Reviewerly/exHarmony huggingface dataset:
METADRIVE - A Decentralized Ride Sharing App *

Centralized ride-sharing apps operate under a closed ecosystem, where customers have limited access to data and information. This creates a lack of transparency and makes it difficult for users to evaluate the quality of the service they receive. App operators charge a commission for their services and increases the fare heavily during peak hours. Such apps operate under a set of rules and regulations, which can limit the flexibility of the service. They collect and store a significant amount of user data, including location, payment information, and personal details. This can compromise the privacy of the users and make them vulnerable to cyber attacks or identity theft. As an automated peer-managed version of Ola and Uber, the proposed solution is decentralized application or DApp, on etherium that utilizes trustless smart contracts. The model eliminates middle-men and projected fare is reduced. This is revolutionary where disputes can be sorted out and legal liability is moved into trust-less smart contracts. Experimental results show improved privacy, cost effectiveness and ride fairness of the proposed solution.
Feature Selection for Classification on High- Dimensional Data Using Swarm Optimization Algorithms

Feature selection plays a crucial role in classification by identifying relevant features. Bio-inspired algorithms, such as genetic algorithms, particle swarm optimization, and ant colony optimization, have gained popularity due to their ability to mimic natural processes and explore high-dimensional feature spaces efficiently. The analysis includes evaluating classification accuracy, and computational efficiency as performance metrics. The experimental setup involves dataset selection, choosing a classification algorithm, implementing bio-inspired feature selection, evaluation, parameter tuning, and comparative analysis. The findings provide valuable insights into the effectiveness and efficiency of bio-inspired algorithms for feature selection in classification. The project focuses on reducing high- dimensional datasets to low-dimensional ones using feature selection through classification with swarm optimization algorithms. It aims to validate the effectiveness of the Jellyfish Search algorithm (JSA), Horse Herd Optimization algorithm (HOA), and Binary Bat algorithm (BBA) on real-world datasets. The objectives include evaluating the algorithms' performance, conducting a comprehensive performance analysis, execution of classifiers like RF, DT, KNN, Voting Classifier and SVM, obtaining and tuning the results, and publishing the findings. The research outcomes contribute to the knowledge of feature selection for classification and provide guidance in selecting appropriate algorithms for specific classification tasks.
Recursive Descent Parser for Abstract Syntax Tree Visualization of Mathematical Expressions

This study presents a novel method for finding and evaluating mathematical expressions using Abstract Syntax Trees (AST). The approach is particularly adaptable for both academic and professional applications since it accurately parses complex equations and accommodates a large number of mathematical operations. The AST model ensures a hierarchical expression structure, enabling accurate and efficient evaluation. Parsed expressions are improved by incorporating interactive graph vi-sualisation, which simplifies mathematical computations and engineering tasks for understanding and application. The project's complete approach to detecting mathematical expressions stands out since it takes into account a wide range of functions and variables and ultimately enhances the system's accuracy and adaptability. The visual representation of AST also helps with debugging and checking the accuracy of parsed expressions, which adds to its significance in the field of mathematical expression recognition.
Revolutionizing Solar Generation Data Mining through Advanced Machine Learning Algorithms: Novel Insights and Results

Solar power generation has emerged as a significant source of renewable energy, emphasizing the importance of precise analysis and prediction of solar generation data. In this study, we focus on enhancing the accuracy of solar generation data mining using advanced machine learning techniques. Our objective is to effectively capture intricate patterns and variations in solar generation data, while also considering various environmental factors. To address this challenge, we propose a novel machine learning algorithm that leverages feature engineering and ensemble learning methods. Through extensive experimentation and comparative analysis, we demonstrate the superior predictive performance of our algorithm in comparison to conventional approaches. Our findings underscore the potential for accurate forecasting and decision-making in solar power generation, thereby contributing to the efficient utilization of renewable energy resources.
Dynamic accommodation measurement using Purkinje reflections and machine learning

Quantifying eye movement is important for diagnosing various neurological and ocular diseases as well as AR/VR displays. We developed a simple setup for real-time dynamic gaze tracking and accommodation measurements based on Purkinje reflections, which are the reflections from front and back surfaces of the cornea and the eye lens. We used an accurate eye model in ZEMAX to simulate the Purkinje reflection positions at different focus distances of the eye, which matched the experimental data. A neural network was trained to simultaneously predict vergence and accommodation using data collected from 9 subjects. We demonstrated that the use of Purkinje reflection coordinates in machine learning resulted in precise estimation. The proposed system accurately predicted the accommodation with an accuracy better than 0.22 D using subject's own data and 0.40 D using other subjects' data with two-point calibration in tests performed with 9 subjects in our setup.
Solving imbalanced learning with outlier detection and features reduction

