Reviewerly/exHarmony huggingface dataset:
A hand rubbing classification model based on image sequence enhanced by feature-based confidence metric

Hand hygiene is critical for declining the spread of viruses and diseases. Over recent years, it has been globally known as one of the most effective ways against COVID-19 outbreak. The World Health Organization (WHO) has suggested a 12-step guideline for hand rubbing. Due to the importance of this guideline, several studies have been conducted to measure compliance with it using Computer Vision. However, almost all of them are based on processing single images as input, referred to as baseline models in this paper. This study proposes a sequence model in order to process sequences of consecutive images as input. The model is a mixture of Inception-ResNet architecture for spatial feature extraction and LSTM for detecting time-series information. After training the model on a comprehensive dataset, an accuracy of 98.99% was achieved on the test set. Compared to the best baseline models, the proposed sequence model is correspondingly about 1% and 4% better in terms of accuracy and confidence, though 3 times slower in inference time. Furthermore, this study demonstrates that the accuracy metric is not necessarily adequate to compare different models and optimize their hyperparameters. Accordingly, the Feature-Based Confidence Metric was utilized in order to provide a more pleasing comparison to discriminate the proposed sequence model with the best baseline model and optimize its hyperparameters.
Analyzing Patient Reviews for Recommending Treatment Using NLP and Deep Learning-Based Approaches

Nowadays technological advancement can be seen in the medical field starting from medical devices, data collection, analysis to diagnosis and treatment recommendations for diseases. Drug and treatment recommendation is one of the most popular applications, which is now observed and used by everyone in this digital era. These types of recommendation systems usually require a huge set of data from the patients and an efficient Machine learning-based model to conclude significant insights that can help in the prediction of the best possible medications for a particular disease. The health-related recommendation systems can be proved as a significant tool in the healthcare sector for speeding up various decision-making processes such as health insurance, clinical pathway-based treatment methods, and assisting doctors by recommending drugs using a patient’s health profile. People frequently utilize social media to explore their health issues, therefore there is now a wealth of information on social networks that may be leveraged to create various health-related recommendation systems. In this research, a deep learning-based based drug recommendation system using N-Gram is provided, which uses patient review data as input and sentiment analysis to choose the appropriate treatment for the ailment. The accuracy achieved, shows the efficacy of the proposed approach for the real time applicability of the model.
Analysis of Ransomware Security on Cloud Storage Systems

Over the years cloud computing and cloud storage solutions have grown into over a 50-billion-dollar industry, and that signifies over 50 % of all corporate data being stored in the cloud as of 2021. In accordance, attacks over these files have grown exponentially, with average damages exceeding multiple millions of dollars per attack. Cloud storages are more susceptible to these attacks, due to the physical factor of accessing local storage being removed, and a simple connection to the internet. Despite the great convenience and arguably fulfilling core necessities during the pandemic, the Cloud storage model is vulnerable and an incredibly juicy target. These ransomware attacks are proving to be huge security risks around the world as such. Combine these with various other problems such as area of jurisdiction of law enforcement agencies and blockchain-based payments making tracing these payments a nigh-impossible task, and the threat is apparent at even the first glance. In this paper, (i) we analyse the insidious nature of ransomware and their power and threat levels, (ii) various papers and industry data to give a comprehensive analysis of the level of security that currently exists, (iii) discuss large security breaches in recent history, and (iv) consider up and coming solutions for cloud storage security. After going through these, we shall draw a final (v) conclusion, on the state of cybersecurity and cloud storage based on presented data.
Analyzing Various Handwriting Recognition Phenomenon for Predicting Gender, Age and Handedness

Handwriting recognition, in the current scenario is becoming an important task to recognize ones general identity by investing minimum time for it. As handwriting recognition plays a very significant role for authentication of documents, genuineness of historical manuscripts and many more. It is now becoming an exploiting field of research as one can copy others handwriting but can never write the same in an identical manner. From here comes rise of a new field of research for researchers. Now this handwriting recognition concept is being used for classifying gender, age, handedness, ethnicity and many more aspects just by analyzing written data of the writers. The focus for this exploration is to study and summarize various techniques used so far for analyzing the handwriting and recognizing the possible features of a person. This concept has built up its importance by raising its usage in various fields such as; for forensic scrutiny, identification of writers, word perceiving, verification of signature etc. The advancement of this research for this concept has increased by observing the attractive positive results for its outcomes and the eagerness of building a computerized system for processing handwriting so that it could replace the manual analysis. From prediction till recognition of correct identity of the writers’, this complete process takes place majorly in three basic steps: segmentation, features extractions and classification. SVM, KNN and RFC supervised machine learning algorithms are being used for classification purpose. Few of plenty of researches done by researchers for the same purpose had also purposed approaches by using the unsupervised machine learning techniques as well. Thus, both the phenomenon may be utilized for fulfilling the defined query with most appropriate possible computational approach.
Multi-scale fusion based super-resolution underwater image segmentation network

