Reviewerly/exHarmony huggingface dataset:
Application Research on Blockchain Technology in Accounting System

Blockchain technology is a product of the development of science and technology in the new era, which adapts to the quality requirements of accounting information with technological advantages such as decentralization, distributed storage, and data tampering resistance. Accounting is a basic work in enterprises. Applying the features of blockchain technology, such as decentralization, tamper proof, and trustworthiness, to enterprise accounting can effectively improve the efficiency of accounting, optimize the accuracy of financial data, and improve the operating efficiency of enterprises. The application of blockchain technology in the field of accounting can be explored from four aspects: accounting confirmation, accounting measurement, accounting records, and accounting reports. The advantages of blockchain technology will also affect the efficiency of enterprises in various aspects of accounting. We collected data from manufacturing listed companies that introduced blockchain technology in their accounting work in 2002 as samples and used multiple regression methods to reveal the actual effectiveness of blockchain technology in enterprise accounting. The regression results indicate that the application of blockchain technology in accounting work can have a positive impact on enterprises.
Categorical Boosting Machine for Tamil Character Recognition Using Shape Based Features

Tamil is one of the world's earliest surviving languages, from which modern Indian scripts draw inspiration. However, there has been limited development of Optical Character Recognition techniques for Tamil characters. This paper describes a model for Tamil vowels-based Optical Character Recognition. The strategy seeks to utilise a CHFR feature extraction technique to extract three distinct feature vectors from the contours of each individual character's centroid. These feature vectors are condensed into a single vector before being sent to the CatBoost classifier for classification. Using the Optuna framework, hyperparameter optimisation and comparative analysis of existing models are performed. The proposed scheme is applied to a dataset of 6,972 handwritten samples evenly divided into 12 classes representing the vowels of the Tamil alphabet. The primary evaluation metric is accuracy, and 84.36% testing accuracy is observed.
Construction of Simulation Environment and Design of Path Planning for Maze Robot

Aiming at environment modeling and path planning search of the maze robot, a method how to construct simulation environment and design path planning algorithm is presented in this paper. On the basis of WPF framework, Using XAML interface description language and C# to build simulation environment, a multi-functional open maze generation interface and maze solution and verification platform is provided. The user can intuitively design the maze grid map to simulate the maze solving process by animation. They also can easily add new and verify algorithms by inheriting or rewriting the abstract class provided. What’s more, based on the traditional maze algorithm breadth-first searching strategy and depth-first searching strategy, the shortest path planning algorithm using a combination of breadth and depth is brought forward for demonstration. And the final test result has shown the feasibility and effectiveness of this method.
Utilization of CNN and Ensemble Methods to Support Medical Practitioners in Categorizing Lung Cancer: A Research Paper

Detecting lung cancer early is critical for successful treatment, and it is essential to evaluate C. T. scans effectively. To address this, we have developed a platform that aims to improve the accuracy of lung cancer detection by combining the strengths of these two machine-learning techniques. The proposed system employs an XGBoost classifier to preprocess the data and extract important features, followed by a CNN model to classify the images. The web application interface allows users to upload C. T. scans, and the output is generated instantly, indicating the presence or absence of lung cancer. The proposed system achieved a high accuracy rate of 96 % on the dataset used, demonstrating its effectiveness in the early detection of lung cancer. The system has potential as a screening tool for lung cancer, allowing for early detection and timely treatment. The proposed approach offers a promising solution for more effective and efficient lung cancer detection.
Multi-Scale Occluded Pedestrian Detection Based on Deep Learning

With the intensification of urbanization, the number of pedestrians in densely populated areas is showing an explosive growth trend, which brings new challenges to the technological advancement of urban traffic management, intelligent security, and other fields. Pedestrian detection in densely populated areas is an important task aimed at achieving real-time recognition and tracking of all pedestrians in a specific area. This article proposes a multi-scale occluded pedestrian detection method based on deep learning. This method can effectively detect occluded pedestrians by introducing multi-scale feature maps and occlusion detection modules. The experimental results indicate that Faster RCNN has high accuracy in detecting occluded pedestrians and can be applied to pedestrian detection tasks in practical scenarios. Deep learning algorithms can be used to effectively measure the occlusion of pedestrians. Multi-scale detection methods allow for accurate detection of pedestrians at different scales. By using deep learning algorithms, patterns with masked pedestrian features can be automatically learned and judged during the detection process.
Apache Spark Big data Analysis, Performance Tuning, and Spark Application Optimization