Abstract A critical problem for several real world applications is class imbalance. Indeed, in contexts like fraud detection or medical diagnostics, standard machine learning models fail because they are designed to handle balanced class distributions. Existing solutions typically increase the rare class instances by generating synthetic records to achieve a balanced class distribution. However, these procedures generate not plausible data and tend to create unnecessary noise. We propose a change of perspective where instead of relying on resampling techniques, we depend on unsupervised features engineering approaches to represent records with a combination of features that will help the classifier capturing the differences among classes, even in presence of imbalanced data. Thus, we combine a large array of outlier detection, features projection, and features selection approaches to augment the expressiveness of the dataset population. We show the effectiveness of our proposal in a deep and wide set of benchmarking experiments as well as in real case studies.
Text Language Identification and Translator

Language Identification refers to the process of detecting the language(s) of the text in the document based on the script used for writing and observing the diacritics particular to a language. This research area has always fascinated researchers as early as 1970 and till now due to varied applications and increased demands of this field. In this work, I address the problem of detecting language of textual documents. I have introduced a method which is able to detect language of text more efficiently and accurately by determining their respective proportions and finding the greatest of them which represents the language of the text. I have demonstrated the performance comparison of three different approaches which are using n-gram approach (word-wise), using n-gram approach (character-wise) and using a combination of word search and stop words detection. My project currently contains language models for 4 languages. On an average the accuracy of my program is about 96.5%.
Digital Signature Based on Cryptography Implementing in Java

The digital signature in Java utilizes cryptographic techniques to create a unique hash of a message, which is then encrypted using a private key. This encrypted hash, along with the original message, forms the digital signature. Verification involves decrypting the signature with the corresponding public key, confirming the integrity and origin of the message
Short-Answer Grading for German: Addressing the Challenges

Abstract Short-Answer Grading (SAG) is a time-consuming task for teachers that automated SAG models have long promised to make easier. However, there are three challenges for their broad-scale adoption: A technical challenge regarding the need for high-quality models, which is exacerbated for languages with fewer resources than English; a usability challenge in adapting high-quality research prototypes to the needs of non-expert users, and a trust challenge in communicating the abilities and limitations of the tools. We propose to meet the technical challenge for German with a robust Transformer-based SAG model. We address the usability challenge with an easy-to-use graphical user interface for the SAG model, and the trust challenge with a workflow that allows teachers to evaluate the model on their own data, to choose on the basis of this evaluation which model predictions to trust, and in consequence to stay in control of grading their students while saving grading effort.
Detection of Image Manipulation using Feature-Based Algorithms

In today's digital age, the integrity of images is paramount as they serve various critical purposes, from news reporting to personal communication and marketing. With the widespread availability of powerful image editing tools, the risk of image manipulation, particularly copy and move forgery, has increased. This deceitful practice involves copying or moving objects within images to create manipulated versions, potentially deceiving viewers by altering the context or composition. To address this challenge, this research project is dedicated to image forgery detection with a specific focus on identifying copy and move forgery. We propose a comprehensive solution that leverages digital image processing techniques, encompassing image preprocessing, feature point extraction, feature matching, and the clustering of similar features. Our approach incorporates advanced algorithms, such as the Stationary Wavelet Transform (SWT) for image preprocessing, the Scale-Invariant Feature Transform (SIFT) for feature point extraction, and the Geometric Two Nearest Neighbour (G2NN) for feature matching. Additionally, we employ hierarchical clustering to create clusters of similar feature points, enabling us to pinpoint potential forgery regions. In summary, the aim to provide a robust and effective method for detecting and mitigating image manipulation, with a particular focus on copy and move forgery. By achieving accurate detection of such manipulations, we contribute to preserving the authenticity and integrity of digital images, thereby protecting against misleading and deceptive uses of altered images.
Head-mounted display-based augmented reality for water quality visualisation