To address the problem of low accuracy of underwater image segmentation images due to low resolution, low visibility, a wide variety of objects and insufficient illumination of underwater acquired images, we build a super-resolution underwater image segmentation network based on multi-scale fusion. We apply Resnet50 as the backbone feature extraction network to the U-shaped network architecture, which can well enhance the feature capture capability of the model, and apply the upsampling output of the feature extraction model combined with the super-resolution module to obtain a refined output. Compared with other networks, our model has significant segmentation capability on the SUIM underwater image segmentation dataset. The shallow feature information of the input image can be better restored and preserved, and the edge feature information of the image is better preserved with higher accuracy, and the model has higher cross-merge ratio and accuracy. Our work is applied to the SUIM underwater image segmentation dataset and compared with U-Net( vgg16),U-Net(Resnet50), PSPNet(Mobilenet), PSPNet(Resnet50), deeplab(mobilenet) networks on this dataset, and our proposed network The average cross-merge ratio (MIou) of our proposed networks improved by 4.29%, 1.58%, 7.44%, 2.69% and 3.24%, respectively, and the pixel accuracy (mPa) improved by 3.24%, 1.57%, 5.4%, 1.93% and 5.73%, respectively.
How insights from the field of information behavior can enrich understanding of knowledge mobilization

The authors review the literature on information behavior, an autonomous body of work developed mainly in library studies and compare it with work on knowledge mobilization. The aim is to explore how information behavior can contribute to understanding knowledge mobilization in healthcare management.The authors conducted a narrative review using an exploratory, nonkeyword "double-sided systematic snowball" method. This is especially useful in the situation when the two traditions targeted are broad and relies on distinct vocabulary.The authors find that the two bodies of work have followed similar trajectories and arrived at similar conclusions, with a linear view supplemented first by a social approach and then by a sensitivity to practice. Lessons from the field of information behavior can be used to avoid duplication of effort, repeating the same errors and reinventing the wheel among knowledge translation scholars. This includes, for example, focusing on sources of information or ignoring the mundane activities in which managers and policymakers are involved.The study is the first known attempt to build bridges between the field of information behavior and the study of knowledge mobilization. The study, moreover, foregrounds the need to address knowledge mobilization in context-sensitive and social rather than technical terms, focusing on the mundane work performed by a variety of human and nonhuman agents.
Multi-Sensor Fusion for Decentralized GPS-Denied Robotic Swarm Cooperative Navigation

—This work presents a cooperative navigation technique for autonomous robotic swarms in a GPS-denied environment using a random finite set-based sensor fusion technique. Swarm agents collect 2D range and bearing measurements, compute their local random finite set multi-object tracking filter solution, and broadcast the result to allies. These solutions are then processed with a multiple field of view sensor fusion algorithm to extract the fusion agent’s position estimate in the global coordinate system.
A Multi-Agent UAS Path-finding Algorithm for UnmannedTraffic Management Operations

With the popular usage of Unmanned Aerial Systems (UAS) in the commercial industry, a notional Unmanned Traffic Management (UTM) System was developed by the Federal Aviation Administration (FAA). In this UTM, Universal Service Suppliers (USS) must be able to communicate with UAS Operators and provide support conflict management particularly for path planning. This paper proposes a hierarchical approach for path planning of multiple UASOperators prior to take-off and is easily configured into the UTM as a USS. The method can be adjusted for various different topologies as well as mimics standard route planning methods conducted in manned aircraft operations. To test this simulation 30,000 total Monte Carlo simulations were conducted for 10 to 100 UAS operators, with different prioritization techniques. In addition, the method was tested in High-Fidelity simulation utilizing Unreal Engine and Robot Operating Systems (ROS) integrating the FAA's notional UTM architecture. Results indicate 99% success for UAS operations under 50 UAS and that utilizing a minimum prioritization protocol yields the best performance.
Classifying Aircraft using Velocity Data with Support Vector Machines and Likelihood Ratio Tests

