Reviewerly/exHarmony huggingface dataset:
Hybrid intelligent framework for automated medical learning

Abstract This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up‐to‐date medical learning models regarding both the computational and cost the quality of returned solutions.
An efficient hybrid hash based privacy amplification algorithm for quantum key distribution

Privacy amplification (PA) is an essential part in a quantum key distribution (QKD) system, distilling a highly secure key from a partially secure string by public negotiation between two parties. The optimization objectives of privacy amplification for QKD are large block size, high throughput and low cost. For the global optimization of these objectives, a novel privacy amplification algorithm is proposed in this paper by combining multilinear-modular-hashing and modular arithmetic hashing. This paper proves the security of this hybrid hashing PA algorithm within the framework of both information theory and composition security theory. A scheme based on this algorithm is implemented and evaluated on a CPU platform. The results on a typical CV-QKD system indicate that the throughput of this scheme ( $$261\,\mathrm{Mbps}@2.6\times 10^8$$ input block size) is twice higher than the best existing scheme ( $$140\,\mathrm{Mbps}@1\times 10^8$$ input block size). Moreover, this scheme is implemented on a mobile CPU platform instead of a desktop CPU or a server CPU, which means that this algorithm has a better performance with a much lower cost and power consumption.
The Cryptocurrency Uncertainty Index

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Slim MobileNet: An Enhanced Deep Convolutional Neural Network

The Convolutional Neural Network (CNN) is dominant in computer vision applications such as object detection, traffic sign recognition, image classification, face recognition applications, etc. The MobileNet model is a CNN architecture that was constructed to be implemented on an embedded board. However, there are constraints of such architecture for the hardware deployment, which is the limited memory of micro-controller units. This paper proposes an enhanced version of MobileNet that verify the condition to be implemented on an embedded board while improved the accuracy. The proposed model obtained the name of Slim MobileNet because of its small size of 7.3 MB. Slim MobileNet has fewer number of layers, improved accuracy while depreciating the overall size of the model and lower average time compared to the MobileNet-V1 model. We achieve a significant accuracy by replacing the ReLU activation function with the Tanh Exponential (TanhExp) activation function and by making some modifications in the unit of depthwise separable convolution. The small size of Slim MobileNet is occurred by dropping some layers from the original architecture of baseline MobileNet. The experiment is realized using the CIFAR-10 database.
Comparative Study of Different Orange Data Mining Tool-Based AI Techniques in Image Classification

In the past few years, artificial intelligence (AI) has contributed significantly to the domain of image processing and image classification. There are many tools available for pre-processing of data, visualization of data and training of models. Orange is one such tool which has great data visualization, multiple pre-processing modules along with special add-on for image processing. The proposed work has applied this tool and presented a comparative analysis of different classifier models on cat and dog image dataset from Kaggle. Classification of the images is based on specific features which are extracted from the trained images with the help of a particular convolutional neural network model that is SqueezeNet. The result shows that the artificial neural network-based approach is found to be the best AI technique for classifying images with highest accuracy as compared to other techniques such as logistic regression, k-nearest neighbours, decision tree, random forests, Naïve Bayes and SVM.
Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome

Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events' prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS.
Swarm Intelligence-Based Methodology for Scanning Electron Microscope Image Segmentation of Solid Oxide Fuel Cell Anode

Segmentation of images from scanning electron microscope, especially multiphase, poses a drawback in their microstructure quantification process. The labeling process must be automatized due to the time consumption and irreproducibility of the manual labeling procedure. Here we show a swarm intelligence-driven filtration methodology performed on raw solid oxide fuel cell anode’s material images to improve the segmentation methods’ performance. The methodology focused on two significant parts of the segmentation process, which are filtering and labeling. During the first one, the images underwent filtering by applying a series of filters, whose operation parameters were determined using Particle Swarm Optimization upon a dedicated cost function. Next, Seeded Region Growing, k-Means Clustering, Multithresholding, and Simple Linear Iterative Clustering Superpixel algorithms were utilized to label the filtered images’ regions into consecutive phases in the microstructure. The improvement was presented for three different metrics: the Misclassification Ratio, Structural Similarity Index Measure, and Mean Squared Error. The obtained distribution of metrics’ performances was based on 200 images, with and without filtering. Results indicate an improvement up to 29%, depending on the metric and method used. The presented work contributes to the ongoing efforts to automatize segmentation processes fully for an increasing number of tomographic measurements, particularly in solid oxide fuel cell research.
Detecting malicious COVID-19 URLs using machine learning techniques