Big data analysis, the processing of vast and diverse data, is crucial for enhancing business intelligence, predictive modeling, and quicker decision-making. It encompasses structured, unstructured, and semi-structured data, ranging from TBs to ZBs. Popular open-source tools like Apache Hadoop and Apache Spark are instrumental for managing this data deluge. Apache Spark is described as a superior environment in this article for handling huge data carefully and effectively. A few of the studies, technical achievements, and popular future development paths of Big Data Analysis utilizing Apache Spark are highlighted. Apache Spark can offer a better working environment than Apache Hadoop, however it cannot be argued that it is not widespread in Industry 4.0 such that it could outreach the Hadoop. The main factor is Hadoop's use of the simple, comprehensible, and straightforward MapReduce programming model. However, the users of Spark platform are well-pleased with the versatile processing capabilities. As Spark is mainly based on Resilient Distributed Data-sets and RDDs are outmoded now, there is a severe necessity of performance tuning that ensures flawless working from Spark and Spark based APIs. It is a need of the hour scenario where Spark applications should adopt advanced optimization techniques.
Detection of Nuclei for Breast Cancer Detection Using Histopathology Images

Breast cancer ranks as utmost prevalent form of cancer among women on a global scale, and a pathologist must spend a lot of time studying many images under various magnifications in order to provide an accurate and prompt diagnosis. Many researchers automate this procedure in order to provide a quicker and more reliable diagnosis of these malignancies. They do this by using computer vision, machine learning, and deep learning approaches. This paper is meant for the various techniques and algorithms used for the detection of cancerous nuclei. There are the variations in shape, size and appearance and texture in the nuclei of breast cancer in histopathology images, there is a need for the automated nuclei detection. And it is the difficult field in the computer aided pathology research. In this study, For the automated identification of the nuclei of breast cancer histopathology images, Mask RCNN (Region-Based Convolution Neural Network) method is used. Various methods like Feature Pyramid Networks(FPN), Fully Convolution Networks(FCN), ROIAlign and ResNet network are used by Mask RCNN. ROIAlign is used as it increases accuracy of the nuclei detection. FCN helps to predict the results in more detail. The study also reflects that Mask RCNN algorithm is more superior compared to the other nuclei detection algorithms.
Detecting Phishing URLs using the BERT Transformer Model

Phishing websites many a times look-alike to benign websites with the objective being to lure unsuspecting users to visit them. The visits at times may be driven through links in phishing emails, links from web pages as well as web search results. Although the precise motivations behind phishing websites may differ the common denominator lies in the fact that unsuspecting users are mostly required to take some action e.g., clicking on a desired Uniform Resource Locator (URL). To accurately identify phishing websites, the cybersecurity community has relied on a variety of approaches including blacklisting, heuristic techniques as well as content-based approaches among others. The identification techniques are every so often enhanced using an array of methods i.e., honeypots, features recognitions, manual reporting, web-crawlers among others. Nevertheless, a number of phishing websites still escape detection either because they are not blacklisted, are too recent or were incorrectly evaluated. It is therefore imperative to enhance solutions that could mitigate phishing websites threats. In this study, the effectiveness of the Bidirectional Encoder Representations from Transformers (BERT) is investigated as a possible tool for detecting phishing URLs. The experimental results detail that the BERT transformer model achieves acceptable prediction results without requiring advanced URLs feature selection techniques or the involvement of a domain specialist.
Design and Development of Cloud and IoT-Based Monitoring System for Environmental Health and Safety in Homes

Residential pollution is portrayed as a key contributor to health problems and environmental damage since it comes from indoor and outdoor sources. To discover a solution, a monitoring system should be built and capable of measuring the level of pollutants in typical residential environments. The suggested method makes use of cloud computing and the Internet of Things to measure pollution in residential areas. The goal is to monitor indoor air quality, gather data, and then communicate those findings with users in real time through cloud storage. An STM32 CPU, an ESP8266 wireless LAN module, a multisensory and hazardous substances are used in suggested system. According to the findings, the error rates for PM 2.5 and noise are 6-7% and 1%, respectively, while the error rate for readings of hazardous gases is 6%. The sensors are updated in 0.76 seconds, and an alarm results in a response of 2 seconds.
Cybersecurity in Public Space: Leveraging CNN and LSTM for Proactive Multivariate Time Series Classification

