Reviewerly/exHarmony huggingface dataset:
A Graph-Transformer Network for Scene Text Detection

Detecting text in natural images with varying orientations and shapes is challenging. Existing detectors often fail with text instances having extreme aspect ratios. This paper introduces GTNet, a Graph- Transformer network for scene text detection. GTNet uses a Graph-based Shared Feature Learning Module (GSFL) for feature extraction and a Transformer-based Regression Module (TRM) for bounding box prediction. Our architecture offers a flexible receptive field, combining global attention and local features for enhanced text representation. Extensive experiments show our method surpasses existing detectors in accuracy and effectiveness.
MBDNet: Mitigating the “Under-Training Issue” in Dual-Encoder Model for RGB-d Salient Object Detection

Existing RGB-D salient object detection methods generally rely on the dual-encoder structure for RGB and depth feature extraction. However, we observe that the encoders in such models are often not adequately trained to obtain superior feature representations. We name this problem the “under-training issue”. To this end, we propose a multi-branch decoding network (MBDNet) to suppress this issue. The MBDNet introduces additional decoding branches with supervision to form a multi-branch decoding (MBD) structure, facilitating the training of the encoders and enhancing the feature representation. Specifically, to ensure the effectiveness of the introduced supervision and improve the performance of additional decoding branches, we design an adaptive multi-scale decoding (AMSD) module. We also design a multi-branch feature aggregation (MBFA) module to aggregate the multi-branch features in MBD to further improve the detection accuracy. In addition, we design an enhancement complement fusion (ECF) module to achieve multi-modality feature fusion. Extensive experiments demonstrate that our MBDNet outperforms other state-of-the-art methods and mitigates the “under-training issue”.
A Lightweight Detail-Fusion Progressive Network for Image Deraining

The performance of image deraining algorithms has been significantly improved by introducing deep learning-based methods. However, their network structures have become more complicated and diverse, making it difficult to strike a balance between rain removal performance and processing speed. To address the aforementioned issues, we innovatively propose the Lightweight Detail-fusion Progressive Network (LDPNet) for image deraining, which can obtain more detailed rain-free images with fewer parameters and faster running speed. First, we decompose the challenging deraining task into multi-stage subtasks that gradually recover the degraded images. An effective combination of dense connections and the Gate Recurrent Unit allows our network to not only reuse features within each stage but also to transfer information between stages. This structure can achieve good performance with fewer parameters. Second, we design a multi-scale detail extraction block for recovering and reconstructing image details, which enhances the processing of detailed information by obtaining features with different receptive fields. Its lightweight design is achieved based on a depth-separable structure. Furthermore, we integrate a lightweight coordinate attention mechanism to achieve precise localization perception of detailed information in the rain removal region, which effectively improves the rain removal effect. The experimental results of the comprehensive datasets demonstrate the great superiority of our algorithm.
GNAT: Leveraging Weighted Negative Sampling for Improved Graph Attention Network Performance

Graph Neural Networks (GNNs) have been considered effective tools for graph representation learning, among which Graph Attention Networks (GAT) has gained much attention since GAT assigns different weights to different nodes to generate a new node representation. In most of GNNs including GAT, the first-order adjacent nodes are only used to message passing, which can be considered as positive samples. However, many non-adjacent nodes, which can be seen as negative samples, draw less attention in graph learning. Although the current works have proposed several methods for negative sampling, they treat all negative samples to have the same weights when learning nodes' representation, which limits the learning ability of the model and could introduce irrelevant information. In this paper, we distinguish the importance of different negative samples by giving samples variant weights through the attention mechanism and proposed Graph Negative enhanced Attention Network (GNAT). Specifically, we first select appropriate negative samples for each node. Then, we utilize the multi-head attention mechanism to let negative samples have different weights. In this way, when learning node representations, GNAT can discriminate the significance of negative samples and reduce the influence of irrelevant information. Experimental evaluations show that appropriated negative samples can enhance the overall performance of the GAT model and GNAT obtained outstanding performance compared with SOTA methods.
MSAM: Cross-Domain Recommendation Based on Multi-Layer Self-Attentive Mechanism