Throughout the COVID-19 outbreak, malicious attacks have become more pervasive and damaging than ever. Malicious intruders have been responsible for most of the cybercrimes committed recently and are the cause for a growing number of cyber threats, including identity and IP thefts, financial crimes, and cyber-attacks to critical infrastructures. Machine learning (ML) has proven itself as a prominent field of study over the past decade due to solving highly complex and sophisticated realworld problems. This paper proposes an ML-based classification technique to detect the growing number of malicious URLs, due to the COVID-19 pandemic, which is currently considered a threat to IT users. We have used a large volume of Open Source data and preprocessed it using our developed tool to generate feature vectors and trained the ML model using an apprehensive malicious threat weight. Our ML model has been tested, with and without entropy to forecast the threatening factors of COVID-19 URLs. The empirical evidence proves our methods to be a promising mechanism to mitigate COVID-19 related threats early in the attack lifecycle.
Effects of Presence and Physical Activity on Player Well-being in Augmented Reality Games: A Diary Study

In this study, we investigate presence in augmented reality games and how it is related to daily physical activity levels and well-being of players. In a diary study, 49 participants played “Ingress” for 14 days and filled out a questionnaire that consisted of several Likert scales, at the end of every day. Multilevel analyses showed that presence was associated with daily walking duration and daily subjective vitality, and daily walking duration was associated with subjective vitality of players in the same-day level. It was also found that walking duration partially mediated the relationship between presence and daily subjective vitality of players at the same-day level. Lastly, cross-lagged analyses showed that presence predicted next day’s walking duration implying a causal direction from presence to walking duration, whereas walking duration did not predict next day’s presence. The results are discussed and the implications are presented.
An Efficient Machine Learning Based Image Encryption Scheme for Medical Image Security

Medical images are very important in most of the application than any other images. In real time applications like telemedicine application, communication of medical image through open access needs protection as well as security at high level. Many imaging information has its own unique features which are so difficult to analyse and make decision to identify necessary techniques for protecting confidential image of unauthenticated access, Utmost all the existing encryption algorithms are mainly concentrating on textual data, but for multimedia data like images, it is not suitable. The main contribution done in this work is for ensuring increased security level over medical images regardless of presence of noises. In this algorithm, DNA subsequence operations combining with the use of improved Combined Linear Congruential Generator (C-LCG) were used for encryption of information. This paper discuss the idea of the improvement of safe and secure techniques using machine learning which is justified by the entropy value and correlation among adjacent pixels with performance parameters. The original image was scrambled using Combined Linear Congruential Generator with Bit rotation operation (BRO) and then image is transformed by effective encryption method using DNA subsequence operations. The proposed scheme discloses the correlation between pixel and entropy. Experimentation results showed that correlation among pixels is reduced while maximizing entropy. Number of Pixel Change Rate (NPCR) and Peak signal to noise (PSNR) ratio were also been analysed. In proposed algorithm, maximum NPCR values is achieved which shows the scattering of pixels in Encrypted image is high. PSNR shows a better encryption quality with lower the values.
A Reinforcement Learning based Path Planning Approach in 3D Environment