Mobile communications have become a vital domain for criminals and terrorists to exploit vulnerabilities, posing significant threats to public safety and national security. More precisely, they can employ cell site simulators such as International Mobile Subscriber Identity (IMSI) catchers to intercept and monitor mobile communications, enabling eavesdropping, tracking individuals’ movements, and potentially coordinating illegal activities while evading detection by law enforcement agencies. To overcome this issue, an innovative approach to detect IMSI catchers using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models is proposed. Leveraging the power of deep learning, the developed models process multivariate time series data to distinguish suspicious patterns indicative of IMSI catcher presence. This work compares nine different model architectures based on CNN, LSTM, or a combination of both through a series of case studies. We demonstrate that LSTM-based and Parallel CNNLSTM models outperform other architectures, achieving high precision and recall rates. Then, the best two models are tested with several sequence lengths. The presented models serve as valuable tools, providing a further enhancement to the security of mobile networks. The goal of this research work is to contribute to the broader mission of integrating artificial intelligence within the daily investigative practices of law enforcement agencies.
Aspect-Based Cross-Modal Sentiment Analysis of Fused Adjective-Noun Pairs

The objective of an aspect-based sentiment analysis task is to identify the emotional polarity of specific aspects in text sequences. Previous study usually only involve the text modality. However, user-generated comment data generally include not only plain text, but also a large number of image and text data. For ignoring the cross-modal correlation between image and multiple aspects of text, and the problem of alignment between text and image high-level semantics. We propose a new aspect-based cross-modal sentiment analysis model HCMI-PLNT (Hierarchical Cross-Modal Interaction - Pre-LN Transformer). The core idea of this model is to use the hierarchical cross-modal Pre-LN Transformer interaction module to capture the bidirectional deep interaction between aspect, text and image. The high-level semantic feature of images are obtained by extracting adjective-noun pairs (ANPs) from images, and the text is aligned with the high-level semantic of images. The accuracy of HCMI-PLNT model on the TWITTER-15 data set is 78.60%, and the accuracy on the TWITTER-17 data set is 72.43%. The experiment proves the feasibility and effectiveness of the HCMI-PLNT model.
Web Reading Recommendation System Based on Spring MVC+Hibenate Design and Implementation

With the popularization of the mobile Internet, followed by the explosive growth of online reading, online reading has become an indispensable and important part of the spiritual life of the vast number of people. The disadvantages of low knowledge storage and expensive unit price of paper books have a huge disadvantage in the face of online reading. Whether it is from the importance and necessity of the popularization of knowledge or the cost of acquiring knowledge, the development of online reading is an irreversible trend of the times, so the development of online reading platforms has its necessity.
Web Content Distribution with Low Latency on Worldwide Using Amazon CloudFront Service *

The proposed work uses CloudFront to tackle the problem of accessing a website rapidly for the people, as well as how the end-users upload or deliver videos, photos, or multimodal data to their website. This paper also explains how the end-user is accessing the information available on the server site which is far from the end-user along with heavy network traffic. Today everyone is in a hurry, so required fast accessing of web contents. To overcome the above-mentioned problem, Amazon web service CloudFront features and performance optimization is done using the Access Web Content-CloudFront (AWC-CF) algorithm. Access Web Content-CloudFront algorithm also en-sures the preparation of significant events, service launches and guaranteed low latency such that process 7 times faster over the internet (i.e. Normal web link processing takes time 5.6 seconds while through AWC-CF algorithm web link processing takes time 0.8 seconds) using amazon web service CloudFront infrastructure.
Exploring the Potential of Deep Reinforcement Learning for Autonomous Navigation in Complex Environments

One of the most challenging problems in robotics and autonomous vehicles is autonomous navigation in complex and dynamic environments. Deep Reinforcement Learning (DRL), which enables agents to learn complicated behaviors autonomously through trial and error, has demonstrated that it has the potential to be an effective solution to this problem. By utilizing the Waymo open dataset and the Proximal Policy Optimisation (PPO) algorithm, this research paper aims to investigate the potential of DRL for autonomous navigation in complex environments. In the first step of this process, we conduct a literature review that focuses on numerous research that has studied the application of DRL for autonomous navigation in various settings. After that, we discuss our methodology, which entails utilizing PPO to instruct an agent navigating the Waymo dataset. According to the findings of our study, the trained agent can properly navigate through the environment, even when barriers and other dynamic elements are present. In addition, we assess our agent's performance using various criteria, such as the percentage of successful attempts, efficiency, and risk. According to our research's conclusions, DRL-based navigation systems have the potential to create genuinely autonomous systems that can navigate across surroundings that are both complicated and dynamic. In general, the findings of this study demonstrate how important it is to investigate the possibilities of DRL to find solutions to complex problems in the fields of robotics and autonomous cars.
Modular Deep Learning for Big Data: Motivation, Challenges, and Approaches

