Reviewerly/exHarmony huggingface dataset:
VGG16 Deep Learning Architecture Using Imbalance Data Methods For The Detection Of Apple Leaf Diseases

Data in the real world, there are many conditions (situations) where the number of instances in one class is much less than the number of instances in other classes. This situation is a problem in unbalanced datasets (imbalance class). As a result, performance in classification will decrease in some data systems. In this study, it was identified that the apple leaf disease performance dataset used had a large enough data imbalance problem where the comparison between instances was 1:5, so an oversampling method was needed to solve the data imbalance problem. Methods that can be used include the Synthetic Minority Over Sampling Technique (SMOTE). In order to validate the effectiveness of the proposed model, two experimental scenarios were carried out: first, the VGG16 algorithm was directly applied to modeling without considering class imbalance by reducing the number of layers and kernels in each layer to achieve optimal results, second, over-sampling SMOTE to increase the number of balanced datasets. The results showed that using the confusion matrix the accuracy results for each method were obtained where VGG 16 scored 85.16%, VGG 16 with SMOTE scored 92.94%. The conclusion of this study is that SMOTE helps improve the accuracy of leaf disease detection in apples.
A dataset of abusive comments on the Nigerian web

As the online comments keep growing in an exponential manner, the need for effective filtering or detecting abusive language in generated web textual content becomes increasingly important.In addition, the geometric increase in the textual content of the web has made the use of methods like word-bag and pattern matching for detection of hate speech less effective.The purpose of this work is to develop a corpus of online users' comments annotated for abusive language and develop a classification machine learning based model to detect hate speech on the Nigerian web.The model is evaluated on real-time comments and in different settings to further increase the learning rate of our model.
Implementation of adaptive image contrast enhancement technique for Image Processing Applications

An automatic and objective assessment of image quality is important in processing of imaging data.To magnify the consumed medicine for simpler X-ray interpretation Digital Image Processing Techniques can be used.In this paper, the Implementation of adaptive image contrast enhancement technique for image processing applications is done.The main objective of the project is to produce image processing algorithm which can enhance the contrast information efficiently.The adaptive technique of selection for contrast has become effective in image processing applications.However, many of the imaging parameters are weighted towards late stage that allow detection such as passive contrast improvement across a compromised areas.
Variational quantum simulation of long-range interacting systems

Abstract Current quantum simulators suffer from multiple limitations such as short coherence time, noisy operations, faulty readout and restricted qubit connectivity in some platforms. Variational quantum algorithms are the most promising approach in near-term quantum simulation to achieve practical quantum advantage over classical computers. Here, we explore variational quantum algorithms, with different levels of qubit connectivity, for digital simulation of the ground state of long-range interacting systems as well as generation of spin squeezed states. We find that as the interaction becomes more long-ranged, the variational algorithms become less efficient, achieving lower fidelity and demanding more optimization iterations. In particular, when the system is near its criticality the efficiency is even lower. Increasing the connectivity between distant qubits improves the results, even with less quantum and classical resources. Our results show that by mixing circuit layers with different levels of connectivity one can sensibly improve the performance. Interestingly, the order of layers becomes very important and grouping the layers with long-distance connectivity at the beginning of the circuit outperforms other permutations. The same design of circuits can also be used to variationally produce spin squeezed states, as a resource for quantum metrology.
A Analysis of Information System Audit Using Control Objectives for Information and Related Technology 5 Framework on Permata Hebat Application

Permata Hebat application is an application created as a service to develop micro businesses among housewifes in Semarang City. However, to fulfill this expectation, of course, the application needs good IT management or governance, so that the application can be optimally utilized by its users. However, since its operation on March 23, 2021, it is not yet known how the quality or level of management capability or IT governance services run by the organization. Information system audit itself is an activity to evaluate and ensure that the system has met the standards. Meanwhile, one of the frameworks that can be used to conduct an audit is COBIT 5. COBIT 5 is a good practice whose processes have been adapted to current standards. As for the process control used is the Deliver, Services, and Support (DSS) domain. The results of the calculation show that for domains DSS01, DSS03, and DSS06 each received a maturity level value of 0.60, 0.52, and 0.61 or at level 1 performed. Meanwhile, domains DSS02, DSS04, and DSS05 each received maturity level values of 0.45, 0.35, and 0.42 or are still at level 0 incomplete. Therefore, there is still a need for a lot of improvement or improvement in each process. The goal is that the system can run in accordance with organizational expectations.
Quantum AI simulator using a hybrid CPU–FPGA approach

