Reviewerly/exHarmony huggingface dataset:
Utilizing Python for Web Scraping and Incremental Data Extraction

The automated process of extracting data from web pages is known as web scraping. The process involves downloading the HTML content of a web page, parsing it, and then retrieving the required data from it. Python's robust toolkit, which includes programs like Beautiful Soup and Scrapy, makes web scraping tasks straightforward and effective. Incremental data extraction, in addition to web scraping, is a useful tactic for dealing with large amounts of data or websites that frequently change their content. Retrieving only newly added or changed data since the previous extraction is the aim of incremental data extraction. Python offers several techniques for incremental data extraction processes, such as timestamp-based methods, pagination, and caching.
Optimization of Deep Neural Networks for Vehicle Classification

This paper addresses the challenges in vehicle recognition for optimizing results and reducing efforts in traffic management. The proposed solution focuses on developing solutions for a market leader of traffic cameras in Brazil, aiming to classify vehicle properties such as brand, segment, subsegment, and color. The proposal analyzes model portability and optimization techniques, demonstrating experimental results that significantly reduce computational costs without compromising accuracy. We applied pruning techniques and optimization approaches using metaheuristics to reduce approximately 40% of the model’s weights with minimal impact on accuracy. We also developed two inference and training scripts and a C++ executable packet.
Applying Reinforcement Learning to Combine Multiple Swarm-based Algorithms

Swarm intelligence is a very efficient field for the optimization of high-dimensional functions. Nevertheless, choosing the best swarm-based algorithm is still challenging because it requires expertise in the field. Here, we propose to use reinforcement learning to dynamically select the swarm-based techniques to solve a benchmark function based on the current simulation state. First, we created a swarm capable of modifying its metaphor over iteration. Next, we created a reinforcement learning environment to solve benchmark functions. Then, we trained Proximal Policy Optimization to select the well-suited metaheuristic (GWO, GPSO or LPSO) to solve Rastrigin and F3 based on the information retrieved from the simulation. Our proposal reached competitive results in all simulated scenarios. Moreover, we found that the use of GPSO is consistently more efficient at the middle of the convergence and that using GWO is more efficient than using the other selected algorithms at the beginning of the convergence. Future works will bring us more robustness in combining swarm-based techniques while decreasing the computational cost. Thus, we show that reinforcement learning has the potential to overcome the effort of choosing the well-suited metaheuristic for a specific problem.
Extraction of Emojis and Texts to Intensify Opinion Mining using Machine Learning and Deep Learning Models

This research mainly focuses on product reviews by improving the customer experience. Furthermore, it allows customers to use a better-quality product according to their budget. It allows Businesses to make data-driven decisions about product development, marketing tactics, inventory management, and customer service enhancements by collecting and evaluating sentiment data. This research examines the possibility of emoji-text fusion algorithms to increase sentiment prediction accuracy considering the widespread use of emojis to represent emotion in textual material. A varied dataset of product reviews from Flipkart and Amazon, including a wide range of consumer goods and services, has been put together for this research. This research focuses on sophisticated natural language processing (NLP) methods for text analysis and emoji sentiment classification to accomplish the required goal. To properly capture both textual and emoji-based sentiment cues, This study centers on various feature extraction techniques. Additionally, this study recommends creative fusion along with machine learning and deep learning models such as logistic regression, random forest, BERT, and LSTM to find the polarity of the both texts and emojis, Hence the result is obtained with BERT model with high accuracy including texts with emojis and the outcomes are obtained using proper visualization techniques.
Visual Analysis of High Dam Based on Digital Twin Technology

To solve the problem that high dams have high data dimension, high collection density and it is difficult for traditional data analysis methods to obtain effective information from large amounts of data in the construction of water conservancy projects, this paper puts forward a visualization analysis of high dams based on digital twin technology, which realizes the construction of virtual high dams and real-time feedback of virtual high dams to real high dams through the modeling and data reuse of Physical high dams.
An Autonomous Mobile Robot Path Planner using Spline Curves and Differential Evolution