Water covers most of the Earth’s surface and is nowhere near a good ecological or recreational state in many areas of the world. Moreover, only a small fraction of the water is potable. As climate change-induced extreme weather events become ever more prevalent, more and more issues arise, such as worsening water quality problems. Therefore, protecting invaluable and usable drinking water is critical. Environmental agencies must continuously check water sources to determine whether they are in a good or healthy state regarding pollutant levels and ecological status. The currently available tools are better suited for stationary laboratory use, and domain specialists lack suitable tools for on-site visualisation and interactive exploration of environmental data. Meanwhile, collecting data for lab analysis requires substantial time and significant effort. We, therefore, investigate using an augmented reality system with a Microsoft HoloLens 2 device to explore the visualisation of water quality and status in situ. The developed prototype visualises geo-referenced sensor measurements incorporated into the perspective of the surroundings. Any users interested in water bodies’ conditions can quickly examine and retrieve an overview of water body status using augmented reality and then take the necessary steps to address the current situation.
Hydrogenation catalyst target detection algorithm based on improved CenterNet

In order to realize intelligent recognition and statistics of hydrogenation catalyst electron microscope image information and speed up electron microscope experiments. This paper proposes an improved CenterNet hydrogenation catalyst target detection algorithm, which uses Resnet50 backbone network to extract features, and adopts two sets of spatially separable convolution optimization residual modules of different scales to improve feature extraction capability. Moreover, FPN (Feature Pyramid Networks) is added to integrate shallow and deep features. In order to further solve the problem of background interference, the coordinate attention module is introduced to adjust the weight from adaptation, which can highlight the target characteristics of the catalyst more effectively. At the same time, the backbone network is optimized by referring to DenseNet in the downsampling stage, and on this basis, an improved ASPP module is added to adjust the sampling rate and increase the receptive field, so as to further improve the network's ability to identify and locate the target. Experimental results show that the AP value of the improved CenterNet model improved by 19.37, 20.16, 24.80, and 12.92 percentage points over the original CenterNet, YOLOv4, SSD, and Faster R-CNN detection algorithms in the same experimental setting. After the improvement, the detection accuracy of the model is obviously improved, and the model has a high capability of hydrogenation catalyst target detection.
Survey of intelligent program synthesis techniques

Program synthesis refers to the task of solving a specific problem by automatically generating a computer program. It has received considerable attention from artificial intelligence and programming language communities. Over time, software codes and group wisdom have been accumulated on the internet. Simultaneously, artificial intelligence, such as deep learning, has obtained promising achievements in numerous fields, which has motivated researchers to address the problem of automatic program generation by considering both software engineering and intelligent technology. The key challenges in the field of program synthesis mainly consist of the huge search space of the programs and the ambiguity of user intent. In this study, we analyze program synthesis techniques according to their user intent description, focus on the impact of new technologies on program synthesis, such as data-driven and artificial intelligence, and summarize the pruning methods of program space and search technologies. Further, we discuss the existing challenges in program synthesis technology and present suggestions for further studies in this field.
An improved BiLSTM model for daily activity recognition

Approaches and models of intelligent control have been developed to improve quality of daily life. Smart home solution has been designed to provide convenience for residents by recognizing their daily activities. In order to improve the performance of daily activity recognition, here we proposed an improved BiLSTM-Attention based daily activity recognition method which introduced dimension upgrading. Firstly, we used the feature extraction algorithm to obtain the relevant feature vectors. Secondly the feature vectors were transferred to the Bi-directional Long Short-Term Memory (BiLSTM) model for learning the time-series relationship between features and daily activity recognition. Thirdly, the BiLSTM model and attention mechanism were combined to improve the discrimination capability of daily activity features. Finally, the input data of the BiLSTM-Attention model was upgraded to improve the performance of daily activity recognition. The the F1-score of BiLSTM-Attention model was improved over the benchmark method by 5% using the public dataset CASAS_Cairo, and by 19% after the data dimension upgrading. On the public dataset CASAS_Aruba, the F1-score was improved 6% by using BiLSTM-Attention model and improved 11% by using additionally data dimension upgrading.
Construction of cloud detection sample library based on Landsat-8 data and semi-supervised learning

Cloud detection of remote sensing images is an important preprocessing step for remote sensing image applications. While machine learning (deep learning) methods improve the accuracy of cloud detection in remote sensing images, they put forward higher requirements for the quantity and quality of data sets. However, if you want to reconstruct the dataset by yourself, you generally need to select the training samples by manual visual interpretation, which will consume a lot of manpower and material resources. Previous research mainly focused on the optimization of cloud detection algorithms, and few studied the extraction of training samples. In order to construct a cloud detection dataset, this paper proposes an Erosion and Diversion-based semi-Supervised Learning (EDSL) model. Fmask algorithm is used to automatically obtain cloud masks from Landset-8 remote sensing images to construct a cloud detection dataset. The results show that the dataset constructed by EDSL model is better than Fmask algorithm. The precision of the dataset constructed by EDSL model reaches 0.93, which exceeds Fmask. At the same time, we use SVM algorithm to conduct experiments on this data set, and the results are equivalent to the data set constructed by human vision, which proves the scientific nature of this method.
Construction and application of a knowledge graph for flood control and emergency response plans

