Reviewerly/exHarmony huggingface dataset:
Comparing Automated Machine Learning Against an Off-the-Shelf Pattern-Based Classifier in a Class Imbalance Problem: Predicting University Dropout

When facing a classification problem, data science practitioners must search through an armoury of methods. Often, practitioners are tempted to use off-the-shelf classifiers, including automated Machine Learning toolboxes; however, stand-alone classifiers are not applicable to any problem and automated Machine Learning may be time-consuming raising up environment-ethical issues. To magnify the problem, (commercial) automated Machine Learning toolboxes are black and practitioners are not allowed to extend them with new methods to improve their classification performance. In this paper, we present a case study, student dropout prediction, which most off-the-shelf classifiers find difficult to solve due to the problem’s inherent class imbalance. We shall see that MS Azure’s autoML outperforms a number of popular, stand-alone classifiers; yet, multivariate PBC4cip, an off-the-shelf classifier especially designed to deal with class imbalance, yields results that are just as good as MS Azure’s autoML. Our studies show that data science practitioners need to build themselves a taxonomy of classification mechanisms in terms of the properties of the problem to solve; additionally, autoML platforms should let scientists modify the armoury of classifiers and provide an explanation of both mechanism selection and mechanism tunning so that practitioners learn further lessons.
Impact of Decentralization on Electronic Voting Systems: A Systematic Literature Survey

The modernization of voting methods is a dynamic area of research currently. In the past, innovation in voting methodswas limited to the automation of steps in the process through mechanical means. This changed with the introduction of commercial cryptography in the 1970s, whose applications to voting triggered a new era in this research field. Researchers used the following years to apply tools derived from cryptographic methods to build increasingly secure, transparent, and practical electronic voting systems. Despite the effort, a true remote electronic voting system was never achieved with the technology available. The introduction of Bitcoin in 2009 brought much attention to the blockchain concept that supported it. This new data model offered new levels of transparency, data immutability, and pseudo-anonymity that made it attractive and useful to e-voting researchers. Soon after, articles detailing the first blockchain-based e-voting systems were published, and the research field entered a new era. This article presents a study on the evolution of research in electronic voting systems, following a systematic literature review methodology and a chronological evolution from the first systems that employed public cryptographic concepts up to blockchain-based proposals, with the objective of detailing the evolution of the technology as a whole, as well as all the elements, centralised and decentralised, created and used to implement voting systems.
New hybrid deep learning models to predict cost from healthcare providers in smart hospitals

Accurate cost prediction of healthcare resources is challenging as diverse factors affect the overall prediction. The cost of healthcare providers is increasing exponentially as different healthcare providers charge differently for the same service due to various factors, majorly the sky rocketing inflation and increased population. It increases the importance of predicting healthcare costs to avoid unpleasant surprises. This study aims to provide the expected cost of healthcare providers that helps the patients in resource allocation and strengthens decision-making according to their resources. This paper proposes three hybrid Deep Learning (DL) models, Visual Geometry Group and Stacked Autoencoder (VGG-SAE), Visual Geometry Group and Deep Neural Network (VGG-DNN), and Stacked Autoencoder and Deep Neural Network (SAE-DNN), which optimize learning the hidden patterns from the given data more efficiently than individual models. The three hybrid DL models estimate the cost of healthcare providers effectively. The preprocessing is performed using the mode imputation for handling the missing values, Z-score for removing the outliers and standard scaler for standardizing the data. To train the hybrid models on optimum parameters, the Random Search technique is used that provides the best hyper-parameters of each hybrid model. The interpretation of the hybrid models’ output is achieved using the SHapley Additive ExPlanations (SHAP) technique. The performances of VGG-SAE, VGG-DNN, and SAE-DNN are compared with the baseline DL models such as SAE, DNN, and VGG. To assess the robustness of the proposed approach, the hybrid models are trained on two different datasets of healthcare such as Healthcare Providers and Hospital Inpatient Cost Transparency. With the hyper-parameter tuning of the Healthcare Providers Dataset, VGG-SAE achieved MSE of 0.01, RMSE of 0.13, MAE of 0.02, and R-squared of 0.98. VGG-DNN achieved MSE of 0.01, RMSE of 0.12, MAE of 0.02, and R-squared of 0.99. SAE-DNN achieved MSE of 0.01, RMSE of 0.11, MAE of 0.02, and R-squared of 0.99. With the hyper-parameter tuning of the Hospital Inpatient Cost Transparency Dataset, VGG-SAE achieved MSE of 0.007, RMSE of 0.08, MAE of 0.03, R-squared of 0.99, and execution time of 1680 seconds. VGG-DNN achieved MSE of 0.0006, RMSE of 0.08, MAE of 0.03, R-squared of 0.99, and execution time of 645 seconds. SAE-DNN achieved MSE of 0.003, RMSE of 0.06, MAE of 0.02, R-squared of 0.99, and execution time of 850 seconds. Our proposed hybrid combinations outperformed other deep models and Machine Learning (ML) techniques such as SAE, DNN, VGG, SVR and GBR, which ensures high efficiency of the proposed models in terms of healthcare providers cost.
A Comparative Study of Sparsity Promoting Techniques in Neural Network for Modeling Non-Linear Dynamics