In recent years, recommendation systems have been extensively implemented across multiple platforms. It can extract useful information from vast amounts of data and recommend appropriate products to users based on their preferences. Typically, recommendation systems are plagued by data scarcity and chilly start issues, which is a serious challenge. To solve these problems, cross-domain recommendations (CDR) have received a lot of attention. Typically, CDR aims to leverage data from other domains or map user preferences from other domains to the target domain to improve recommendation quality and alleviate data sparsity and chilly start problems for new services. However, the majority of existing cross-domain recommendation methods are based on matrix decomposition, which can only learn shallow linear features and cannot adequately address the challenges associated with cross-domain recommendation. Therefore, this paper proposes a multi-layer self-attentive mechanism (MSAM) cross-domain recommendation method to make more accurate predictions by fusing and passing information between different domains. The framework is composed primarily of a feature extraction layer, a multilayer perceptron, and a feature fusion layer that discovers the potential factors of users and items and fuses the potential factors of users from various domains. In addition, we employ the Wasserstein self-attentive mechanism and the multi-headed self-attentive mechanism in the feature extraction layer and the feature fusion layer, respectively, to better extract key user features and learn the affinity of user potential factors across domains. we conducted multiple experimental validations on two actual datasets to demonstrate the model's efficacy and superiority.
Knowledge Graph Construction for Supply Chain Management in Manufacturing Industry

Knowledge graph technology is crucial in enhancing supply chain management (SCM) in the manufacturing industry. However, the existing SCM ontology knowledge suffers from coarse granularity, leading to reduced accuracy of knowledge extraction and making knowledge graph construction more challenging. A novel construction method for the SCM event logic knowledge graph (ELKG) is proposed to overcome these challenges. The proposed method includes constructing an event logic ontology and annotating the SCM dataset based on the ontology. Meanwhile, a knowledge joint extraction model FIBGN based on Bidirectional Graph Convolutional Network (BiGCN) and feature interaction between different spaces (BiGCN) is proposed. Experimental results show that this method can improve the effect of event argument entity and relation joint extraction and is better than other methods. Finally, the event logic knowledge graph of supply chain management in large-scale manufacturing fields is established, which provides decision support for the supply chain system.
Breaking the Chains: Navigating the Trade-off between Decentralization and Governance in Blockchain Technology

This research paper critically examines the limitations of blockchain technology with a focus on the need for centralization and governance.Regardless of its progressive potential, blockchain faces provokes in accomplishing total decentralization because of issues of versatility, agreement systems, and asset concentrated processes.Also, the shortfall of unified power raises concerns with respect to administration, as direction becomes intricate, and clashes emerge.Through a complete examination of existing writing and contextual analyses, this paper reveals insight into the compromises among decentralization and reasonableness, underscoring the basic of consolidating smart administration designs to streamline blockchain's effectiveness, security, and reception in different spaces.The research paper examines technical, legal, and social aspects to offer valuable insights for policymakers, industry stakeholders, and researchers to effectively address these challenges.The objective is to foster responsible and sustainable blockchain implementations, ensuring enhanced network efficiency, security, and transparency across diverse sectors.Numerous researchers have been concentrating on this and recommend Semi centralization and government support as an answer and have given their own evaluations; this paper fundamentally talks about, and surveys crafted by 5 articles, The Limits to Blockchain? Scaling vs. Decentralization [1] by Usman W. Chohan; Blockchain demystified: a technical and legal introduction to distributed and centralized ledgers [2] by Bacon,
Online Property and Land Market with Google Maps Integration

An innovative QR code-based attendance management system for student council events, aimed at improving student engagement and streamlining the attendance process. By generating a unique QR code linked to the student ID, the system allows students to register easily by scanning their code with a mobile device. Data is instantly recorded in a centralized database, providing real-time attendance tracking and valuable information for event planning and analysis. Implementing this technology-based solution provides an efficient and convenient way to manage attendance and encourages students to participate more in school events organized by the Student Council
TravelSnap Powered Vue and Laravel Technology

TravelSnap is an innovative mobile app that enhances travelers' experience by allowing them to capture and store geotagged photos during their travels. Developed using the Rapid Application Development (RAD) approach, TravelSnap uses Vue.js for the frontend and Laravel for the backend, giving users an interactive platform for visualizations their travel routes on Google Maps with photo markers. This app not only preserves precious memories but also fosters a vibrant travel community where users can share their adventures and inspire others to discover destinations
Crimeline: Empowering Public Safety with GIS-Based Crime Incident Reporting

"Crimeline" is an innovative mobile application that revolutionizes the reporting and analysis of crime incidents thanks to a geographic information system (GIS) technology. This app allows citizens to report crimes in real time and facilitates data-driven decision making for law enforcement. By visualizing crime incidents on an interactive map, "Crimeline" empowers citizens and law enforcement to work together to combat criminal activity and improve public safety. This article presents the evolution and impact of the "Criminal Line", showing how its geographic information system (GIS)-based approach is changing the way crime incidents are managed for organizations community is safer
Design of Spherical Hopping Terrain Surveillance Robot