Deep learning (DL) technologies have the potential to fundamentally transform many Big Data domains ranging from agriculture and healthcare to transportation and entertainment. However, a large percentage of “non-tech” sectors have been unable to fully harness the power of DL technologies because of the lack of expertise, resources and training data for developing, tuning and managing DL models. The monolithic model paradigm that dominates the current DL landscape has a number of drawbacks that have impeded wider adoption of DL in various Big Data domains. This paper presents an alternate vision, namely modular DL paradigm. Semantically meaningful DL modules form the basic building blocks of this paradigm, and specific DL tasks are accomplished by creating pipelines of these modules. This paper motivates the need for modular DL through a case study that includes an experimental analysis examine the trade-offs between modular and monolithic DL paradigms. We discuss the challenges in designing a modular DL framework for robust and cost-effective Big Data analytics, and outline approaches to address the challenges.
Deep Learning Framework for Student Course Recommender System for Higher Educational Institutions

With the advancement in technology like Artificial Intelligence (AI) and Machine Learning (ML) there are many sub fields which are emerged from the core areas. Deep Learning (DL) is one such field which is gaining vast importance and is often mentioned as branch of ML and AI. In order to quickly and efficiently find the relevant information on the internet based on our past browsing history, recommender systems are playing a major role. Many such systems has been proposed in recent works. Recommender system has been implemented in major E-commerce sites like Amazon, Flipkart, Netflix, YouTube and many other giants have introduced AI based recommendation systems. Most of these systems uses the algorithms which can efficiently collect user behavioral data, filter it and recommends the user various choices based on the previous data. These systems often need to develop their own framework and algorithms which can solve the purpose. Students are often confused to opt for courses out of various options available in today's digital world. Based on their skill, interest the system should recommend a course. A general framework where it can handle data diversity, various dimensions and stimulation types is needed. Deep learning based generalized framework can solve such type of issues. In this paper we propose such generalized and effective Deep learning framework for a student course recommendation system for students who wish to pursue a course in various Higher Educational Institutions. The recommender system can be useful for the students who wish to opt a particular course based on their past history of interest and data collected from the logs. In this paper we use the Intel OpenVino toolkit for the deployment of the recommender system. The recommender system which is based on deep learning will provide the student with the exact set of modules he need to choose based on his skill set. The result of a student recommender system using deep learning can vary depending on several factors, including the quality of the data, the effectiveness of the model, and the specific goals of the recommender system. In this paper we focused on better performance, personalized learning paths and enhanced student satisfaction.
Analysis of Vitiligo Disease Diagnosis Expert System Using Fuzzy AHP and TOPSIS

The disease Vitiligo causes the skin's color to deteriorate. This fading can affect any part of the body's skin in addition to the hair, eyes, and inner corners of the mouth. In this work, an expert system for Vitiligo diagnosis was developed to make it simpler for patients to determine the severity of their Vitiligo. The challenge this research work is how to employ the fuzzy AHP and fuzzy Topsis Method and fuzzy logic to develop an expert system to ease the diagnosis of Vitiligo. Fuzzy logic is one method that uses certain stages. Use case, sequence, and activity diagrams were used as a system design tool during the system's structured approach to construction.
Evaluate and Compare Machine Learning Models for Temperature Forecasting of Meteorological Data in Hohhot

Air temperature is closely related to daily life, and accurate temperature forecast can improve efficient production and convenient living. Currently, in terms of temperature forecast by machine learning, academic research is mostly based on the performance of a single or a few models on the same dataset, and there is a slight lack of research on comparing the performance of multiple models under the same conditions. This article uses real-world datasets to evaluate and compare 19 popular machine learning models based on 7 evaluation metrics under the same conditions, which helps with in-depth exploration, analysis, and improvement of machine learning models. The experiments show that CatBoost achieved the best performance on all metrics except for the training time.
Mitigating Concept Drift in Distributed Contexts with Dynamic Repository of Federated Models

This paper proposes a novel federated learning methodology, called FedRepo, that copes with concept drift issues in a statistically heterogeneous distributed learning environment. The proposed horizontal federated learning methodology, based on random forest (RF), can be used for collaborative training and maintenance of a dynamic repository of federated RF models, each one customized to a group of clients/devices. The clients are grouped together if their performance patterns with respect to the global RF model are similar. The performance of the customized RF global models is continuously monitored during the inference phase and the repository is accordingly adapted to mitigate the detected concept drift. The proposed methodology is studied and evaluated against an electricity consumption forecasting use case. The evaluation results demonstrate clearly that the proposed methodology is able to deal with concept drift issues in an efficient and adequate fashion without compromising the overall performance of the distributed environment.