Sparsity-promoting techniques are showing promising results in improving the generalization of neural networks. However, the literature contains limited information on how different sparsity techniques affect generalization when using neural networks to model non-linear dynamical systems. This study examines the use of sparsity-enhancing techniques to improve the accuracy and reduce the divergence rate of neural networks used to simulate such systems. A range of sparsity methods, including hard and soft thresholding, pruning and regrowing, and L1-regularization, were applied to neural networks and evaluated in a complex nonlinear aluminum extraction process. The results showed that the most effective technique was L1 regularization, which enhanced the important connections in the network and improved the model performance. In contrast, many of the more advanced sparsity techniques resulted in significantly worse performance and higher divergence rates. Additionally, the application of Stochastic Weight Averaging during training increased performance and reduced the number of diverging simulations. These findings suggest that carefully selecting the right sparsity techniques and model structures can improve the performance of neural network-based simulations of dynamical systems.
SegTex: A Large Scale Synthetic Face Dataset for Face Recognition

Face recognition remains challenged by data limitations in both scale and diversity, coupled with the ethical dilemmas of using images without the subjects’ consent. To address these issues, this paper presents the SegTex framework, a cutting-edge method for generating synthetic face datasets by converting Segmentation maps into Textured images. Using the CelebAHQ-Mask dataset for segmentation maps and extracting facial features from the CelebAMask-HQ dataset, the SegTex method efficiently creates varied synthetic facial characteristics. This approach not only sidesteps the need for real-world data collection but also offers a rich and diverse dataset, essential for improving face recognition algorithm performance. In our experiments, models trained on the SegTex-generated dataset displayed superior performance metrics when compared to those trained on conventional datasets, underscoring the practical utility of our method. This robust performance, combined with the ethical advantages of synthetic data generation, ensures our approach holds significant importance in the field of face recognition.
Robust Arabic and Pashto Text Detection in Camera-Captured Documents using Deep Learning Techniques

In the realm of Document Image Analysis (DIA), the primary objective is to transform image data into a format that can be readily interpreted by machines. Within a DIA-based system, layout analysis plays a crucial role in pre-processing, for the identification and extraction of precise and error-free textual segments. However, regarding the Pashto language, the document images are not explored so far. Pashto text detection in camera-captured documents is a challenging task due to variations in image quality, lighting conditions, complex backgrounds unavailability of labeled documents, cursiveness, shape-context dependency, multi scripts per image, and language-specific layouts. This research examines the case of Pashto and Arabic text and contributes in two aspects. First, it introduces the creation of a real dataset that contains 1080 images of the Pashto documents captured by a handheld camera. Second, this work examines deep learning based classifiers that can perform layout analysis tasks and detects Pashto and Arabic text per document. For the layout classification, we used deep learning models such as Single-Shot Detector (SSD), Yolov5 and Yolov7. A baseline results are achieved by examining 30% images as a test set and achieve a mean average precision (mAP) of 84.51% on SSD, 88.50% on Yolov5 and 91.30% on Yolov7 respectively. The proposed methods have the potential to contribute to various applications, such as document analysis, information retrieval, and translation, for Pashto and Arabic language users.
Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy Path

Cultural communication plays a vital role in social development and human interaction. Red culture, as an integral part of China’s revolutionary history and socialist construction, holds significant meaning and exerts a wide influence. However, in the era of information technology, effectively disseminating red culture and stimulating public interest and participation has become an urgent challenge. In this study, we use the advanced deep learning tech to explore the use of multimodal data fusion for enhancing the effectiveness and impact of red culture communication. Specifically, we extract text features and image features from users’ browsing information using BI-GRU and CNN, respectively. These features are then fused with user portraits to create a multi-source information fusion vector. Subsequently, we employ a BPNN (Backpropagation Neural Network) to perform user interest classification based on the fused features. Experimental results demonstrate that our proposed user recognition framework achieves an average recognition rate of 95.4% across three types of users, indicating high accuracy. Therefore, the user interest classification model, incorporating fused multi-features, presented in this paper offers a promising approach for future red culture communication, as well as user intelligent recommendation and analysis.
Encoding-Decoding-Based Distributed Fusion Filtering for Multi-Rate Nonlinear Systems With Sensor Resolutions

