Reviewerly/exHarmony huggingface dataset:
TSK Fuzzy Model with Integrated Label Smoothing and Feature Smoothing

The TSK fuzzy model, proposed by Takagi, Sugeno, and Kang, transforms complex nonlinear problems into linear problems on different small segments and uses multiple linear subsystems to fit the nonlinear system. When the TSK fuzzy model is applied to imbalanced data, its learning performance is easily affected by data imbalance, resulting in poor generalization ability. Real-world data often exhibits imbalanced distributions, and existing techniques for handling imbalanced data focus on independent classification objectives. However, many tasks involve classification indicators without obvious hard boundaries. This paper introduces two methods (label smoothing and feature smoothing) to handle the imbalanced classification of labels using the similarity of neighboring targets in the label space and feature space. The specific weighting method and the fusion of the TSK model are used to improve the generalization ability of the TSK fuzzy model and ensure the interpretability of the obtained rules. Experimental results show that the integrated TSK fuzzy model has good performance for imbalanced classification.
The New Development of Steganography Technique for Problem Solving of Gaussian Blur

Cybercrimes are increasingly invading the privacy of individuals, organizations, and governments. Personal data is increasingly insecure because of illegal data collected by unauthorized person. This study aims to develop a data embedding technique in image whose data robustness is tested when attacked by Gaussian Blur. The development of this technique uses the python programming language and the LSB (Least Significant Bit) method. By testing the gaussian blur attack using imperceptibility, fidelity, and robustness tests. The test results that have been carried out for imperceptibility testing use 25 respondents with 10 images and get a 80 percentage. The results of the fidelity test using 10 images found that an average MSE value is 0.001623 and an average PSNR value is 77.642 dB. The results of the robustness test using a blur radius of 0 to 1 on 10 stego-images obtained the message that 8 out of 10 stego-images could be extracted.
Copy-paste Augmentation for better saliency learning of underrepresented Object Classes

Data Augmentation plays an important role in training a better and well generalized object detection model. The publicly available object detection datasets are limited and are severely unbalanced. This leads to an inconsistent and unstable representation learning of the minority class objects. Hence, the resulting object detector model suffers from confirmation bias problem, giving a lot of inconsistent detections of minority class samples. To solve the unbalanced nature of object detection datasets we propose copy-paste augmentation method. In this method we paste the minority class samples into the images, which have no minority class samples. Our method relies on the random resizing and brightness adjustment of the selected minority sample for pasting. Our method can increase the number of samples of minority class which in turn help detector to learn better and accurate representation of the minority class objects.
Accelerating YOLO-based Real-time Object Detection via Foveated Rendering

Foveated rendering emulates human vision by prioritizing sharpness at the center while introducing peripheral blur outside this central area. We explore its potential for enhancing object detection performance by integrating it into various object detection methods. Our investigation demonstrates that foveated rendering effectively reduces detection time and false alarm detections, thus showcasing its utility in improving object detection performance. Experimental results indicate a reduction in detection time of up to 20% compared with object detection using conventional rendering techniques.
The Application of Brain Drain Optimization Algorithm on Static Drone Placement Problem

With the advancement of technology, the desire to use more robots and computers instead of humans has also grown. One of the robots that can be used in lots of situations is a drone. Drones can be used to control or monitor some targets but finding the optimal position for them to fly is a big problem that is in the group of NP-hard problems. In this paper, we tried to solve the static drone placement problem with a recently-proposed swarm intelligence algorithm called BRADO which is derived from the migration of humans between countries. The results of our experiments show that BRADO has worked well in solving the problem. In our tests, we used static drones and targets deployed in a squared search space and our goal is to find the optimal position for the drones in a way that all of the targets will be covered. The results of our proposed solution were better than some other famous meta-heuristic algorithms. The outcome of our work shows that BRADO works well with the static drone placement problem.
A Chaotic Crow Search Algorithm for Overlapping Clustering

Clustering is a major task of data mining that can be used to partition a collection of data points into distinct groups, commonly referred to as clusters. Each clustering algorithm aims to minimize the inter-cluster similarity and, at the same time, maximize the intra-cluster similarity. However, many datasets in the real world are inherently overlapping. In such datasets, a single sample has the potential to be assigned to more than one cluster. Overlapping K-Means (OKM) method is one of the most effective overlapping clustering methods. Similar to the K-means method, the performance of the OKM method is notably affected by the choice of initial cluster centers. In this paper, to enhance the OKM method's performance, we determined the optimal initial centers using the chaotic crow search algorithm. We used the FBCubed metric, which evaluates the effectiveness of overlapping clustering approches. According to the obtained results of eight publicly available datasets, the proposed method demonstrates superior results compared to other methods under comparison and, thus, it can be employed as an effective technique for clustering datasets.
A Novel Hybrid Method for Clustering Text Documents using Evolutionary Optimization