The quantum kernel method has attracted considerable attention in the field of quantum machine learning. However, exploring the applicability of quantum kernels in more realistic settings has been hindered by the number of physical qubits current noisy quantum computers have, thereby limiting the number of features encoded for quantum kernels. Hence, there is a need for an efficient, application-specific simulator for quantum computing by using classical technology. Here we focus on quantum kernels empirically designed for image classification and demonstrate a field programmable gate arrays (FPGA) implementation. We show that the quantum kernel estimation by our heterogeneous CPU-FPGA computing is 470 times faster than that by a conventional CPU implementation. The co-design of our application-specific quantum kernel and its efficient FPGA implementation enabled us to perform one of the largest numerical simulations of a gate-based quantum kernel in terms of features, up to 780-dimensional features. We apply our quantum kernel to classification tasks using the Fashion-MNIST dataset and show that our quantum kernel is comparable to Gaussian kernels with the optimized hyperparameter.
Federated learning with superquantile aggregation for heterogeneous data

We propose a federated learning framework to handle heterogeneous client devices which do not conform to the population data distribution. The approach hinges upon a parameterized superquantile-based objective, where the parameter ranges over levels of conformity. We present an optimization algorithm and establish its convergence to a stationary point. We show how to practically implement it using secure aggregation by interleaving iterations of the usual federated averaging method with device filtering. We conclude with numerical experiments on neural networks as well as linear models on tasks from computer vision and natural language processing.
Feasibility Study of Federated Learning on the Distributed Research Network of OMOP Common Data Model

Since protecting patients' privacy is a major concern in clinical research, there has been a growing need for privacy-preserving data analysis platforms. For this purpose, a federated learning (FL) method based on the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) was implemented, and its feasibility was demonstrated.We implemented an FL platform on FeederNet, which is a distributed clinical data analysis platform based on the OMOP CDM in Korea. We trained it through an artificial neural network (ANN) using data from patients who received steroid prescriptions or injections, with the aim of predicting the occurrence of side effects depending on the prescribed dose. The ANN was trained using the FL platform with the OMOP CDMs of Kyung Hee University Medical Center (KHMC) and Ajou University Hospital (AUH).The area under the receiver operating characteristic curves (AUROCs) for predicting bone fracture, osteonecrosis, and osteoporosis using only data from each hospital were 0.8426, 0.6920, and 0.7727 for KHMC and 0.7891, 0.7049, and 0.7544 for AUH, respectively. In contrast, when using FL, the corresponding AUROCs were 0.8260, 0.7001, and 0.7928 for KHMC and 0.7912, 0.8076, and 0.7441 for AUH, respectively. In particular, FL led to a 14% improvement in performance for osteonecrosis at AUH.FL can be performed with the OMOP CDM, and FL often shows better performance than using only a single institution's data. Therefore, research using OMOP CDM has been expanded from statistical analysis to machine learning so that researchers can conduct more diverse research.
The trilateral game of privacy perception, financial regulation and central bank digital currency issuance

The goal of this study is to clarify how privacy protection affects the insurance of central bank digital currency (CBDC). By constructing a tripartite game model involving consumers, commercial banks, and regulators, this paper explores the impact of privacy protection on the issuance of CBDC. The findings show that privacy protection is critical to ensuring successful adoption of CBDCs. The central bank must strike a balance between protecting user privacy while also regulating usage and ensuring convenience for users. However, due to opportunistic behavior by both commercial banks and consumers during this process, negative reactions are possible. Based on the findings of this research, it is suggested that central banks should encourage commercial banks to participate in CBDC issuance by promoting appropriate data sharing and offering guidance. Additionally, they should focus on consumer education and expectation management to promote CBDC adoption. Commercial banks must also embrace digital transformation and adapt to the changing financial landscape to remain competitive while providing innovative financial services to customers.
Comparative Performance Evaluation of State-of-the-Art Hyperparameter Optimization Frameworks

Machine learning (ML) has proven to be highly effective in solving complex problems in various domains, thanks to its ability to identify specific data tasks, perform feature engineering, and learn quickly. However, designing and training ML models is a complicated task and requires optimization. The effectiveness of ML models is highly dependent on the selection of hyperparameters that determines their performance. Hyperparameter optimization (HPO) is a systematic search process to find the optimal combinations of hyperparameters to achieve robust performance. Traditional HPO methods such as grid and random search take a lot of computing time when used in large-scale applications. Recently, various automated search strategies, such as Bayesian optimization (BO) and evolutionary algorithms, have been developed to significantly reduce the computing time. In this paper, we use state-of-the-art HPO frameworks, namely BO, Optuna, HyperOpt, and Keras Tuner, for optimizing the ML and deep learning models for the classification tasks and evaluate their comparative performance using two different sets of experiments. The first one uses different ML classifiers to solve the optimal parameter selection problem with HPO. The second one attempts to optimize the convolutional neural network (CNN) architecture using HPO frameworks to improve its performance in the image classification task. We use four publicly available real-world datasets including one image dataset. The experimental results show that HyperOpt - TPE outperforms the other HPO frameworks for the ML classifiers and achieves up to 94.12% of accuracy with 30 minutes for performing the optimization. Similarly, for the CNN model, HyperOpt-TPE outperforms the other HPO frameworks by improving 34% of the classification accuracy, while taking 2 hours and 24 minutes of computing time.
Marine Robotics: An Improved Algorithm for Object Detection Underwater