Optimal motion planning involves obstacles avoidance whereas path planning is the key to success in optimal motion planning. Due to the computational demands, most of the path planning algorithms can not be employed for real-time-based applications. Model-based reinforcement learning approaches for path planning have received particular success in the recent past. Yet, most such approaches do not have deterministic output due to randomness. In this paper, we investigate existing reinforcement learning-based approaches for path planning and propose such an approach for path planning in the 3D environment. One such reinforcement learning-based approach is a deterministic tree-based approach, and the other two approaches are based on Q-learning and approximate policy gradient, respectively. We tested the preceding approaches on two different simulators, each of which consists of a set of random obstacles that can be changed or moved dynamically. After analysing the result and computation time, we concluded that the deterministic tree search approach provides highly stable results. However, the computational time is considerably higher than the other two approaches. Finally, the comparative results are provided in terms of accuracy and computational time.
The dynamics of negative concord

Concord describes a natural language phenomenon in which a single logical meaning is expressed syntactically on multiple lexical items. The canonical example is negative concord, in which multiple negative expressions are used, but a single negation is interpreted. Formally similar phenomena have been observed for the redundant marking of distributivity and definiteness. Inspired by recent dynamic analyses of these latter two phenomena, we extend a similar dynamic analysis to negative concord. We propose that negative concord items introduce a discourse referent (like an existential), but then test that no discourse referent has been introduced in any assignment. These apparently contradictory requirements are licensed with split scope around negation: introduction occurs below negation; the test appears above it. The analysis successfully predicts that negative concord items must be licensed by a sufficiently local negative operator. We further show that modulation of what is at-issue can account for cases in which NC items themselves carry negative force.
Improving preprocessing in reversible data hiding based on contrast enhancement

In this paper, we propose an improvement in the preprocessing stage of the reversible data hiding method based on contrast enhancement applied to grayscale digital images. In conventional proposals, the preprocessing stage plays a fundamental role in the performance of the data hiding algorithm in terms of capacity and visual quality. Thus, this work presents two improvements to the preprocessing stage based on the analysis, merging, and shifting the image histogram, which, in conjunction with the data hiding stage, maximize the embedding capacity while keeping the visual distortion at a minimum. The proposed algorithm was implemented on two image datasets and compared with several related works previously reported in the scientific literature. The experimental results show that the visual quality can be preserved after data have been concealed when employing different embedding rates. Either of the preprocessing techniques presented in this paper can be adopted considering the application scenario and the priority of the requirements of the capacity and visual quality needed.
MEDAL: An AI-Driven Data Fabric Concept for Elastic Cloud-to-Edge Intelligence

Current Cloud solutions for Edge Computing are inefficient for data-centric applications, as they focus on the IaaS/PaaS level and they miss the data modeling and operations perspective. Consequently, Edge Computing opportunities are lost due to cumbersome and data assets-agnostic processes for end-to-end deployment over the Cloud-to-Edge continuum. In this paper, we introduce MEDAL—an intelligent Cloud-to-Edge Data Fabric to support Data Operations (DataOps) across the continuum and to automate management and orchestration operations over a combined view of the data and the resource layer. MEDAL facilitates building and managing data workflows on top of existing flexible and composable data services, seamlessly exploiting and federating IaaS/PaaS/SaaS resources across different Cloud and Edge environments. We describe the MEDAL Platform as a usable tool for Data Scientists and Engineers, encompassing our concept and we illustrate its application though a connected cars use case.
Mobile Robot Circumnavigating an Unknown Target Using Only Range Rate Measurement

This brief proposes a bounded continuous control law to steer a mobile robot to approach and maintain a predefined distance from an unknown stationary target in a GPS-denied environment where the positions of the robot and the target are not available. The control algorithm utilizes only the derivative of the distance between the mobile robot and the target, that is, range rate measurement. We demonstrate that the controller can achieve global convergence regardless of the initial states of the mobile robot. Compared with the existing works, the proposed control algorithm is the simplest form (derivative-type controller) and easy to implement. Numerical simulations and real-time experiments are both performed to confirm the effectiveness of the proposed control scheme.
Smart Aging Wellness Sensor Networks: A Near Real-Time Daily Activity Health Monitoring, Anomaly Detection and Alert System