Unstructured text documents generated daily by millions of internet users have allocated a considerable volume of data in this digital age. Text document clustering is widely regarded as a highly effective method for analyzing text documents, particularly in response to the growing prevalence of big data. This technique is employed to group documents based on their content. Many text clustering algorithms commonly employ a single-criterion optimization strategy, which frequently falls short of generating effective clustering solutions across diverse datasets exhibiting various clustering characteristics. To address this challenge, the multi-objective meta-heuristic approach is employed to achieve optimal clustering outcomes by maximizing or minimizing multiple objective functions. Balancing exploitation and exploration is a crucial aspect of the meta-heuristic approaches. To enhance this balance, we propose a Multi-objective Firefly Differential Jaya (MFDJ) evolutionary algorithm. MFDJ enhances the quest for optimal clustering by improving the equilibrium between exploitation and exploration. We evaluate MFDJ on some text datasets. As the experimental results show, the MFDJ algorithm outperforms new document clustering algorithms.
Secure data sharing on the industrial Internet: a decentralized scheme based on CP-ABE

With the rapid development of the Industrial Internet, higher challenges are posed to the data security and privacy protection of traditional industrial Internet data sharing. At the present stage, the industrial Internet field has problems such as the lack of flexibility in access control, the difficulty in ensuring data availability, and the inability of data from different enterprises to circulate efficiently and securely, which hinders the development of the industrial Internet. In response to these challenges, a decentralized data-sharing solution based on CP-ABE is proposed to achieve fine-grained access control of industrial Internet data. To address issues related to centralized data storage and single points of failure, this solution combines distributed storage systems to enable distributed storage under the original data ciphertext and sharing of key ciphertext information chains, suitable for a decentralized industrial Internet data sharing architecture. To achieve the "availability and invisibility" of industrial Internet data, smart contracts and zero-knowledge proof technology are utilized to separate data ownership and data usage rights. Finally, security analysis and simulation experiments are conducted to evaluate the proposed scheme. The results demonstrate that the proposed solution effectively achieves fine-grained data access control while ensuring data security, and integrity, and meeting the increasing demands for industrial Internet data sharing efficiently.
Maximal Extractable Value in Blockchains: Current Trends

This paper delves into the prevailing trends surrounding Maximal Extractable Value (MEV) within blockchain networks. Our objective is to furnish an incisive overview of the MEV challenge and to elucidate salient developments in MEV extraction. Such insights aim to inform and influence prospective research trajectories in this domain.
Representativeness of Sample Statistics in Solution of a Stochastic Two-Criteria Control Problem

A criterion for estimating the representativeness of sample data is proposed in solving one stochastic problem of quality management in the presence of an integral constraint on the volume of products received by the end of a given planning period. The problem is included in the class of feedback control problems, with a distinctive feature that the feedback depth is considered as a function of time associated with the empirical distribution function of the external perturbation factor. On the basis of A.N. Kolmogorov sample statistics is refined to a constant factor, some approximation of the dependence of the representativeness of the data on the sample size. A simple example is given to illustrate the order of the solution.
Structural Elements of Knowledge-Oriented Information Exchange in the Universities

There are considered theoretical approaches for the implementation of a web-based learning environment of higher education institutions as a type of online-based learning environment and network surroundings, which provides registered participants of virtual learning communities with access to the functions of knowledge-oriented information exchange using the latest social software communication tools. The central element of the study was obtaining new results regarding to the web-based learning environment and the relevance of the contexts of its functioning. Approaches based on virtual communities and knowledge networks are singled out as basic theoretical approaches. The role of virtual learning communities is substantiated and their main constructive elements for implementation in the web-based learning environment of the university were given. It was determined that virtual communications are key elements in the processes of data and knowledge exchange in universities. In the study, learning communication is understood as the processes of data and knowledge exchange between members of the learning community within learning platforms and university learning environments.
Can Generative AI Eliminate Speech Harms? A Study on Detection of Abusive and Hate Speech during the COVID-19 Pandemic