The digitization of emergency plans is an important foundation for improving the information level of emergency management in China. In response to the problems of low level of intelligence and reliance on traditional document management methods in most emergency plans in the water conservancy industry, this paper focuses on the technical path and methods of constructing a knowledge graph for flood control and emergency response plans. Based on this and combined with the construction of the knowledge platform of the digital twin project of the Dateng Gorge Reservoir, digital practices of flood control and emergency response plans for Dateng Gorge Reservoir were carried out. The application results provide intelligent support for emergency decision-makers in the smart water conservancy industry and effectively improve the scientific and accurate decision-making in water conservancy emergencies.
Traffic flow prediction algorithm of dangerous curved slope section based on random forest algorithm and open source cloud computing platform

In order to improve the accuracy of traffic flow prediction on dangerous curve sections and shorten the time consumption, this paper designs a traffic flow prediction algorithm on dangerous curve sections based on Random Forest algorithm and open-source cloud computing platform. Firstly, collect traffic flow data for dangerous bend slope sections, and design a traffic flow data storage system architecture for dangerous bend slope sections based on open-source cloud computing using Ceph open-source distributed storage system; Then, the stored data is repeatedly cleaned, and the cleaned data is used as the test data. Finally, a traffic flow prediction algorithm based on Random Forest is designed for dangerous curve sections. So far, the design of traffic flow prediction algorithm for dangerous curve sections based on Random Forest algorithm and open-source cloud computing platform has been completed. The experimental results show that the overall effect of the traffic flow forecast of the dangerous curve slope section designed in this paper is good. When the number of vehicles reaches 150, the prediction error of the proposed method is only 6.17%, and the prediction time is 12.4 s. It is proved that the design method in this paper can significantly improve the traffic flow prediction of dangerous curve slope sections and can be used in dangerous curve slope sections.
Research on path planning of mobile robot based on NF-RRT

In order to solve the problem of node aggregation and low collision detection efficiency of RRT algorithm in complex environments, this paper proposes an RRT algorithm based on a multi-indicator node filtering strategy. This algorithm can avoid repeated collision detection of nodes in subsequent planning processes by collecting information such as collision probability, collision direction angle, and node distribution. With the growth of the random tree, the limit information in the space can be continuously improved, so that the reachable workspace of the robot will be gradually clear. Through comparative experiments, it has been proven that the improved algorithm in this paper can quickly and stably avoid obstacles and reach the target point, effectively improving the efficiency of path planning.
Emotion analysis of dialogue text based on ChatGPT: a research study

Emotions are important signals in human communication in daily life and are also considered a crucial factor in human-machine interaction. Conducting emotion analysis based on text data holds significant importance for expanding the scope of machine applications and enhancing interactivity. In this paper, we conduct emotion analysis research using the public Twitter dataset, focusing on two widely used models in the current NLP field for emotion analysis: the Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). Additionally, we explore the application of large pre-trained language models, represented by ChatGPT, in the domain of emotion analysis. The experimental results indicate that while CNN and BiLSTM models achieve relatively high accuracy in supervised emotion analysis, ChatGPT demonstrates superior performance in unsupervised learning for emotion analysis. Furthermore, the exploration of emotion analysis based on ChatGPT reveals some previously unnoticed issues.
End-to-end OCR text recognition method for power equipment nameplate based on image enhancement and template matching

Due to the low accuracy and poor recognition performance of traditional methods in end-to-end OCR text recognition of power equipment nameplates, an end-to-end OCR text recognition method for power equipment nameplates based on image enhancement and template matching is proposed. Calculate the similarity of end-to-end OCR text data collection on power equipment nameplates and extract end-to-end OCR text features on power equipment nameplates; Based on actual node deployment requirements and standards, combined with image enhancement and template matching technology, OCR text feature recognition nodes are deployed to achieve end-to-end OCR text recognition on power equipment nameplates. The experimental results show that the proposed method has a maximum recognition accuracy of 99.67%. The difference between the recognition results and the actual situation is small.