The paper investigates the distributed fusion filtering problem for time-varying multi-rate nonlinear systems (TVMRNSs) with sensor resolutions based on the encoding decoding scheme (EDS) over sensor networks, where the iterative method is applied to the transformation of TVMRNSs. In order to enhance signal interference-resistant capability and improve transmission efficiency, the EDS based on dynamic quantization is introduced during the measurement transmission. On the basis of the decoded measurements, a local distributed filter is constructed, where an upper bound on the local filtering error (LFE) covariance is derived and the local filter gains are obtained by minimizing the trace of the upper bound. Subsequently, the fusion filtering algorithm is presented according to the covariance intersection fusion criterion. In addition, a sufficient condition is provided via reasonable assumptions to ensure the uniform boundedness of the upper bound on the LFE covariance. Finally, a moving target tracking practical example is taken to show the superiority of the proposed filtering algorithm and discuss the monotonicity of the mean-square error of the fusion filter with respect to the sensor resolutions and quantization intervals.
Analysis of Underlying Emotions in Textual Data Using Sentiment Analysis Which Classifies Text In to Positive, Negative or Neutral Sentiments

Analysis of underlying emotions in textual data can provide important insights and has been the focus of much research. Sentiment analysis is a popular technique which classifies text into positive, negative or neutral sentiments based on polarity and subjectivity. But this fails to extract actual emotions represented by text. This can be achieved by analyzing the occurrence of particular words, symbols and emojis in text. For emotion classification, machine-learning and deep-learning techniques Peng et al.(2022) are also used. These models classify textual data into only a small subset of the broad spectrum of emotions. Subtle variations such as "disappointment" and "grief" are lost and text is simply classified into "sadness". Extracting such subtle emotions from texts present some unique problems as one single sentence may represent multiple related emotions. Thus we have explored various Deep learning models based on convolution Neural NetworkO’Shea and Nash (2015) and Long Short Term Memory Staudemeyer and Morris (2019) for classifying textual data into one or more of 27 different fine grained emotions.
Comparative Analysis of YOLOv4 and EfficientDet based models for Traffic Sign Detection in Autonomous Vehicles

Visual perception systems enable autonomous vehicles to make right driving decisions in dynamic driving environments. Traffic sign recognition is an important perception application, where accuracy and real-time execution are key requirements. Deep learning models like YOLO and RetinaNet have shown reasonably good accuracy and speed as detectors in traffic sign recognizers. With compound scaling, EfficientDet models of varying complexity and performance can be created for use in systems with different computational power. This paper presents a comparative analysis on the usability of YOLOv4 and EfficientDet models in the detection stage of traffic sign recognizers. Both models are trained and evaluated using GTSDB dataset. Comparative performance analysis of YOLOv4, EfficientDet model family and prior models like Faster-RCNN, YOLOv3 and RetinaNet, for traffic sign detection, is reported in this paper. YOLOv4 with a mAP of 97.7@9.18fps is found to be the most suitable one as traffic sign detectors in autonomous vehicles.
Evaluating the Effectiveness of a Facial Recognition-Based Attendance Management System in a Real-World Setting

Face recognition technology has been extensively used in multiple verticals of security, surveillance, and human-computer interaction. Conventional techniques including manual sign-ins, identity cards, or biometric verification have been used by traditional attendance systems. Face recognition systems have, however, become a popular way to track attendance, thanks to developments in computer vision and machine learning. The construction of an attendance registration application is the main topic of this research study, which also offers a thorough overview of facial recognition attendance systems. This study seeks to provide light on the benefits, drawbacks, and potential applications of these fast-developing technologies. Face recognition technology may be integrated into attendance systems to increase productivity, accuracy, and user comfort. However, issues like privacy worries and technological constraints must be resolved. With predicted future improvements in machine learning algorithms and hardware capabilities, face recognition attendance systems look to have a bright future. This research article adds to a deeper understanding and successful application of facial recognition technology in attendance systems by examining these features.
A Comparative Analysis of Traditional versus Blockchain-based Voting Systems

Voting holds immense importance in any democracy, serving as the bedrock of any country's democratic structure. It possesses the transformative potential to shape the nation’s future. However, the history of elections and the voting process has witnessed significant developments. Initially, paper ballots and laborious counting methods were employed, but they faced severe criticism due to fraudulent practices and booth capturing. To address the shortcomings of the paper ballot system, Electronic Voting Machines (EVMs) emerged as a technological advancement. EVMs addressed paper ballot drawbacks but concerns persist over hacking, fraud, software programming, manufacturer affiliations, machine security, and vote storage. An alternative to the prevalent EVM system is a blockchain-based voting system, although it has limitations. The primary objective of this paper is to present the working of traditional, EVM, and blockchain-based voting systems. Finally, the objective is to present a comparison between traditional, EVM, and blockchain-based voting systems.
A Novel Dense Generative Net Based on Satellite Remote Sensing Images for Vehicle Classification Under Foggy Weather Conditions