The visibility of items in water is lower than that of those on land. Light waves from a source don't have enough time to reach an item before it vanishes beneath the surface because light waves in water travel more quickly than they do in air. As a result, it can be challenging for people to deal with water properly due to certain of its physical characteristics. In light of this, object detection underwater has a wide range of uses, including environmental monitoring, surveillance, search and rescue, and navigation. This might enhance the precision, efficiency, and safety of undersea activities. In light of the aforementioned, this paper presents an algorithm for detecting objects underwater using YOLOv5. The algorithm has been improved by changing the way YOLOv5 works, which makes it better at detecting small objects. We tested our algorithm and found that it is more accurate than the original YOLOv5 algorithm.
Proposed Smart 5G Framework: Incorporating Federated Learning and Transfer Learning

&lt;p&gt;This research aims to develop a secure and intelligent framework for 5G networks by incorporating federated learning (FL)&lt;br&gt; and transfer learning (TL) strategies. The primary objective is to enhance network evaluation metrics, such as capacity, service rate,&lt;br&gt; privacy preservation, low latency, and energy consumption, in the selection of access networks. The proposed framework will tackle&lt;br&gt; existing challenges in wireless communication systems, such as mobility, limited bandwidth, energy constraints, and limited feedback&lt;br&gt; from a receiver to a transmitter. The secondary objective is to address privacy preservation and scalability concerns during user&lt;br&gt; authentication in 5G networks. The federated user authentication model leverages the privacy preservation benefits of FL and secure&lt;br&gt; aggregation protocols during model averaging. The research methodology consists of five stages: literature review, classification of&lt;br&gt; objectives, determination of state metrics, definition of evaluation functions, and selection of FL and RL techniques. The resulting&lt;br&gt; framework is expected to provide a robust, secure, and efficient solution for 5G networks, ensuring enhanced quality of service and&lt;br&gt; optimization &lt;/p&gt;
Access to full-text documents in libraries via Sci-Hub: a blessing in disguise to library users

Purpose The main purpose of this paper is to explore access to full-text documents in libraries through Sci-Hub and consider whether it is a blessing in disguise to library users. Findings Sci-Hub has been a valuable resource for accessing full-text documents. Sci-Hub is potentially problematic from a legal standpoint but has provided a “blessing in disguise” to library users who lack access to academic resources. Sci-Hub has gained significant popularity among scholars and researchers; it has democratized access to academic research. The ability to access scholarly publications for free has provided an important avenue for research and has allowed for greater efficiency and effectiveness in research projects. The legality of the service, concerns about the quality and accountability of the available materials, and the unclear long-term sustainability of the service are some of the challenges associated with using Sci-Hub in libraries. Originality/value This paper highlights how the Sci-Hub improves access to full-text scholarly publications for library users. The blessings and challenges associated with using Sci-Hub in libraries and recommendations are discussed.
Classifying toxicity in the Arabic Moroccan dialect on Instagram: a machine and deep learning approach

People crave interaction and connection with other people. Therefore, social media became the center of society’s life. Among the brightest social media platforms nowadays with a massive number of daily users there is Instagram, which is due to its distinctive features. The excessive revealing of personal life has put users in the spots of getting bullied and harassed and getting toxic revues from other users. Numerous studies have targeted social media to fight its harmful side effects. Nevertheless, most of the datasets that were already available were in English, the Arabic Moroccan dialect ones were not. In this work, the Arabic Moroccan dialect dataset has been extracted from the Instagram platform. Furthermore, feature extraction techniques have been applied to the collected dataset to increase classification accuracy. Afterward, we developed models using machine learning and deep learning algorithms to detect and classify toxicity. For the models’ evaluation, we have used the most used metrics: accuracy, precision, F1-score, and recall. The experimental results gave modest scores of around 70% to 83%. These results imply that the models need improvement due to the lack of available datasets and the preprocessing libraries to handle the Moroccan dialect of Arabic.
Controlled Hand Gestures using Python and OpenCV

