Reviewerly/exHarmony huggingface dataset:
Enhanced Mayfly Optimization with Active Elite Approach Based Cluster Head Selection for Energy Efficient IoT based Healthcare Monitoring System

In order to conserve energy in the Internet of Things (IoT) network and also effectively handle the integrity and security issues in medical information, a framework for transmitting data that is both secure and energy efficient was proposed. It was used Enhanced Mayfly Clustering-based Q Learner Routing (EMCQLR) and Exponential Key-based Elliptical Curve Cryptography (EKECC) techniques. In EMCQLR, Enhanced Mayfly optimization Algorithm (EMOA) was used to select the Cluster Head (CH) for data collection from the nodes and form clusters of IoT medical sensors. This paper proposes a new approach called EMOA with Active Elite Approach (EMOA-AEA) to deal with the issues of slow convergence speed and the tendency of EMOA to fall into local optimum. The EMOA-AEA algorithm establishes a definite area around the most optimal mayfly in the present population, which is used to identify the top-performing CH. This region’s search radius is then adjusted as needed. Elite mayflies are subsequently produced within this designated zone, and if their fitness level surpasses that of the most exceptional mayfly, the finest cluster head from these new elite mayflies is selected to replace the current population’s top mayfly. After the selection of cluster head, Path-Weighted Q Reinforcement Learning (PWQRL) is used for data routing. At last, EKECC algorithm encrypts the medical records to provide data security. The experimental outcomes prove that the EMOA-AEA method surpasses the existing method in terms of network lifetime, average energy consumption, and throughput.
A Hyper-Graph Embedded Bandlet-Based Facial Emotion Monitoring System for Enhanced Urban Health

The state of health of a person can affect their facial expressions. As a result, a system that recognizes facial expressions can be beneficial for healthcare services. In this study, a Facial-Expression Recognition system has been developed to improve healthcare in smart cities by extracting features from a face image through a bandlet transform and Center-Symmetric Local Binary Pattern (CS-LBP). The most prominent features are selected using a Feature-Selection algorithm and then provided to two classifiers, Gaussian mixture model and support vector machine, to determine the facial expression with a confidence score that is calculated from the combined ratings of the classifiers. The proposed system has been tested with large data sets and found to have an accuracy of 99.5% in identifying facial expressions.
Machine Learning based Algorithmic approach for Detection and Classification of Leukemia

In general, leukemia is diagnosed by taking repeated complete blood counts, since this will enormously increase the blood cell count of the patient compared to normal people. The malignant cells resemble the normal blood cell which complicates the prediction process. So, this aliment must be detected and treated in early stages to avoid any complications. The methods already existing in laboratories are time consuming. This study presents a Machine Learning approach for detecting leukemia in patients. A dataset consisting of blood smear images was collected and preprocessed to extract relevant features. The features were then used to train and test various machine learning classification algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and KNN. Then by evaluating the performance of the above-mentioned classifiers using different performance metrics like accuracy, precision, etc., the efficient one can be identified.
Interpretable Machine Learning Models for Predicting Malaria

Malaria remains one of the deadliest diseases in underdeveloped regions, particularly in Sub-Saharan Africa. The lack of high-quality healthcare services and accurate disease diagnosis systems has resulted in acute medical problems for patients. This necessitates reliable automated decision-making tools to aid medical professionals in their decision-making process. This paper presents a transparent approach to malaria diagnosis by applying Explainable Artificial Intelligence (XAI) techniques, namely Shapley Additive Explanation (SHAP) and Local Interpretable Model-agnostic Explanation (LIME), to provide meaningful interpretations of severe malaria predictions made by machine learning models. Various models, including Extreme Gradient Boosting, K-means, K-Nearest Neighbor, Support Vector Machine (SVM), Decision Tree, Logistic Regression (LR), Random Forest, Naive Bayes, AdaBoost, and Explainable Boosting Machines (EBMs) are deployed for this task. The results of the study showed that Random Forest and Explainable Boosting Machines achieved the highest accuracy of 84%. EBM also provided a practical clinical understanding of features that drive clear prediction. The LR achieved an accuracy of 81% after applying GridSearchCV to increase prediction accuracy. Furthermore, K-fold validation was used on XGBoost to estimate the model’s skill on new data. The interpretations were enhanced by XAI, which revealed features that contribute to severe malaria. The application of these techniques can significantly improve the accuracy of severe malaria predictions and aid medical professionals in making informed decisions. This paper provides a compelling argument for the urgent need for XAI techniques to address the challenges associated with severe malaria diagnosis and treatment. The study’s findings demonstrate the effectiveness of these techniques in enhancing the accuracy and interpretability of machine learning models, which can greatly benefit medical professionals in their decision-making process.
Block Chain-Based Voting System