Robots that operate autonomously require a path planner that ensures the robot reaches the desired position in a safe manner. The robot will be able to achieve greater speeds along the path if the path is smooth, minimizing the effort of the controller. This work presents two new path planners, called DE-Spline and DE-NURBS. These planners provide a smooth path directly by the cost function optimization using the algorithm L-SHADE (Success-History based Adaptive of Differential Evolution using linear population size reduction algorithm) and two path’s parametrization: Ferguson cubic spline and NURBS. Additionally, it ensures that the robot reaches the target in the desired orientation. The planners are compared with a Vector Field-based planner, widely used in robot soccer. Based on the results, it has been found that both planners provide a smoother path along the shortest path, by accounting for the robot’s starting angle and its goal angle. Furthermore, DE-NURBS provides a greater degree of planning freedom than DESpline.
Integrating Decision Theory and Syntactic Data for Enhanced Rough Fuzzy C-Means Clustering Algorithm

Clustering is a basic strategy in data mining, allowing the detection of latent patterns within complex datasets. However, when dealing with the datasets composed of a variety of properties, traditional clustering algorithms frequently find challenges. Also, the data gets separated into different clusters in the hard clustering paradigm, with each data clearly associated with a specific cluster. On the other hand, Rough Clustering outperforms these constraints by allowing data points to belong to many clusters at the same time and form connections with components of other clusters. The incorporation of decision theory allows to offer an entirely new trajectory for the creation of clusters. This advancement intends to provide the clustering process with an inherent adaptive nature, allowing the algorithms to res pond to data patterns dynamically and optimize cluster allocations. This research study provides a coherent and systematic methodology for cluster evolution through the perspective of decision theory, thereby contributing to the advancement of clustering techniques.
Leveraging BERT-GRU Model for Drugs Interaction Relationship Extraction

In the medical field, drugs are often used in combination to improve their efficacy, but it is also common for drug interactions to produce adverse reactions that can put a patient’s health at risk and even cause serious side effects or toxic reactions. In order to minimise the risk of adverse reactions from drug interactions, doctors need to understand the properties of each drug and provide appropriate dosing instructions based on the patient’s individual circumstances. In order to allow physicians to quickly extract drug-drug relationships, drug-drug interaction relationship extraction techniques need to be studied and analysed to establish a medical drug information base to assist physicians in accessing important information about drug-drug relationships and avoiding adverse drug interactions. This paper proposes a new algorithm for drug interaction relationship extraction, with two main studies covering the following aspects: (1) To address the problems of multiple meanings of words in relationship extraction, high computational complexity, low training efficiency and low generalization ability, this paper proposes a new model BERT-GRU based on recurrent neural network and BERT. in order to eliminate the influence of Chinese word separation ambiguity on entity relationship classification, the BERT is introduced as an embedding layer to better obtain the contextual information of Chinese characters; then The long-distance dependencies of entities in the sentences are captured by gated loop units and output to the Sigmoid layer for classification, so as to obtain better entity relationship classification results. (2) By comparing and analysing the experimental results of the BERT-GRU model with other models, the BERT-GRU model showed better performance on the drug-interaction relationship extraction task, especially achieving higher F1 values for relationships of advice, mechanism and int types. The BERT-GRU model performed even better compared to several other common models. This suggests that the combination of BERT and GRU is very effective in the drug interaction relationship extraction task. In summary, through a series of experimental comparisons, the BERT-GRU model proposes in this paper can effectively solve the problems of inability to distinguish between polysemous words and low training efficiency, and provides a more effective method for drug interaction relationship extraction.
Evaluating the Inclusion of Images with Artifacts in Medical Image Databases of Mammography for Machine Learning

The use of the federated learning technique has grown in the medical field of diagnostic imaging, with a large number of publications and applications in clinical practice. However, the lack of knowledge of the training data by the central server and the clients among themselves raises concerns about the local composition of the databases, raising questions about the occurrence of image artifacts and their impact on the final performance of the global model. Using databases with a selection of areas of interest for mammograms distributed in an unbalanced way to 03 clients, simulations of mammography image artifacts were proceeded, with their insertion in the databases on different scenarios. An open access convolutional neural network model (EfficientNetB7) was adapted to classify the images regarding the presence and suspicion of microcalcifications according to the BI-RADS system. By measuring the F1-score, the performance of the neural network was verified without the presence of artifacts, after the insertion of artifacts and after the exclusion of images with artifacts. The results showed an improvement in the performance of the central model when opting to keep the images with artifact in the training databases.
Intelligent Video Surveillance System for Real Time Effective Human Action Recognition using Deep Learning Techniques