In the growing automation of existing world, activity modeling is being used in the field of technology to serve various purposes. One such field, which will be majorly benefited from daily activity modeling and life- living activities analysis, is monitoring of seasonal behavior pattern of elderly people, which can be further utilized in their remote health analysis and monitoring. Today’s demand is to develop a system with minimum human interaction and automatic anomaly detection and alert system. The proposed research work emphasizes to diagnose elderly persons daily behavioral patterns by observing their day-to-day routine activities with respect to time, location and context. To grow the accurateness of the structure, numerous sensing as well as actuator units have been deployed in elderly homes. Popular this research paper, we have recommended a unique sensing fusion technique to monitor seasonal, social, weather related and wellness observations of routine tasks. A novel daily activity learning model has been proposed which can record contextual data observations of various locations of a smart home and alert caretakers in the case of anomaly detection. We have analyzed monthly data of two old-aged smart homes with more than 5000 test samples. Results acquired from the investigation validate the accuracy and the efficiency of the proposed system which are recorded for 20 activities.
LPV H₂ State Feedback Controller for Automated Parking System

In this letter, we propose a linear parameter-varying (LPV) kinematic model for automated parking systems. We also design an LPV <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> state feedback controller with the proposed model. The vehicle kinematic model is intrinsically a nonlinear system, but we show that the kinematic model can be represented as an LPV kinematic model. The state feedback control gain is obtained using convex interpolation in the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sense of a linear matrix inequality approach. We will show that the proposed parking system guarantees the stability of the closed-loop system with disturbances. To validate the proposed method, we conduct parking experiments with a test vehicle for two scenarios. Using the proposed LPV <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> state feedback controller, the vehicle tracks paths for all scenarios, reaching the final parking spot and showing smooth steering performance. The results show that the lateral position error and the heading angle error are smaller than 0.05 m and 0.005 rad, respectively.
Early Diagnosis of Breast Cancer Prediction using Random Forest Classifier

Abstract Breast Cancer is one of the most dreadful diseases and is a potential cause of death in women. Late prediction of Breast Cancer may greatly reduce survival chances, and as a solution to that the automatic disease detection system aids the medical field to diagnose and analyze, which offers rapid response, reliability, effectiveness as well as decrease the risk of death. In this paper, we explain how breast cancer can be predicted using a Machine Learning Technique named Random Forest Classifier. This classifier structures the data into numerous trees and obtains a final result i.e., whether a person is at risk of having breast cancer or not. This model has an accuracy of 98%.
Online deep transferable dictionary learning

In real-world applications, large-scale unlabeled data usually becomes available gradually over time. Online learning is important to update models while preserving their historical knowledge. However, a time-varying distribution shift exists in incoming sequential data in online learning, resulting in a data cluster discrepancy between the incoming unlabeled data and older labeled data, which is a challenging situation for online learning. To address this issue, we propose an online deep transferable dictionary learning (ODTDL) method that simultaneously mitigates the data cluster discrepancy for incoming unlabeled data while preserving historical knowledge of older data in the dictionary. By forming a locally linear representation and association of incoming unlabeled data over a small amount of labeled data in a deep feature space, the proposed ODTDL method can reveal data cluster discrepancies. To implement this approach, we propose a two-level affiliation regularizer that both comprehensively reveals the local instance-level and global cluster-level affiliations and enables an off-the-shelf dictionary reconstruction error method to establish a knowledge transfer pipeline between the labeled and unlabeled data. For online learning, this approach further decomposes the knowledge transfer pipeline into batchwise transfer pipelines, thereby establishing batchwise transfer pipelines between labeled and unlabeled data. Finally, the proposed method is confirmed to be feasible in online semi-supervised learning (SSL) and online unsupervised domain adaptation (UDA) scenarios and demonstrates its superiority in the online setting.