Reviewerly/exHarmony huggingface dataset:
Understanding the Order of 500 and 1000 Rupees Notes Ban using Reinforcement Learning

In the field of machine learning called reinforcement learning, complicated sequential decision-making problems have been addressed. The issue that arises when an agent learns behavior by trial-and-error runs to determine the ideal policy, or the sequence of behaviors so that rewards are maximized,is known as reinforcement learning. Because many reinforcement learning methods use dynamic programming approaches, the environment is characterized as a Markov Decision Process (MDP). The research presents reinforcement learning using Bigram, trigram, and 4-gram models for tweets collected for "500 and 1000 notes banned." A multistage graph problem is used to draw the graph and the Bayes method is used to compute the probabilities. For the given word sequence, it determines the shortest route between source and destination. After that, the path is defined by the agent's randomly selected states and actions, which are subsequently followed to receive rewards. Epsilon greedy selection mode randomly chooses an action to explore the environment.
Exploring Software Development Change Analysis with an Emphasis on Requirements

Most software requirements are not definitive of the development process. Rapid changes in user expectations, market conditions, and company practises all need regular updates to software. Requirement change management is a crucial and difficult component of every software development project. Project failure or cancellation often occurs because of requirements changes. Requirements Change refers to requirements that are added, removed, or amended during the system development life cycle. Requirements Change requires additional work in the design phase, which boosts the cost of developing the system, lengthens the time required, and reduces system quality. The paper investigates research efforts in the topic of requirement change and helps in determining the study's purpose. Various Requirement Change Management concepts and approaches are provided, and numerous activities are conducted to mitigate the effects of requirement changes. The study emphasizes on causes, attributes, prioritization of changed requirement, framework for RCM and Change Impact Analysis. This study briefly describes all the possible fact and figure about requirement change. The study includes various phases of requirement change management such as Requirement Elicitation, Requirement Change Identification in Requirement Document using Two Phase Requirement Document Comparison Algorithm, Prioritization of Changed Requirement using Fuzzy approach, Interdependency analysis and change impact analysis on various software project parameters such as time, cost and human resources.
Artificial Intelligence Enabled Methods for Human Action Recognition using Surveillance Videos

Computer vision applications have been attracting researchers and academia. It is more so with cloud computing resources enabling such applications. Analysing video surveillance applications became an important research area due to its widespread applications. For instance, CCTV camera are used in public places in order to monitor situations, identify any theft or crime instances. In presence of thousands of such surveillance videos streaming simultaneously, manual analysis is very tedious and time consuming task. There is need for automated approach for analysis and giving notifications or findings to officers concerned. It is very useful to police and investigation agencies to ascertain facts, recover evidences and even exploit digital forensics. In this context, this paper throws light on different methods of human action recognition (HAR) using machine learning (ML) and deep learning (DL) that come under Artificial Intelligence (AI). It also reviews methods on privacy preserving action recognition and Generative Adversarial Networks (GANs). This paper also provides different datasets being used for human action recognition research besides giving an account of research gaps that help in pursuing further research in the area of human action recognition.
Applying Ensemble Machine Learning Techniques for Fake News Identification

The sharing of information has entered an unprecedented era in human history due to emergence of the Internet With the widespread use of social media sites like Facebook and Twitter. As these platforms enjoy extensive use, users are generating and disseminating a wealth of information, some of which is inaccurate and devoid of factual basis. Detecting false or misleading information within textual content poses a significant challenge. Before arriving at a judgment regarding the accuracy of an article, it is imperative to consider various factors within a specific domain. This paper proposes an Ensemble method for the identification of fraudulent news stories. We leverage different textual features found in both authentic and fake news articles. Our dataset comprises 72,134 news articles, with 35,028 being genuine and 37,106 being false, categorized as binary 0s and 1s. To evaluate our approach, we employed well-known machine learning classifiers including Logistic Regression (LR), Decision Tree, AdaBoost, XGBoost, Random Forest, Extra Trees, SGD, SVM, and Naive Bayes.&#x0D; To enhance the precision of our findings, we devised a multi-model system for identifying fake news the Ensemble approach and the aforementioned classifiers. Experimental analysis conclusively demonstrates that our suggested ensemble learning technique surpasses the performance of individual learners.
Rain Streaks Removal from Rainy Images Using Convolutional Neural Networks