This paper deals with the design of rolling, steering and jumping mechanisms of a spherical surveillance robot that can be operated in hilly and rough terrain regions. The driving mechanism of the robot is designed such that the robot resembles the movement of a spherical ball. The steering mechanism is designed to enable the robot to achieve on spot turning. The principle behind the design of steering is the conservation of angular momentum. The jumping mechanism adds a special advantage to the locomotion of ground robots, in terms of the ability to avoid obstacles. The parameters considered for the selection of optimum mechanism design for the robot are also presented in this paper.
Design of Smart Glove for Sign Language Interpretation Using NLP and RNN

The sign language interpreter aims to build a communication bridge between a speech- and hearing-impaired person and an able person. It has been a challenging task to understand sign language which uses finger shapes and hand movements. There is a need to build an interpreter to convey the ideas of deaf and mute to able people. The proposed system consists of a pair of wearable gloves with sensors embedded in them, which convert the hand gestures to text and speech. It uses technologies like natural language processing (NLP) and recurrent neural networks (RNN) for processing signals. The hand glove consists of sensors that track the movement of fingers and hands. The sensed values are sent to the processing unit using a wireless module. The dataset is prepared using simulation software. The machine learning model is built based on the training set, to help in identifying the hand gesture. A mobile phone application runs this machine learning model. The phone processes the data received from the glove using the proposed model to identify the letter or word. These words and letters are interpreted as a sentence using NLP. The speaker outputs the natural voice from the mobile phone.
Incorporating Six Sigma in e-Learning Platform During COVID-19 Pandemic

The COVID-19 pandemic has caused various changes in the routine lives of people all across the world. In terms of education, the pandemic resulted in the closure of educational institutions. Because students were unable to attend courses and lectures in person, the institutions resorted to using the Internet to provide classes in order to continue their education. Currently the Six Sigma implementation in this whole system might help to analyze the quality of the education which is being delivered through online mode. The data collection through surveys helps to find shortcomings and the advantages. The methodology used in this paper is DMAIC which undergoes define, measure, analyze, improve and control phases in which the data collected will be analyzed using critical to quality tree (CTQ), bar charts, cause and effect diagram, weighted score matrices, and statistical testing to control the process. From this review paper the quality of the online education and statistical data of the students and faculty undergoing or conducting the online classes are consolidated and discussed. The data collected can be used in the educational research domain if one intends to make research on online education and this can be used to assess the quality of education.
Implementasi Data Mining Menggunakan Algoritma Fp-Growth Untuk Menganalisa Transaksi Penjualan Ekspor Online

In today's online technology, we have to learn various things so that we can adjust to social behavior, for example, buying and selling activities that cover all walks of life, now everyone doesn't hesitate to shop online. The online market is growing fast, especially in Indonesia. Anyone can sell and buy goods easily through online stores, so that people's purchasing power and sales increase due to the proliferation of online stores, XYZ stores are still beginners in terms of sales strategies so they try all efforts without careful preparation, one of the ways to do this is to expand its market coverage even to export goods abroad. the process of exporting goods in the current era is not a difficult thing to do, but it requires knowledge and preparation of a good strategy. from existing sales data can be analyzed to determine the right strategy in marketing goods. Data Mining is a science that helps analyze large amounts of data with the aim of gaining new knowledge that can be utilized. In Data Mining Analysis there is the FP-Growth algorithm which is a data mining technique for finding association rules from lots of data. This is known as the Association rule, where the rule will be determined from the minimum support and confidence results. the rules that are formed produce 10 rules out of 51 existing data, these rules help determine the export sales strategy at the XYZ store.
Prediction of Corona Virus Deaths Based on Big Data Analysis - Focusing on the Change Point Detection and the Forecast Combination method -

As the spread of the corona virus slows down, this study predicts the corona virus deaths in Korea using various prediction methods.While the previous studies in this domain mainly focused on predicting the corona virus confirmed cases using autoregressive models and deep learning based timeseries models, this study aims at the accurate prediction of the corona virus deaths using various machine learning methods with the above prediction methods.In particular, this study facilitated the prediction stability by adopting change-point detection and forecasting combination method.As explanatory variables, this study used various corona virus-related data, social distancing level and daily temperature, and it also showed the comparison of the prediction results of various prediction methods and two forecasting combination methods.The prediction performances of the various prediction methods differed depending on the prediction interval segmented by change-point detection.But it was found that the forecast combination methods mainly showed more accurate prediction results compared to other methods.In addition, the forecast combination methods showed the low volatility of the prediction error implying their stability of prediction.This study has significance in that it accurately predicted the corona virus deaths and achieved the accuracy and stability of the prediction, facilitating the practicality in the real application cases.
Research Highlight Generation with ELMo Contextual Embeddings