Abstract: Due to its communal nature, gesture recognition has been used in recent trends to develop machines. Gestures are a type of verbal communication that allows humans and computers to communicate with one another. Artificial intelligence makes heavy use of hand gesture detection to improve functionality and person commerce. Then, to carry out particular tasks, we used counterplotted action dyads and some Python libraries (OpenCV, cvzone) that aid in image capture, pre-processing, and discovery
IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing

The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across cloud networks. Here, we propose Generative Adversarial Networks (GANs) method for predicting secure data transmission in IoT-based systems using cloud computing. We evaluated our model’s attainment on the UNSW-NB15 dataset and contrasted it with other machine-learning (ML) methods, comprising decision trees (DT), random forests, and support vector machines (SVM). The outcomes demonstrate that our suggested GANs model performed better than expected in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The GANs model generates a 98.07% accuracy rate for the testing dataset with a precision score of 98.45%, a recall score of 98.19%, an F1 score of 98.32%, and an AUC-ROC value of 0.998. These outcomes show how well our suggested GANs model predicts secure data transmission in cloud-based IoT-based systems, which is a crucial step in guaranteeing the confidentiality of IoT networks.
Detecting the Anti-Social Activity on Twitter using EGBDT with BCM

The rise of social media and its consequences is a hot topic on research platforms. Twitter has drawn the attention of the research community in recent years due to various qualities it possesses. They include Twitter's open nature, which, unlike other platforms, allows visitors to see posts posted by Twitter users without having to register. In twitter the sentiment analysis of tweets are used for detecting the anti-social activity event which is one of the challenging tasks in existing works. There are many classification algorithms are used to detect the anti-social activities but they obtains less accuracy. The EGBDT (Enhanced Gradient-Boosted Decision Tree) is used to optimize the best features from the NSD dataset and it is given as input to BCM (Bayesian Certainty Method) for detecting the anti-social activities. In this work, tweets from NSD dataset are used for analyzing the sentiment polarity i.e. positive or negative. The efficiency of the proposed work is compared with SVM, KNN and C4.5. From this analysis the proposed EGBDT and BCM obtained better results than other techniques.
gamified environment supported by augmented reality for improving communicative competencies in students with ASD: design and validation

The objective of this study was to design and validate an Augmented Gamified Environment (AGE) for improving communicative skills in students with ASD. The study used a mixed methodology: a) qualitative, focused on a single case to describe the AGE design process. The environment uses a pirate adventure story which includes gamified activities supported by augmented reality. It promotes immersive learning through activities that encourage social, linguistic, and cognitive skills linked to symbolic play; and b) quantitative, based on the Delphi method in which experts (N=12) evaluated the environment using 14 indicators in four dimensions: adaptability to the pace of learning, suitability of the base and code for presenting the content, didactic potential for developing competence, and the suitability of the gamification process for students with ASD. The expert assessment helped to improve the adaptability, flexibility, and functionality of the AGE, which incorporated versatile and augmented reality activities and applications to provide the student with playful linguistic and socio-emotional development in an immersive environment.
Novel Heuristic Recurrent Neural Network Framework to Handle Automatic Telugu Text Categorization from Handwritten Text Image

In the near future, the digitization and processing of the current paper documents describe efficient role in the creation of a paperless environment. Deep learning techniques for handwritten recognition have been extensively studied by various researchers. Deep neural networks can be trained quickly thanks to a lot of data and other algorithmic advancements. Various methods for extracting text from handwritten manuscripts have been developed in literature. To extract features from written Telugu Text image having some other neural network approaches like convolution neural network (CNN), recurrent neural networks (RNN), long short-term memory (LSTM). Different deep learning related approaches are widely used to identification of handwritten Telugu Text; various techniques are used in literature for the identification of Telugu Text from documents. For automatic identification of Telugu written script efficiently to eliminate noise and other semantic features present in Telugu Text, in this paper, proposes Novel Heuristic Advanced Neural Network based Telugu Text Categorization Model (NHANNTCM) based on sequence-to-sequence feature extraction procedure. Proposed approach extracts the features using RNN and then represents Telugu Text in sequence-to-sequence format for the identification advanced neural network performs both encoding and decoding to identify and explore visual features from sequence of Telugu Text in input data. The classification accuracy rates for Telugu words, Telugu numerals, Telugu characters, Telugu sentences, and the corresponding Telugu sentences were 99.66%, 93.63%, 91.36%, 99.05%, and 97.73% consequently. Experimental evaluation describe extracted with revealed which are textured i.e. TENG shown considerable operations in applications such as private information protection, security defense, and personal handwriting signature identification.