In computer vision, recognition of human action is an important and difficult task with many practical uses, inclusive of visual recording surveillance systems and human-computer interaction. The integration of KTH and real-time datasets within a framework for human action recognition in video surveillance using deep learning. The integration of real-world data with the well-established KTH dataset notably improves the action recognition models’ generalization and robustness. The real-time dataset adds richness to the training process by capturing a variety of dynamic human actions. The system uses Convolutional Neural Networks (CNNs) to categorize behaviors in real-time video streams by extracting spatiotemporal information from video frames. The results of the experiments indicate how well this integrated dataset approach performs, and how well it can recognize a wide range of activities under different settings. This method demonstrates an overall performance of 98.7% using a real-time dataset. This breakthrough has substantially increased the dependability and efficacy of video surveillance systems insecurity applications, guaranteeing a safer environment through the effective detection and tracking of human actions in real-time scenarios.
Adversarial Feature Hallucination in a Supervised Contrastive Space for Few-Shot Learning of Provenance in Paintings

Few-Shot Learning (FSL) is a problem formulation in which an intelligent system must learn discriminative patterns from a strictly reduced set of samples. While this can be achieved through many distinct strategies, Adversarial Feature Hallucination (AFH) has shown great promise when applied to general-purpose datasets. In this work, we expand AFH to the domain of artwork provenance analysis, and train a conditioned generative network to produce features from a given FSL context, which in turn are used to enrich the training of classifiers of few-sample data. We then further enrich the generative process by leveraging multi-objective optimization and Supervised Contrastive strategies to learn a more discriminative artwork feature space. We obtain encouraging results evaluated in an extensive painting dataset — considering authorship attribution —, and find that the hallucinated features can improve the decision accuracy by a substantial margin when compared to the baseline.
Regularizing Neural Networks with Noise Injection for Classification of Brain Tumor in Magnetic Resonance Imaging

Overfitting jeopardizes deep learners dealing with noisy medical data. Despite its importance, our understanding of how neural networks generalize in medical imaging remains elusive. In this work, we propose to characterize the generalization profile of a model by evaluating its F1-score under data with varying noise levels. We use two architecturally distinct neural models, a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN), to classify brain tumor in Magnetic Resonance Images (MRI) in a independent test data with different levels of salt-and-pepper noise. Our results reveal a clear distinction between the generalization profile of MLPs and CNNs. We demonstrate that shallow models can perform comparably to deep models when regularization is neglected. We also show that deep neural models benefit more from noise injection than shallow neural models, regardless of the differences between training and test data distribution. Our study sheds light on the nature of generalization in neural networks, laying the groundwork for further investigations of their underlying learning processes.
Text-Conditioned Image Synthesis using TAC-GAN: A Unique Approach to Text-to-Image Synthesis

Text-to-image synthesis has emerged as a pivotal domain within artificial intelligence, enabling the creation of lifelike visuals from textual descriptions. This research work introduces a novel solution to the text-to-image synthesis challenge by leveraging the Text Conditioned Auxiliary Classifier Generative Adversarial Network. The proposed TAC-GAN model is both trained and evaluated using the renowned Oxford flower dataset, a benchmark in image synthesis tasks. Notably, our TAC-GAN integrates an auxiliary classifier to ensure semantic alignment between generated images and input text descriptions. This enhancement significantly elevates the authenticity and coherence of the synthesized images, thereby enriching their overall quality. To gauge the quality and diversity of the generated images, we employ two evaluation metrics: The Inception Score and the Multi-Scale Structural Similarity (MS-SSIM) Score. The combined utilization of these metrics furnishes comprehensive insights into the perceptual excellence and structural consistency of the generated images. This holistic evaluation framework underscores the resilience and effectiveness of our approach in delivering high-quality synthesized images that faithfully correspond to textual descriptions.
Method for Job Recommendation based on Machine Learning and Deep Learning Model

In the modern digital era, the integration of machine learning (ML) and deep learning (DL) in job recommendation systems offers a transformative approach to bridging candidates with appropriate job opportunities. Traditional job matching techniques, reliant on human evaluation, are often fraught with inefficiencies and inaccuracies, especially given the extensive volume of job seekers and openings. This study introduces an innovative job recommendation methodology that synergistically combines the capabilities of ML and DL models. Our proposed model leverages the adaptability of ML algorithms to process diverse datasets and the power of DL neural networks to discern intricate patterns within vast data troves. Initial findings demonstrate an improved recommendation accuracy and a reduction in the “cold start” problem often encountered in recommendation systems. Furthermore, the study addresses fairness concerns, ensuring that proposed model provides equitable suggestions across various demographics. By integrating these cutting-edge technologies, our method not only enhances the job-seeking experience for candidates but also streamlines recruitment processes for employers. The overarching aim is to foster a more efficient, transparent, and inclusive job market.
Human-in-the-Loop Evolution of Database Views for Data Exploration