A secure fair electronic voting system is required for the smooth working of any democratic process. This paper implements a voting-based system application that runs with the support of blockchain to create a decentralized voting system. The proposed system aims to eliminate the aspect of trust from an election to make it more secure and transparent.
An Investigational Study of Detecting Acute Lymphoblastic Leukemia using Computer Vision

Among all divesting cancers, Hematologists predict that the Leukemia is mostly occur on the children, teenagers, and young adults. Moreover 85% of cancer cases are detected younger than the age of 15. Due to a Genetic abnormality in the bone marrow, Acute lymphoblastic leukemia (ALL) is particularly susceptible to life-threatening infections. The laboratory method to detect the ALL is prolonged and slow process. Reviewing prior research on detection methods in ALL is the goal of this study. This review includes (a) the parameters necessary for the ALL detection (b) methods involved in the detection process (c) which algorithms gives the accurate prediction and (d) finally all the necessary context for the detection of the ALL-affected cells. The proposed system possesses the automated machine learning (Chabot) approach used to categorize the infected and healthy cells in the blood smears (microscopic images) in order to detect ALL. The Blood smears are converted into the CMYK color space and separated into clusters using K-means Algorithm. A cell from each cluster is picked and detected whether the cell is affected with ALL or not using the nuclei of the cell using supervised learning algorithm like SVM, XGBoost Classifier, Etc., The proposed system aids in enhancing the acute lymphoblastic leukemia detection system.
Hybrid CNN-GRU Model for Handwritten Text Recognition on IAM, Washington and Parzival Datasets

The aim of using the CNN-GRU Hybrid Model for HTR is to improve the accuracy of existing HTR systems by developing more robust models that can handle the variability of handwriting styles and the complexity of language. The proposed model combines CNN and GRU and is evaluated on multiple datasets, including IAM, Washington, and Parzival, to provide a comprehensive analysis and comparison with existing models. The CNN-GRU architecture proposed in the study has been tested on IAM, Washington, and Parzival datasets, and it was found to have lower CER and WER scores compared to many other models. The model achieved CER scores of 7.16%, 6.S%, and S.06% and WER scores of16.16%, 17.24%, and 19.13% on the IAM, Washington, and Parzival datasets, respectively.
Meta Models of Yoga gestures by ACCF and SCHF with ML techniques

This Yoga is a set of techniques that involve both the body and the mind, and its roots may be traced back to ancient India. Its purpose is to harmonise the body, the intellect, and the soul. The practise of yoga as both an art and a science for maintaining a healthy lifestyle has seen explosive growth in popularity over the past few decades all around the world. People who practised yoga during the lockdowns exhibited lower levels of stress, anxiety, and sadness, according to a number of studies, including the most recent COVID-19 pandemic times. For those interested in leading a more physically and mentally fit life, the ancient practice of yoga comes highly recommended. When practising a yoga asana, it is of the utmost significance to keep the correct posture the entire time. In this study, we make use of transfer learning from human posture estimation models to classify yoga postures. The collected images are used to train a meta model like, Classification via Regression(CVR), and Iterative Classifier optimizer(ICO) after image feature extraction techniques(Auto Color Correlogram Filter and Simple Histogram Filter), which is then applied to the task of determining which yogasanas are being performed. The SCHF+ICO gives an optimal outcome compare with other models.
Achievement, Mastery Level and Experiences of Learners in Oral Communication Using Digitized Modules

Due to global pandemic caused by the outbreak of the corona virus disease 2019 (Covid-19), the Department of Education called for distance learning to be adopted as a learning delivery modality. Under this distance learning are different modalities such as printed modular learning which can either be in print or in digitized format. Seeing the need for modular distance learning to gradually shift to digital due to the perceived cost of papers for printing, there is now a need to determine if digitized modular learning offers better opportunities for students to learn. This action research sought to determine the achievement and mastery levels as well as experiences of learners of Solano High School learning Oral Communication in Context through printed modular and digitized modular learning modalities for the School Year 2021-2022. The study was experimental in nature and employed quantitative and qualitative research approaches. A pretest and posttest were administered for the first four weeks of the first quarter period of observation. Results showed that learners performed academically better under the digitized modular learning modality than those of printed modular learning modality as reflected in their mastery and achievement levels. Despite this, learners perceived both positive and negative experiences under digitized modular learning.
Tree-Structured Data Clustering-Driven Neural Network for Intra Prediction in Video Coding

