbitrary photos. The dataset, codes and pre-trained models are released at https://github.com/aiff22/SQAD.
Tracking by Natural Language Specification with Long Short-term Context Decoupling

The main challenge of Tracking by Natural Language Specification (TNL) is to predict the movement of the target object by giving two heterogeneous information, e.g., one is the static description of the main characteristics of a video contained in the textual query, i.e., long-term context; the other one is an image patch containing the object and its surroundings cropped from the current frame, i.e., the search area. Currently, most methods still struggle with the rationality of using those two information and simply fusing the two. However, the linguistic information contained in the textual query and the visual representation stored in the search area may sometimes be inconsistent, in which case the direct fusion of the two may lead to conflicts. To address this problem, we propose DecoupleTNL, introducing a video clip containing short-term context information into the framework of TNL and exploring a proper way to reduce the impact when visual representation is inconsistent with linguistic information. Concretely, we design two jointly optimized tasks, i.e., short-term context-matching and long-term context-perceiving. The context-matching task aims to gather the dynamic short-term context information in a period, while the context-perceiving task tends to extract the static long-term context information. After that, we design a long short-term modulation module to integrate both context information for accurate tracking. Extensive experiments have been conducted on three tracking benchmark datasets to demonstrate the superiority of DecoupleTNL.
Social Media Sentiment Analysis to Understand Agricultural Market Trends and Consumer Preferences

Social media has become a very important communication platform in the daily life of modern society. People use these platforms to share their experiences, opinions, and preferences regarding various topics, including agricultural and food products. This research aims to conduct social media sentiment analysis to understand agricultural market trends and consumer preferences. This research is a literature review that adopts a qualitative method approach, which means it will analyse and interpret data by relying on information and text from various sources. The study results show that social media sentiment analysis is an invaluable tool for agricultural industry players in understanding market trends and consumer preferences. Using natural language processing and machine learning techniques, this analysis enables real-time monitoring of consumer opinions and feelings regarding agricultural products and agriculture-related issues.
The Physics of Electric Saroj Veena: A Folk Musical Instrument from India's North Eastern Region

Saroj Veena is known as a famous Folk musical Instrument in the North eastern part of India. We will discuss about the Shape and Size, Structure, body mechanism of Saroj Veena. In our lab studies, we look at the harmonic structure of standing waves in Saroj Veena strings. Using a digital oscilloscope with a Fast Fourier transform and a magnetic pickup from a Saroj Veena, the experimental data was gathered. Depending on where the string is plucked, the harmonic amplitudes in the measured signal vary, giving the sound a distinct timbre. The relative amplitudes of transverse standing waves in a string were determined from experimental data and predicted by the wave equation when the boundary and beginning circumstances matched the initial shape of the string.
THE HISTORY OF THE DEVELOPMENT OF THE GAMIFICATION METHOD AND THE POSSIBILITY OF ITS APPLICATION IN THE EDUCATIONAL PROCESS

In this article are considered the main stages of the development of the theory of gamification, and reveals the possibilities of using its methods in educational processes, using the example of author's games that can be used in the teaching of the discipline «Philosophy».
Digital mentoring school "Pedagogical Supervision"

In today's constantly evolving digital space, it is necessary to prepare yourself with the necessary tools to achieve high-quality results in education. Mentorship guarantees a comprehensive approach to the development of each teacher. Despite the fact that mentoring system is not a new phenomenon in the education system, it has become more relevant in the context of the modern state educational policy. It is necessary to improve this system. In this regard, the relevance and importance of scientific and methodological digital support for teachers is increasing. The presented project focuses on the organizational and methodological model of the introduction of mentorship as an innovative activity. In search of an effective mechanism, we have created a website that allows schools and teachers the fullest access to the resources for their development and improvement of the educational quality with following dissemination of the experience.
ENHANCED CRYPTOGRAPHIC SECURITY OF AGGREGATED DIGITAL SIGNATURES THROUGH UTILIZATION OF A UNIFIED AUTHENTICATION FRAMEWORK

The significance of this research lies in safeguarding user information and data against unauthorized alterations and destruction. Throughout the study, key aspects were explored, including user identification and authen-tication methods, cryptographic authentication protocols, digital signature properties, and strategies to enhance the cryptographic robustness of digital signatures. These strategies involved scrutinizing authentication methods using the Saati technique and the RSA algorithm. The analysis revealed that the attributes employed for digital signature recognition are highly resistant to replication, and the verification process is notably efficient. The height-ened cryptographic resilience of the electronic signature achieved through the RSA algorithm stems from the incorporation of a public key certificate. Consequently, a larger encryption key size corresponds to heightened sys-tem reliability and electronic digital signature security. While the utiliza-tion of the RSA algorithm results in encrypted text that is approximately 10 times larger than the original, its stability is comparatively increased.
Quantum chain of things: 'the quantum triad - integrating blockchain, IoT and quantum computing for a transcendent future'

The Internet of Things (IoT), which is building a new world where everything is interconnected and can be remotely operated, is one of the developing technologies. The epoch of machines conquering the universe is just getting warmed up. The next technological innovation is blockchain, which uses a peer-to-peer communication link to preserve general transactional data and information (or blocks) across several repositories (or chains). The terminology "Digital Ledger" alludes to an instance of block storage. Quantum computing is the next quickly developing technology. It incorporates the use of numerous quantum physics aspects, notably superposition and entanglement. Every foundational premise of the computational game is amended by quantum excellence. A new paradigm dubbed Quantum - Chain of Things (QCoT), which is the most potent technology from the sub-emerging technologies is presented when we scrutinize the collaboration of the three powerful advanced. The BC-IOT (Blockchain coupled with IOT leveraging Arduino) and QC-IOT (Quantum computing merged with IOT using IBM Qiskit) subsystems are the cornerstones of QCoT technology. The prevailing breakthroughs in blockchain, quantum computing, and IOT will be addressed in this paper, followed by an exploration of the recommended architectural model for the improved technology.
Performance analysis of CNN models for evaluating Biglycan as a biomarker in predicting breast cancer

Breast cancer is found in the present generation at a higher level. Since the middle of the 20th century numerous unfavorable changes have been made to food, our way of life, obesity, the situation, and our microbiomes. Because of these variations, people have become more susceptible to cancer at an earlier age. The considered dataset is images of breast cancer-positive and cancer-negative histological sections utilizing the Biglycan biomarker. The dataset and models created from it can be used by pathologists and other healthcare practitioners to address probable disorders. Three widely used CNN models were examined for performance, and we discovered that MobileNet-V2 outperformed them all. Transfer learning-based breast cancer classification with the Biglycan biomarker dataset helps in tracking at the earlier stages. Additionally, our model outperformed earlier research using the same dataset in terms of accuracy. Our ability to create high-performance devices with lower power consumption and cheaper costs is made possible by the MobileNet-V2&apos;s ability to achieve greater precision while using a lighter model. In comparison with the existing work using LeNet, MobileNet-V2 outperforms.