Under the increasingly complex and interconnected world we live in, data has become extremely valuable. Nevertheless, exploring even a simple database is not a trivial task, as it requires technical knowledge which many new and non-technical data users do not have. This task includes writing database queries to retrieve data, uncover insights, and exploit patterns. Furthermore, in large volumes of data, finding valuable data that matches a certain user’s purpose requirement is challenging, especially under restrictive time constraints. Typically, this task is manual, ad-hoc, and time-consuming. To address these challenges, researches have proposed tools to support data exploration tasks, especially by means of View Recommendation. However, current recommendation approaches require enumerating all candidate views that can be defined over an input database. In this work, we introduce a novel view recommendation method that leverages a human-in-the-loop approach to create, evaluate, and tailor views to match user intent. Our experimental evaluation on real-world data demonstrates that our approach suitably recommends views that capture multiple perspectives on the data at hand.
Improving Transferability of Population-Based Learning-to-Optimize Algorithms Through Imitation Learning

The process of algorithm development is iterative and requires a meticulous evaluation of the existing problem. In recent years, Learning to optimize (L2O) models have transformed this task by using machine learning to generate efficient optimization methods. However, transferring knowledge effectively to problems divergent from the original training context remains a challenge encountered by most L2O approaches. This paper introduces a method that enhances transferability of population-based L2O algorithms through imitation learning. Our method adopts a two-step learning process involving imitation and reinforcement learning to train L2O policies. Experimental results demonstrate that our proposed model, incorporating Particle Swarm optimization (PSO) as the teacher algorithm, outperforms the baseline model in key scenarios. This validates the potential of imitation learning in catalyzing the progress of populations-based L2O models.
Analysis of Different Adversarial Attacks on Various NLP SoTA Models

The core technology driving several applications, including as question answering, machine translation, and text categorization, is known as “Deep Learning-based Text Understanding” (DLTU). Given an input x and any goal classification y, it is feasible to create a new input x’ that is identical to x but classed as y. However, neural networks are susceptible to adversarial instances. Given its growing use in security-sensitive applications like sentiment analysis and toxic content monitoring, it is quite troubling that the security flaws of DLTU remain widely hidden despite its enormous popularity. Here, we demonstrate how various SoTA NLP models are intrinsically susceptible to different types of adversarial text attacks, in which specially crafted texts are used to cause misbehavior in the target DLTU systems and services. By exposing the maliciously created adversarial cases, it is possible to assess or possibly enhance the robustness of these models. Here the various NLP SoTA models are TextAttack, a Python-based framework is used for analyzing different adversarial attacks on various NLP So-TA models.
SSDP-H: Hybrid Approaches for Subgroup Discovery on High-Dimensional Datasets

This work presents new approaches to the task of Subgroup Discovery with a focus on high-dimensional databases: SSDPH-E and SSDPH-SD. They’re hybrid, top-k, and mono-objective approaches resulting from the integration of SSDP+ with exhaustive search and SD, respectively. SSDP+ tends to be better than SD in breadth-first search and worse in depth-first search. Additionally, SSDP+ is faster in runtime but inferior in subgroup quality compared to the exhaustive search. Therefore, we believe that a hybrid approach can reduce the limitations of these algorithms by integrating their strengths. We evaluated the impact of these hybridizations with respect to criteria such as quality, diversity and execution time. The experiments were performed on 10 databases, covering bioinformatics and relevant problems in Brazil. The results indicate that the proposed solutions may represent a relevant advance for the Subgroup Discovery area.
Does Kaizen Programming need a physic-informed mechanism to improve the search?

In recent years, the study of physics-informed machine learning has increased. Works that use information about the shape or some characteristic of the expected function, have been used with genetic programming and neural networks. In those studies, it was found that including information about the expected model makes the resulting models better.Motivated by these studies, the goal of this work is the evaluation of the inclusion of information about the shape of the function in Kaizen Programming using a penalty function. In order to answer if the inclusion of this information in the search results in better models. In order to answer that we worked with 13 benchmark functions. The functions have between 2 and 9 input variables, and all have different types of shapes.We found that there is no significant difference in the performance of the models obtained using plain Kazan Programming and the shape-constrained approach.