Intra prediction is a crucial part of video compression, which utilizes local information in images to eliminate spatial redundancy. As the state-of-the-art video coding standard, Versatile Video Coding (H.266/VVC) employs multiple directional prediction modes in intra prediction to find the texture trend of local areas. Then the prediction is made based on reference samples in the selected direction. Recently, neural network-based intra prediction has achieved great success. Deep network models are trained and applied to assist the HEVC and VVC intra modes. In this paper, we propose a novel tree-structured data clustering-driven neural network (dubbed TreeNet) for intra prediction, which builds the networks and clusters the training data in a tree-structured manner. Specifically, in each network split and training process of TreeNet, every parent network on a leaf node is split into two child networks by adding or subtracting Gaussian random noise. Then data clustering-driven training is applied to train the two derived child networks using the clustered training data of their parent. On the one hand, the networks at the same level in TreeNet are trained with non-overlapping clustered datasets, and thus they can learn different prediction abilities. On the other hand, the networks at different levels are trained with hierarchically clustered datasets, and thus they will have different generalization abilities. TreeNet is integrated into VVC to assist or replace intra prediction modes to test its performance. In addition, a fast termination strategy is proposed to accelerate the search of TreeNet. The experimental results demonstrate that when TreeNet is used to assist the VVC Intra modes, TreeNet with depth = 3 can bring an average of 3.78% bitrate saving (up to 8.12%) over VTM-17.0. If TreeNet with the same depth replaces all VVC intra modes, an average of 1.59% bitrate saving can be reached.
障害物の角速度情報と解像度可変ポテンシャル場を用いた自律走行パーソナルモビリティの運動制御

In recent years, personal mobility (PM), electric vehicles that are smaller and slower than conventional automobiles, have been attracting attention, and are expected to be used on sidewalks and in shared spaces where vehicles and pedestrians can coexist. PM is also expected to be used as a means of transportation for the physically challenged and the elderly who have difficulty getting around by themselves, and research is being conducted to automate PM operation. In this situation, our group proposed a PM motion control method based on a spatio-temporal potential that takes into account the motion characteristics of obstacles. However, this method is not sufficient to avoid obstacles with large angular velocities, such as pedestrians. It is also not a practical method because of the time required for computation. Therefore, this paper proposes a new practical PM motion control method that improves these points. By defining a new potential field that takes into account the angular velocity of the obstacle, the proposed method improves the performance against obstacles whose avoidance performance was insufficient in the previous method. At the same time, to increase practicality, a reduction algorithm with variable resolution of the potential field is incorporated as a method to reduce computational complexity while maintaining avoidance performance as much as possible. Finally, the effectiveness of the proposed method is verified through numerical simulations assuming a shared space.
Enhanced Hybrid Hierarchical Federated Edge Learning Over Heterogeneous Networks

In this work, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hybrid Hierarchical Federated Edge Learning</i> (HHFEL) architecture that consists of a device layer, an edge layer, and a cloud layer over heterogeneous networks, is investigated for large-scale model training. In such systems, learning efficiency is severely degraded by limited communication resources and device heterogeneity in terms of local data distribution and computation capability, especially for synchronous FL mechanisms where the training of each round should wait for the slowest device. To tackle this issue, asynchronous FL is proposed, which allows the devices with powerful computation and communication capabilities exchanging information with the server more frequently. However, this asynchronous FL framework faces a new challenge of low accuracy caused by the imbalanced local model updating. To overcome the shortage of both synchronous and asynchronous FLs, we propose an enhanced online semi-asynchronous FL mechanism between the edge-device layers, where each device trains its local model with the newly generated data and each edge server aggregates a number of local models based on their arrival order in each round. Particularly, devices with faster training speeds would fully utilize the idle time by training their local models repetitively. Meanwhile, synchronous FL with an edge elastic update strategy is adopted to the cloud-edge layers for personalized information exchange. Considering the continuous data generation feature, we formulate the objective problem as an online <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Markov Decision Process</i> (MDP) to realize efficient communication-and-computing HHFEL via joint device selection and resource allocation. Due to the non-convex and combinatorial problem structure, we develop a hybrid <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Q-Network</i> (DQN) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Deterministic Policy Gradient</i> (DDPG) approach with low computational complexity to adapt the device selection and resource allocation strategies. Numerical results show the effectiveness of the proposed mechanism compared with existing benchmarks.
On the Impact of Project Management Certification for Software Projects and Practitioners

This article investigates the benefits project management certifications offer to those who pursue them, at significant cost in terms of time and money, and the extent to which they contribute to project success.
Error Analysis of NIST SP 800-22 Test Suite