Timely classification of aircraft is important for small unmanned aerial system (sUAS) technologies, such as onboard collision avoidance systems, and aerial perimeter security for prisons and sports venues. This work uses velocity-based metrics to classify multi-rotor sUAS, fixed wing sUAS, and general aviation planes using two classification methods: Support Vector Machines (SVM), and Likelihood Ratio (LR) tests. We found that a 96\% classification accuracy is achieved when either classifier is trained using average speed derived from flight controller data or radar data and tested with one second of radar data. Further, we show that LR tests perform similarly to SVM for single metric classification. In addition, we present two novel metrics for classifying aircraft: log variance of absolute change in speed, and log variance of relative change in speed. Finally, we discuss challenges associated with training classifiers with flight controller data but testing on radar data.
Open-Cavity Fluid Flow as an Information Processing Medium

Recent advances in sensor technology have opened the door to applying distributed sensing to bodies in complex flow environments. However, the increased computational burden of processing the resulting information is not trivial. Using the mathematical framework of reservoir computing, we demonstrate that it is possible to offload some of this burden to the dynamics of the environment. The local flow environment may be treated as a non-linear operator which maps an input signal, a perturbation of the mean external flow, into a high dimensional latent space. A complex non-linear output function, such as a control signal or a classification designation, may then be computed by strategically sampling this space with a distributed sensor array and combining the readouts using a simple weighted sum. In this work, we demonstrate this concept using computational simulations of flow in an open-cavity and explore how sensor placement, cavity geometry and Reynolds number affect the information processing capability. We find that for an incoming laminar incompressible boundary layer flow, placing sensors inside shear-driven open cavity flow performs much better on benchmark tasks requiring memory than placing many sensors directly in the boundary layer flow. This could reduce the need for electronic computing hardware, potentially reducing cost, size, weight, and power and shortening control cycle times.
Machine-Learned Background Quantification for Filtered Rayleigh Scattering Measurement Processing

Processing filtered Rayleigh scattering (FRS) data is currently a non-trivial, computationally expensive process that attempts to minimize a multivariate, least-squares fit algorithm to obtain approximations of three-component velocity, static density, static temperature, Mie (particle) scattering contribution, and background (geometric) scattering contribution. This approach aims to fit a measured FRS spectrum to a simulated spectrum with known flow properties of interest for every pixel captured in an image set during the measurement. Considering that images may contain millions of pixels, solving this minimization problem for each pixel can be cumbersome. The current work uses a machine learning approach that leverages the multi-layer perceptron (MLP) to solve the inverse problem of determining desired flow properties from FRS spectra. As a proof of concept, the only properties considered as predicted outputs for this development were velocity and background scattering contribution. The trained machine learning models were tested on a simulated FRS experiment with an artificial addition of background. These models were able to predict the desired velocity field to an accuracy of 0.6 m/s RMSE and solved the full plane of predicted properties for a 20x20 resolution image in 100 ��sec on one core processor. When compared to a standard least-squares approach, which solved for velocity on 10 core processors in around 10 minutes, this machine learning approach provides an impressive speed-up.
Ensuring Safety of Machine Learning Components Using Operational Design Domain

The introduction of machine learning in the aviation domain is an ongoing process.This is also true for safety-critical domains, especially for the area of Urban Air Mobility.A significant growth in number of air taxis and an increasing level of autonomy is to be expected allowing for operating a large number of air taxis in complex urban environments.Due to the complexity of the tasks and the environment, key autonomy functions will be realized using machine learning, for example the camera-based detection of objects.However, the safety assurance for avionics systems using machine learning components is challenging.This work investigates safety and verification aspects of machine learning components.A camera-based detection of humans on the ground, e.g. to assess a potential landing area, serves as an example for an machine learning-based autonomy functio and was integrated into an Unmanned Aircraft. In the context of this exemplary machine learning component, the concept of Operational Design Domain as recently adapted European Aviation SafetyAgency in the context of machine learning assurance is described along with other key concepts of machine learning assurance.Furthermore, runtime assurance is used to monitor conformance to the Operational Design Domain during flight.The presented flight test results indicate that monitoring the Operational Design Domain can support performance as well as the safety of the operation.
Reusable Entry Vehicle Trajectory Optimization Using Multiple-Domain Radau Collocation