Image processing is very important thing to improve the contrast and enhance the degraded images of real world. The rainy images are blurred and contain unclear structure of the objects for that apply deep learning based method to recover the rainy image objects. In this paper, remove the rainy streaks from rainy image and improve the contrast of the rainy image for that apply deep learning method of RainyNet is based on the Convolutional Neural Network. The RainyNet is made up of the encoder-decoder structure is constitute the detail rectify the network to find out the final clear structure. Here, DarkRain trained synthesised dataset is take over to testify the proposed method to produce good results than state-of-art methods. The proposed RainyNet is achieved better results than existing methods in terms of PSNR and SSIM.
A Quantitative Analysis of Noise Impact on Document Ranking

After decades of massive digitization, a sub-stantial amount of documents exists in digital form. The accessibility of these documents is strongly impacted by the quality of document indexing. Most of these documents are indexed in noisy versions that include numerous errors. The noise can be due to manual input mistakes or optical character recognition process and results in errors like spelling mistakes, missing characters, and others. This paper presents a study of the impact of noise on document ranking, an essential task in natural language processing (NLP) with wide-ranging practical applications. We provide a deep and quantitative analysis of the impact of recognition errors on document ranking by testing two popular ranking models on several noisy versions of a subset of the MS MARCO passage ranking dataset, with various levels and types of noise. Our study provides insights into the challenges of document ranking under noisy conditions and advocates for developing ranking models that are more robust to noise.
A Simple yet Effective 2D-3D Lifting Method for Monocular 3D Human Pose Estimation

Monocular single-frame 3D human pose estimation (HPE) has garnered significant interest, particularly in the domains of human-computer interaction and human action recognition. Several remarkable works have achieved excellent results in obtaining accurate 2D human poses. Building upon the foundation laid by previous advancements, this paper focuses on 2D-3D lifting. The proposed 2D-3D lifting algorithm in this paper is a Transformer-based model, which demonstrates its unique advantages in processing sequential data. The self-attention mechanism in the Transformer can handle global information without being limited by the receptive field. The Transformer's superior ability to process global information enables it to adaptively learn the relationships between human joints across different human behaviors. However, directly estimating 3D human pose from a single 2D image is a complex task due to depth ambiguity and joint occlusion. This ill-posed problem arises from the limited information available in a 2D image, making it challenging to determine the precise 3D pose. Additionally, occlusions further complicate the accurate estimation of joint positions. This article introduces a Transformer-based network that enhances the algorithm's robustness by utilizing multi-layer dual-stream blocks. One path concatenates the 2D coordinates and the focal length of the camera as input, while the other path takes the difference between the 2D coordinates as input. The two paths undergo an information fusion process at the front end of the block. Experiments conducted on various datasets verify the effectiveness of the algorithm and achieve state-of-the-art performance on Human3.6M and MPI-INF-3DHP benchmarks. Our code will be made publicly available on GitHub.
Investigating the Potential of AI-Driven Innovations for Enhancing Differential Evolution in Optimization Tasks

In recent years, artificial intelligence (AI) and machine learning have demonstrated remarkable potential in various application domains, including optimization. This study investigates the process of leveraging AI, particularly large language models (LLMs), to enhance the performance of metaheuristics, with a focus on the well-established Differential Evolution (DE) algorithm. We employ GPT, a state-of-the-art LLM, to propose an improved mutation strategy based on a dynamic switching mechanism, which is then integrated into the DE algorithm. Throughout the investigation, we also observe and analyze any errors or limitations the LLM might exhibit. We conduct extensive experiments on a comprehensive set of 30 benchmark functions, comparing the performance of the proposed AI-inspired strategy with the standard DE algorithm. The results suggest that the AI-driven dynamic switching mutation strategy provides a competitive edge in terms of solution quality, showcasing the potential of using AI to guide the development of improved optimization algorithms. This work not only highlights the effectiveness of the proposed strategy but also contributes to the understanding of the process of using LLMs for enhancing metaheuristics and the challenges involved therein.
GRDet: Rotated Object Detection in Remote Sensing Images Based on Gaussian Distribution

In recent years, many rotated object detection (ROD) methods have been proposed and have attracted wide attention in many fields. Most of them use anchor-based or Gaussian heatmaps for label assignment (LA), which cannot capture the shape and orientation characteristics of the rotated object and introduce a large number of hyper parameters. At the same time, most methods only add angle regression or use enclosing rectangles to realize ROD, which cannot express the object well. In this paper, we propose a new method for ROD, named GRDet, which is keypoint-based Anchor-free algorithm. GRDet can adaptively learn and represent an object with point sets, discarding the limitation of anchor on the size and orientation of the object. Specifically, we introduce a conversion function that is able to transform the point set into a rotated bounding box (RBB) for precise localization and classification. In addition, we propose a Gaussian-based dynamic label assignment (GDLA) strategy to realize the assignment of positive and negative (P&N) samples, which can adaptively learn according to the size and orientation characteristics of any rotated object. Moreover, we define an intersection over union (IoU) suitable for ROD, called Gaussian-IoU, which simulates the calculation of IoU by Gaussian distribution and solves the case that some points are not differentiable. Furthermore, we design a dynamic spatial quality constraint (DSQC) for RBB, which can dynamically evaluate the quality of the predicted RBB, and adaptively select high quality RBB. We use KFIoU loss and introduce Gaussian center loss to supervise the training of the network. Extensive experiments with DOTA dataset demonstrate the effectiveness of our proposed method.
Prediction of Bed-Leaving Behaviors Using Edge AI to Prevent Medical Accidents