Statistical tests for randomness play an essential role in cryptography, but the reliability of these tests is rarely taken into account, which may mislead our judgement of randomness especially with extremely large samples. In this paper, we make a two-stage error analysis of the commonly used two-level randomness tests in the NIST SP 800-22 test suite. Especially, we give the estimates of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> -value deviations of chi-square approximation in the basic tests based on our proposed continuity constraints, and mathematically express the reliability of uniformity test used in the test suite with the fact of noncentral chi-square approximation. Finally, we analyze the respective error factors and derive the corresponding probability deviation estimation for the tests, explaining some false positive issues in practical test experiments. With our derived error analysis models, one can get a more reliable randomness test strategy with extremely large samples and wider parameter selections.
Multiframe Super-Resolution With Dual Pyramid Multiattention Network for Droplet Measurement

Accurate visual measurement of micrometer-scale flying droplets in inkjet printing remains a challenge due to low image resolution caused by severe image conditions. Multi-frame super resolution (MFSR) has the potential to break through the measurement bottleneck. However, most existing MFSR methods are not satisfactory in multi-frame information utilization, especially for fast-motion scenes, and they often suffer from detail loss. In this study, focusing on multi-frame information utilization and deep feature extraction, we propose a dual pyramid multi-attention network (DPMAN). First, a dual pyramid deformable alignment (DPDA) module is proposed to deal with diverse motion, which extracts explicit offsets to enhance deformable alignment and perform coarse-to-fine alignment. Then, a gated attention fusion (GAF) module is devised to adaptively aggregate the aligned features to emphasize favorable features. Finally, a residual self-attention reconstruction (RSAR) module based on the multi-stage aggregation self-attention architecture is proposed to extract finer deep features for detail restoration. Experimental results on three benchmark datasets demonstrate that DPMAN achieves state-of-the-art performance. DPMAN is applied to droplet image reconstruction and improves the measurement accuracy from 3.34% to 2.52%.
Multiscale Feature Aggregation Network for Salient Object Detection in Optical Remote Sensing Images

Optical remote sensing images (ORSIs) have various applications in different fields, and salient target detection (ORSI-SOD) of ORSI has become an important research topic in recent years. However, ORSI-SOD is a challenging problem due to the variable and complex backgrounds, large differences in levels, mixed backgrounds, and diverse topological shapes of ORSI. In this article, we propose a novel model called a multiscale feature refinement aggregation network (MFANet), which consists of a multiscale feature refinement (MFR) module and a context feature aggregation (CFA) module. The MFR module extracts semantic information of ORSI across different dimensions in the multiscale feature extraction stage. In the feature refinement stage, we use the proposed self-refinement module under the guidance of attention and reverse attention to progressively refine the prediction results. The CFA module introduces the hybrid attention module to gradually aggregate and extract salient regions from the context extraction module. To adapt to dense scenes, we develop a hybrid loss function that enables network optimization of multiscale objectives in a self-adaptive manner. Our method outperforms most state-of-the-art salient object detection (SOD) methods proposed in recent years in terms of accuracy.
ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories

We present ManuKnowVis, the result of a design study, in which we contextualize data from multiple knowledge repositories of a manufacturing process for battery modules used in electric vehicles. In data-driven analyses of manufacturing data, we observed a discrepancy between two stakeholder groups involved in serial manufacturing processes: Knowledge providers (e.g., engineers) have domain knowledge about the manufacturing process but have difficulties in implementing data-driven analyses. Knowledge consumers (e.g., data scientists) have no first-hand domain knowledge but are highly skilled in performing data-driven analyses. ManuKnowVis bridges the gap between providers and consumers and enables the creation and completion of manufacturing knowledge. We contribute a multi-stakeholder design study, where we developed ManuKnowVis in three main iterations with consumers and providers from an automotive company. The iterative development led us to a multiple linked view tool, in which, on the one hand, providers can describe and connect individual entities (e.g., stations or produced parts) of the manufacturing process based on their domain knowledge. On the other hand, consumers can leverage this enhanced data to better understand complex domain problems, thus, performing data analyses more efficiently. As such, our approach directly impacts the success of data-driven analyses from manufacturing data. To demonstrate the usefulness of our approach, we carried out a case study with seven domain experts, which demonstrates how providers can externalize their knowledge and consumers can implement data-driven analyses more efficiently.
Adversarial Attacks Against Machine Learning-Based Resource Provisioning Systems

Microarchitectural attacks, such as side-channel, exploit shared resources to leak sensitive information. Performing microarchitectural attacks on the cloud is possible once the attacker’s virtual machine (VM) is co-located with the victim’s VM. Hence, the co-location requirement with the victim limits the practicality of microarchitectural attacks on the cloud. In this work, we demonstrate that resource provisioning systems (RPSs) can be exploited to solve the co-location challenge of microarchitectural attacks in the cloud by deploying adversarial evasion attacks on RPSs to co-locate attackers’ VMs with victims’ VMs. Moreover, we discuss the adaptability of defense techniques proposed against adversarial attacks in the image classification domain on the RPSs.