The trajectory optimization of a reusable launch vehicle entry is studied. The objective is to maximize the crossrange during atmospheric entry subject to a constraint on the stagnation point heating rate. The problem is solved by partitioning the domain of the independent variable into multiple subdomains such that each subdomain consists of a segment where the heating rate constraint is either active or inactive. Additional necessary conditions for optimality are enforced in segments where the heating rate constraint is active. A multiple-domain Legendre-Gauss-Radau direct collocation method is then used to solve the partitioned problem. Key features of the stagnation point heating rate constraint are presented, and it is observed that the aforementioned approach is capable of solving the problem under consideration more accurately than traditional direct collocation methods.
Reinforcement Learning based Coverage Planning for UAVs Fleets

This paper proposes a Reinforcement Learning (RL) approach for coverage planning of unexplored areas with obstacles applied to fleets of Unmanned Aerial Vehicles (UAVs) . The goal is to reduce the steps and the energy needed to achieve full coverage, while avoiding collisions with fixed obstacles and other fleet members. This objective is accomplished through a Reinforcement Learning (RL)-based algorithm, through which UAVs are trained concurrently in simulated environment to maximize their individually explored areas while keeping uniformly distributed. This mixed cooperative-competitive behaviour is learned through a Convolutional Neural Network (CNN), running on each fleet unit, which outputs a suitable waypoint to be reached on the basis of all UAVs locations and already explored areas. Training process is developed in a novel approach, by gathering all UAVs’ trajectories collected during simulated episodes to update a shared policy function. In test phase, the learned behaviour is exploited in a decentralized way. Trained fleets are tested in simulated fields with different obstacle configurations, and performances are assessed in terms of both strategical distribution and exploration capabilities in maps with different complexity levels. Results show that fleets of 2 to 10 drones manage to reach full coverage of the test maps while spreading efficiently in the environment.
A Study on Autonomous Mechanisms for Swapping of Batteries on Unmanned Aerial Vehicles

In recent times, there has been a surge in the use of Unmanned Aerial Vehicles (UAVs) in the commercial and recreational space. Drones are highly sought after to act as replacements to traditional manned tasks ranging from surveillance to agriculture. An important factor that inhibits complete transition of drones into commercial ecosystem, is their limited battery life or endurance. Numerous attempts to enhance modern battery technology are under progress, however, a steady solution still seems far-fetched. To solve this problem with existing battery technology, rapid-action swapping of the drained battery pack can be projected as a viable option. The drained battery can be replaced with a newly charged battery and there can be a presence of a recharging station which would charge the spare batteries while the UAV is completing the mission. This paper is an ongoing study on various available battery swapping mechanisms, which are autonomous in nature to eliminate any human intervention.The final study will portray a comparative analysis of these battery swapping mechanisms and conclude with a few areas of development to make such a system available commercially.
Path Planning for a Dubins Agent with Resource Constraints and Dynamic Obstacles

This paper considers path planning with resource constraints and dynamic obstacles for an unmanned aerial vehicle (UAV), modeled as a Dubins agent. Ice data from the Aviation Weather Service was employed to create training data sets for learning dynamic weather (ice) phenomena. Dynamic mode decomposition (DMD) was used to learn and propagate the evolution of ice conditions at flight level. These obstacles were integrated into a Hybrid A* path-planner with backtracking to handle resource constraints. It is shown through numerical simulations that the agent is able to navigate around the dynamic obstacles to find the minimum time path to its goal.
Value of Potential Field in Reward Specification for Robotic Control via Deep Reinforcement Learning

Specifying proper state rewards plays an important role in obtaining reinforcement learning-based control policies. The objective of this paper is to design and evaluate a potential field-induced reward specification method in both virtual and real-world environments. In particular, we first describe the procedure to obtain state rewards by analyzing the relationship between potential fields and the principle of reward assignment. Then, we compare the performance of two deep reinforcement learning algorithms based on the potential field-induced state rewards with that of the classic potential field controller in both AirSim and real-world indoor drone testing environments. The comparison demonstrates the value of potential fields in reward specification while also suggesting the limitations, which will be further investigated as {one} part of our future work.