With the advent of digital publishing and online databases, the volume of textual data generated by scientific research has increased exponentially. This makes it increasingly difficult for academics to keep up with new breakthroughs and synthesise important information for their own work. Abstracts have long been a standard feature of scientific papers, providing a concise summary of the paper's content and main findings. In recent years, some journals have begun to provide research highlights as an additional summary of the paper. The aim of this article is to create research highlights automatically by using various sections of a research paper as input. We employ a pointer-generator network with a coverage mechanism and pretrained ELMo contextual embeddings to generate the highlights. Our experiments shows that the proposed model outperforms several competitive models in the literature in terms of ROUGE, METEOR, BERTScore, and MoverScore metrics.
The effectiveness of AR e-module of flower structure material on biology students’ science literacy

The electronic-module (e-module) is one of the teaching materials needed so that the learning process can take place optimally. E-module owned by UM biology students generally do not use the latest technology. Augmented Reality (AR) is a form of technology where there is a process of integrating the virtual world with the real world. Not a few students experience difficulties in understanding the flower structure material. Student scientific literacy is still far from what is expected. The research conducted is Research and Development with the Lee and Owens Development Model. Data collection was carried out during October-December 2022. The technique of analyzing quantitative descriptive data. The subjects in this study were 37 biology students class of 2021 without any differences in gender, age and study program. The results of the effectiveness of the AR electronic-module based on n-gain calculations obtained a score of 0.7 where on that score the AR e-module was categorized as quite effective in increasing scientific literacy. It is hoped that in the future there will be teaching materials and media that can improve some of the competencies needed by the latest generation.
A high‐efficient information extraction mechanism based on complex convolution for wireless signal recognition

Abstract In the integration of terrestrial and non‐terrestrial networks, massive access to wireless signals poses a significant threat to communication systems. However, existing signal recognition models cannot accurately identify signals with low signal‐to‐noise ratios (SNRs). To alleviate this issue, this paper proposes a high‐efficient information extraction mechanism based on complex convolution. Specifically, complex convolution and complex max‐pooling operations have been integrated into a real‐value network to efficiently acquire anti‐noise information. The extracted features contain both in‐phase and quadrature (IQ) structure information and complex relationship information. Experiments on the public RML2016.04C dataset indicated that the model exhibits rapid convergence and superior noise‐robustness under low SNRs. When SNR =0 dB, the model outperforms the suboptimal model by 4.05%. Furthermore, our model demonstrates excellent generalization performance at high SNRs.
Design and Development of Attendance Management System using ReactJS

The study focused on creating a user-friendly attendance tracking solution. Utilizing the ReactJS library, the system aims to automate attendance management for educational institutions, organizations, or events. The study involves evaluating existing attendance systems, identifying areas for improvement, and adopting an Agile development approach with continuous feedback from stakeholders. The system's design encompasses secure data storage, a responsive user interface, and seamless integration of attendance capture methods. Usability testing and user feedback play a crucial role in refining the system, resulting in an efficient and adaptable solution. The project promises to streamline attendance tracking, enhance user management, and generate comprehensive attendance reports for improved efficiency and accuracy
Automated Medical Diagnosis with Deep Learning

Medical diagnosis is of utmost importance in healthcare, ensuring prompt and accurate disease identification for effective patient care. However, conventional diagnostic approaches relying on manual examination are time-consuming and susceptible to human errors. In recent times, the integration of deep learning has revolutionized medical diagnosis by capitalizing on its capacity to comprehend intricate patterns from complex medical data. This research introduces an automated medical diagnosis system, employing advanced deep learning techniques. The system incorporates Convolutional Neural Networks (CNNs) for medical image analysis and Recurrent Neural Networks (RNNs) for processing patient data. Specifically, the CNN model scrutinizes medical images like X-rays and MRIs, proficiently identifying anomalies indicative of diverse conditions such as lung diseases, bone fractures, or tumors. Meanwhile, the RNN model delves into patient records and medical histories, offering valuable insights into disease progression and personalized treatment strategies. Key Words: Deep Learning, Artificial Intelligence ,Machine Learning, Medical Imaging, Radiology