The incidence of falls in hospital facilities is high and can lead to a decrease in patients' quality of life and an increase in medical expenses. Therefore, the development of a system that can predict getting-up from a bed is necessary. This study proposes a get-up detecting sensor using 6-axis inertial sensor. This system can detect getting-up from a bed in real-time using machine learning with Edge AI. To evaluate the basic performance of the proposed system, a protocol was applied for four subjects, and data were collected. Alert accuracy rate and false alert rate were used as evaluation metrics, and a model was built using data from three of the subjects and evaluated with the remaining subject, which was repeated for all four subjects. For high-risk bed-leaving behavior, medium-risk pre-bed-leaving behavior, and low-risk get-up behavior, the alert accuracy rate (i.e., Recall) was 89.4%, 97.5%, and 86.3%, respectively, and the false alert rate (1-Precision) was 6.3%, 10.2%, and 0.0%, respectively. This confirmed the possibility of predicting rising behavior with high accuracy. Furthermore, as a proof of concept, a real-time get-up detection system was developed, and its practicality was demonstrated. Future challenges include reevaluating feature extraction and evaluating the performance of the proposed system with a diverse range of subjects of different ages, genders, and health statuses.
Video Self-Supervised Cross-Pathway Training Based on Slow and Fast Pathways

In the field of video self-supervised learning, contrastive instance learning methods suffer from a lack of semantic information, resulting in inadequate generalization in downstream tasks. Although optical flow can provide some semantic information, it requires significant computational cost prior to training. To address this, we propose a Video self-supervised Cross-pathway training model based on Slow and Fast pathways (VCSF). This model separately extracts temporal and spatial features from pure RGB video frames, and uses the complementary representations of the two pathways to conduct cross-pathway training. Additionally, we propose a motion perception module in the low-frame-rate space to enhance the network's ability to perceive rapidly changing human motion. We conducted extensive experiments in downstream missions of UCF101 and HMDB51, and obtained state-of-the-art results in models using the UCF101 data set for self-supervised pre-training, including motion recognition and nearest neighbor retrieval.
CMM: Code-Switching with Manifold Mixup for Cross-Lingual Spoken Language Understanding

Spoken language understanding (SLU) is an important task which involves two subtasks, including intent detection and slot filling. Although it has achieved great success in high-resource languages, it still remains challenging in low-resource languages due to the lack of labeled training data. Consequently, there is growing interest in code-switching method for cross-lingual SLU to solve the problem in the low-resource languages. However, despite the success of existing models, most of these methods fail to effectively leverage the code-switched utterances. In this paper, we propose a novel framework termed CMM for zero-shot cross-lingual SLU which simplifies the learning task for the model. Specifically, we apply both mixup and curriculum learning method to dynamically combine the information from pure utterances and code-switched utterances. Experimental results demonstrate that the proposed framework improves the performance compared to several strong baselines and achieves the state-of-the-art performance on MultiATIS++ dataset, with a relative improvement of 3.0% in terms of overall accuracy over the previous best model.
Data Mining with Supervised Instance Selection Improves Artificial Neural Network Classification Accuracy

IDSs may monitor intrusion logs, traffic control packets, and assaults. Nets create large amounts of data. IDS log characteristics are used to detect whether a record or connection was attacked or regular network activity. Reduced feature size aids machine learning classification. This paper describes a standardised and systematic intrusion detection classification approach. Using dataset signatures, the Naive Bayes Algorithm, Random Tree, and Neural Network classifiers are assessed. We examine the feature reduction efficacy of PCA and the fisheries score in this study. The first round of testing uses a reduced dataset without decreasing the components set, and the second uses principal components analysis. PCA boosts classification accuracy by 1.66 percent. Artificial immune systems, inspired by the human immune system, use learning, long-term memory, and association to recognise and v-classify. Introduces the Artificial Neural Network (ANN) classifier model and its development issues. Iris and Wine data from the UCI learning repository proves the ANN approach works. Determine the role of dimension reduction in ANN-based classifiers. Detailed mutual information-based feature selection methods are provided. Simulations from the KDD Cup'99 demonstrate the method's efficacy. Classifying big data is important to tackle most engineering, health, science, and business challenges. Labelled data samples train a classifier model, which classifies unlabeled data samples into numerous categories. Fuzzy logic and artificial neural networks (ANNs) are used to classify data in this dissertation.
REQUIREMENTS FOR THE COMPILATION OF EDUCATIONAL AND METHODOLOGICAL COMPLEXES FOR THE COURSE OF ALGEBRA GRADE 9