Accurate vehicle type classification plays a crucial role in the development of intelligent transportation systems. Recently, several deep learning models have been proposed to utilize satellite remote sensing images for vehicle type classification. However, conventional neural network models often have limitations when dealing with remote sensing images, such as adverse weather conditions, as well as the extremely low resolution of remote sensing images containing small objects like vehicles. To enhance the vehicle type classification capability in complex environments, this research develops a novel deep learning framework called Dense Generative Net (DGNet). DGNet consists of three components: feature layer, generation layer, and dense feature fusion layer. The feature layer employs large convolutions to establish a broader receptive field, enabling it to capture more effective global feature information. The generation layer is based on a super-resolution network, which is designed to generate high-resolution feature information. The dense feature fusion layer performs the final classification by integrating the outputs from two upstream branches, and combines the feature information obtained from the feature layer and the generated high-resolution features information from the generation layer, enabling comprehensive and robust classification of vehicle types. To evaluate recognition capability, vehicle data from multiple regions and diverse environmental conditions are utilized, including four different weather conditions. The experimental results demonstrate that DGNet exhibits remarkable vehicle type recognition capability, with minimal degradation even under heavy foggy weather conditions. The effectiveness of each module and its impact on the overall performance have been verified through ablation experiments.
EduVigil: Shaping the Future of Education with AI - An Intriguing Case Study

The rapid transition towards digital classrooms underlines the urgent need for advanced tools to optimize learning experiences. This paper presents EduVigil, a comprehensive multi-modal AI-based system that pioneers real-time monitoring of classrooms. Our approach uniquely leverages a custom dataset tailored to emulate a realistic classroom environment, featuring children in varying states of attention, pose, gesture, and presence. Utilizing this rich dataset, EduVigil incorporates a diverse set of features including facial recognition, attention tracking, pose and gesture recognition, attendance management, real-time alert system, sentiment analysis, automatic speech recognition, voice detection, behavior prediction, Chatbot integration, automatic summarization, and real-time translation. The display of these remarkable attributes is predicated on the deployment of state-of-the-art AI techniques. These include Convolutional Neural Networks (CNNs), a powerful tool for decoding visual data patterns; Long Short-Term Memory networks (LSTMs), specifically designed for scrutinizing sequential data; sophisticated pose estimation algorithms, engineered to accurately locate body positions; and the lightning-fast YOLO (You Only Look Once) algorithm, a crucial asset for swift object identification tasks. Additionally, the system employs sophisticated AI techniques including automatic speech recognition using models like Mozilla's DeepSpeech, real-time translation using the Transformer model, and automatic summarization inspired by Liu and Lapata's extractive model, to enable effective speech recognition, translation, and summarization in digital classrooms. A case study involving teacher and parent surveys indicates a strong acceptance rate of 90 percent, confirming the potential impact of EduVigil in real classroom scenarios. This promising acceptance highlights its capacity to enhance student engagement, automate administrative tasks, and provide real-time insights that facilitate personalized learning experiences. Preliminary tests demonstrate promising performance, underpinning the system's potential for wide-ranging applications in digital education.
FERLP: Facial Emotion Recognition Based on Landmark Points using Artificial Intelligence and Machine Learning

In the field of emotion processing, it can be challenging to determine how to identify a person's state of mind from their realistic expressions of emotion when working in an environment with several dimensions. There have been various investigations and projects done to develop strategies for generating accurate predictions; nevertheless, very little has been done to put the distinctive characteristics of the face of a person to use when interpreting and reacting to the emotional expressions of other people. An emotional picture retrieval approach is proposed for use in multimodal emotion detection in this body of work. This tactic is predicated on the observations that facial characteristics are present at all times and that the human face possesses regional redundancy that is independent of facial characteristics. Our approach makes use of landmark points to strip away any superfluous information on facial expressions, allowing us to zero in on the most appealing examples from each individual frame. If we make use of the landmarks, it's possible that we'll be able to forecast facial expressions with a greater degree of accuracy. This is especially important for long-term emotion prediction because it helps us restore time and space representations when an emotional face is introduced. The preliminary findings derived from a standard dataset indicate that the suggested strategy outperforms its rivals with regard to the accuracy of the predictions it generates.