This study investigates the issue of abusive and hateful language arising from the COVID-19 pandemic, this research employs machine learning techniques to establish a system capable of detecting and rephrasing abusive and hateful language. To begin, a dataset and dictionary specific to abusive and hateful language in Chinese. Subsequently, a two-stage detection model is proposed, with the BERT model yielding the most optimal outcomes. The first stage attains an accuracy of 94.42%, while the second stage achieves an accuracy of 81.48%.
A Deep CNN Model Based Ensemble Approach for Semantic and Instance Segmentation of Indoor Environment

Image segmentation has emerged as a popular and challenging task in computer vision. Deep learning methods have demonstrated significant success in this domain. In this paper, we propose a deep ensemble model for semantic segmentation of indoor scenes. Our ensemble model combines three distinct deep learning models to enhance both diversity and accuracy. Additionally, we customize YOLOv8 for instance-level segmentation of indoor environments. A comprehensive set of experiments on the publicly available data set demonstrates that our model achieves the highest IoU compared to state-of-the-art deep learning models.
AgeNet-AT: An End-to-End Model for Robust Joint Speaker Age Estimation and Gender Recognition Based on Attention Mechanism and Titanet

The potential uses of speaker age estimation in a variety of domains, such as forensics and human-computer interaction, have made it popular in recent years. However, the performance of the implemented techniques is significantly influenced by the noise and utterance length. In this study, the AgeNet-AT, which is based on Titanet model and attention mechanism, is proposed as a reliable age estimation and gender recognition model. To develop an end-to-end architecture for age estimation, the suggested method uses Titanet as the embedding extractor and an attention mechanism to determine the importance of each feature in the embedding vector. It is hypothesized that certain of Titanet’s extracted features may include characteristics that can distinguish between speakers of different ages and genders because Titanet is a model made to discriminate between different speaker identities. Titanet was therefore selected for this study’s embedding extractor. To further concentrate on the most important features for age estimate, an attention layer is employed. In addition, gender classification is included in the model as an auxiliary task to enhance estimation performance. On the TIMIT dataset, trials are run under multiple assessment conditions, such as variable utterance lengths and noise levels. The AgeNet-AT model’s robustness is demonstrated by the experimental findings. With Root Mean Square Error (RMSE) of 5.92 and 6.85 and Mean Absolute Error (MAE) of 4.30 and 4.73 for male and female speakers, respectively, the model exceeded the most recent age estimation results on the TIMIT dataset.
Privacy Protection Smart Agriculture Based on Functional Encryption*

Smart agriculture provides a new path for the development of modern agriculture and is a feasible solution to the food problem. In the process of intelligence, data has become a critical factor in the smooth operation of smart agricultural systems. This paper proposes a supervised agricultural data circulation model and corresponding threat model. Then, the security interaction protocol of the primary data in the data circulation model was designed by using the function encryption to ensure data security and privacy during the operation of the data circulation model. The data circulation model in this paper includes three portions: agricultural research and supervision, agricultural production, and third-party service acquisition. In the first stage, the regulator is responsible for receiving the amount of resources the farm consumes. In the second stage, the service provider calculates the service fee the innovative farm should pay, and the agricultural regulatory authority distributes the key to ensure market stability. At the same time, we analyze the security of the data circulation model and prove through simulation experiments that the running time and ciphertext size of the protocol algorithm are acceptable under the security requirements.
Convolutional Neural Network Model Architecture for Rice Leaf Digital Image Identification

Rice is one of the most important staple crops in the world. Nutrition is one of the crucial things in the growth and development of rice plants. This study aims to classify the nutritional needs of rice plant leaves and determine the accuracy of the convolutional neural network (CNN) algorithm in determining nutrition in rice plants. The architectural models used in this study are VGG16, MobileNet, and Xception, using Jupyter and Google Colaboratory as tools. In this study, a dataset of 1190 images of rice leaves was used. The rice leaf image is divided into two classes, Adequate and less tested, with a ratio of 80%, training data and 10% test data, and 10% as validation data. The best accuracy results were obtained by VGG16 at 78.15% and 76.47%, MobileNet at 82.69% and 86.55%, and Xception at 82.33% and 88.24%. Meanwhile, overall, the best accuracy results were obtained from the Xception model of 88.24% with an input batch size of 32, and the tools used were Jupyter Notebook.
An Approximation Cascade Scheme via Rational Fractions for Biomedical Data Analysis