The article considers the structure of the implementation of the requirements for compiling the OMC for the 9th grade algebra course. The formation of national statehood, a market economy requires reforms of all systems of society, including the education system, with a radical change in social relations. Because education is one of the most important systems of society, as well as a structural part of society, on the work of which not only the present, but also the future depends.The ongoing reforms in the education system of the republic are aimed at achieving a high goal - educating a new generation with deep and solid knowledge, capable of creative thinking, a developed culture of worldview, respect for the environment, by defining and developing a strategy for the development of the national education system, development of concepts of secondary education, formation of a new content of education. The solution of these tasks requires changing the questions set in the pedagogical tasks of the educational process to a certain level. Therefore, in the issue of changing the content of education, one of the main tasks of modernizing the system of school education, including mathematics, is the creation and development of modern teaching materials.
ANALYSIS OF UNINTENDED PREGNANCY FACTORS ON WOMEN OF REPRODUCTIVE AGE IN EAST JAVA USING LOGISTIC REGRESSION WITH SMOTEN

Unintended pregnancy is a condition where pregnancy occurs without the desire of want to have children. The high number of unintended pregnancies shows the need to research to find out the factors attributed to unintended pregnancy. Unintended pregnancy data used as a response variable includes imbalance binary data, which requires the use of logistics regression analysis. The imbalance of unintended pregnancy data causes a misclassification where a minority class sample can be classified as a majority class. One of the methods to overcome this imbalance is resampling. This research uses the Synthetic Minority Over-sampling Technique-Nominal (SMOTE-N) to overcome the imbalance. This technique synthesizes a new sample to balance the dataset by resampling the minority class sample. The data used in the research is the 2019 East Java data of the Accountability and Performance Survey. The sample is 8327 women of reproductive age. The variables which are expected to affect unintended pregnancy are age, education, occupation, residence, marital status, number of living children, and contraceptive knowledge. The best model obtained from the performance through accuracy, sensitivity, specificity, and G-mean. The results show the average accuracy between the model without imbalance treatment shows 89.7 % accuracy compared to only 65.3 % accuracy of the logistics regression model using SMOTE-N. However, the sensitivity of the model without imbalance treatment is lower than that using SMOTE-N. Moreover, the specificity and the G-mean show a not available value (N.A.), which indicates there is an imbalance that cannot classify data of the minority class sample. The results of the regression based on the Odds Ratio (OR) show that women aged 25- 34, aged 35, have higher education, working, married, living in rural areas, have more than two children, and have good contraception knowledge are at high risk of having an unintended pregnancy.
ALGOMOVE – A Move Embedding for Algorand

As applications based on Distributed Ledger Technology (DLT) gain popularity, the wide range of vulnerabilities that have affected existing blockchains (most notably Ethereum and Solidity-based applications) has motivated renewed interest in the design of programming languages capable of providing more adequate abstractions for managing digital assets and their access control policies. These mechanisms are crucial to certify that applications are safe and secure before deploying them on the target blockchains.Venturing into this challenge, we focus on Move, currently one of the most promising programming languages for resources and digital assets management with the aim to investigate its effectiveness in the realm of general-purpose smart contract development, and the long-term goal to identify the design principles and language-based techniques for the safe and secure development of asset-based DLT applications. As a first step in that direction, in the present paper, we develop ALGOMOVE, a Move embedding on Algorand. In addition to providing new insight into the nature of digital assets, the embedding is noteworthy in its own right. It provides Algorand/TEAL developers with a friendly API that aligns with their familiar programming patterns, while at the same time leveraging Move’s mechanisms of static typing and security verification to offer certified, language-level protection against double spending and other resource-related pitfalls commonly encountered in DLT applications.
Blockchain-Based Smart Contracts for Land Title Registry Opportunities and Adaption for fiji

The current situation of Land Registration in Fiji is a time-consuming set of procedures that requires liaising with various middlemen and intermediaries where each would have a different hidden cost and numerous delays with documentation. This paper highlights the issues faced by new buyers, such as dealing with fraudulent agents, lack of transparency, accountability, and delays within the entire process. Therefore, we propose a new secure system of transferring land ownership with increased transparency and security using blockchain technology.