The accuracy of solving the approximation tasks in the case of analysis of large volumes of tabular biomedical datasets by machine learning methods is not always high. This is explained by the complex, non-linear dependencies inside a multi-parametric dataset of a large volume, which combines medical-biological and engineering-technical features. The task of increasing the accuracy of the machine learning algorithm, ANN, or a certain mathematical model, in particular for the case when they are a “black box” during the intellectual analysis of such data, is urgent. The paper proposed a new two-step method for the refinement of regression model parameters (a direct approach). It is based on the principles of approximation by rational fractions, which have many advantages compared to other known methods. Simulation of the developed method is implemented as a cascade of two machine learning algorithms. Experimental studies were carried out based on SGD and extended-input SGTM neural-like structure using a real-world large biomedical dataset. We show a significant increase in the approximation accuracy of the designed method in comparison with several known methods, including cascades.
New Heuristics Method for Malicious URLs Detection Using Machine Learning

In order to carry out most of the social engineering attacks, like phishing, one of the first steps is to fool users with malicious links or Uniform Resource Locators (URLs) that are harmful. As phishing is often reported to be one of the top most used attacks, Malicious URL detection is becoming a hot research topic in which several scientific investigations and detection techniques are evaluated and used. Particularly, with the help of machine learning (ML) techniques, network security controls are able to detect malicious URLs with higher accuracy. However, with the complexity of attacks, malicious URLs have now too many attributes to handle which increases the complexity of the problem. Thus, an accurate yet fast detection mechanism is important at an early phase. In this paper, we propose a new heuristic method for malicious URL detection using very few URL attributes. To accomplish this, we first compare different ML algorithms in an attempt to identify the best method for classifying malicious URLs. Then, using the best ML method in detecting malicious URLs, we try to discover the most important URL attributes that can contribute to the classification process, using feature selection methods. Out of these selected attributes, our new method relies on simple heuristic rules set on few selected ones. Our method provides comparable accuracy levels but with faster classification. To validate results, we conducted our tests different URL datasets.
Combining GAN with Other Machine Learning Techniques for Improving Customs Fraud Detection

The detection and prevention of customs fraud are of paramount importance for maintaining the integrity and security of international trade. With the increasing sophistication of fraudulent activities, there is a growing need for innovative and effective approaches to detect fraudulent behavior. This thesis explores the combination of Generative Adversarial Networks (GANs) with other machine learning techniques to enhance customs fraud detection. We leverage the power of Generative Adversarial Networks (GANs) to generate realistic synthetic data that closely resembles the original customs data. By learning the underlying data distribution, GANs enable us to create a large volume of diverse and representative synthetic data, including both genuine and fraudulent cases.
The Chi-square Test and Data Clustering Combined for Author Identification

A novel approach in this research is to use a combination of a classical method - the chi-square test and a machine learning technique - data clustering for revealing statistically significant differences in word length distribution and word distribution of authorial styles. The chi-square test has been tested for differentiating texts of different historical periods. The research has proved that at 0.5; 0.15; 0.1; 0.05; 0.025; 0.01; 0.001 significance levels the differences between literary texts “Robinson Crusoe” by Daniel Defoe (XVIII th century) and “Me before You” by Pauline Sara Jo Moyes (XXI th century) are statistically significant. The calculations have been done in Python. The level of test validity is 95%. The author identification is based on word length distribution. The use of data clustering technique is also efficient for authorial styles identification. The results of the research have shown that the authorial style is similar in literary works “The Furnished Room” and “The Last Leaf” by 0. Henry. The research may be practically applied in judiciary.
Analysis of the Use of Methods and Feature Groups for Author Recognition on the Example of Texts in the Azerbaijani Language

In the study, the problem of authorship recognition of texts in the example of fiction literature works in the Azerbaijani language is considered. Based on the results of computer experiments for use in an author recognition computer system, the recognition effectiveness of using different text features groups with different methods and models of machine learning was evaluated.
Extracting Drug Names From Medical Reports

In this paper, we present new results on drug names and dosage extraction from text-format medical reports written in a low-resource language. We locate and collect drug dosage information in continuous texts to enable the statistical processing of this information. As a first step, we use rule-based text-processing methods to gain a base model and generate learning data for language model creation. This rule-based annotation part component includes tokenization, tagging, and chunk parsing of tags, using domain-specific grammar to locate and connect related tokens. Then, to improve the extraction of the drug and dosage-related text elements, we introduce a semi-supervised learning process to train a BERT-based language model. This deep learning-based approach is able to handle the rigidity presented in the data to be processed. To evaluate our approach, we consider several standard performance metrics. Based on our experiments, which show a 3.5% improvement in extraction, we conclude that these combinations of the rule-based and deep learning-based natural language processing approaches are a valid approach for this task to improve the recognition of the drug and dosage entities in medical texts.