Reviewerly/exHarmony huggingface dataset:
Deep Learning: An Empirical Study on Kimia Path24

Deep learning has a large interest in medical image analysis as studies have shown several machine learning algorithms were successful in predicting disease. However, more work in needed to better understand the batch size, epoch, and learning rates. An empirical study of image processing with deep learning was conducted on the KIMIA path24 dataset. The rotation, width shifting, height shifting shear range, horizontal flip, and fill mode was used. The network was trained and validated by a total of 22,591 images from the KIMIA path24 dataset. ReLU was used for the convolution layer and softmax for the fully connected layer. Results found the batch size is inversely proportional to the network accuracy, the accuracy of a deep learning network is directly proportional to the number of epochs it passes through, and the learning rate does not bring any change to the network. The network performs best within a preferred learning rate.
Deep-learning Based Denoising and Enhancement in Video Image of Approach Channel

Due to the ubiquitous video sensor technology, it has become possible to build intelligent information system for the supervision of the approach channel. However, at present, there is still a technical problem to be solved, that is, the low-visibility video images taken in low-light environment have low brightness, low contrast and low signal-to-noise ratio. Meanwhile, they are also mixed with a lot of noise, which makes data extraction difficult. We propose a video image denoising and enhancement network based on deep learning, named DeNet, to improve the quality of low-light video images. DeNet consists of two parts: the first part is a deep residual network, which we named D-Net, to denoise the original video image. In the second part, we connect a D-Net network in parallel and add the normalization layer, which is used to fuse the features of the denoised image with the original image, and eventually realize the denoising and enhancement function. Experimental results on synthetic and real port test data sets show that our proposed method is superior to many advanced methods at present, and can meet the requirements of video denoising and enhancement of the approach channel.
A High-Performance Computing GNSS-aware Path Planning Algorithm for Safe Urban Flight Operations

The emergence and development of advanced technologies and vehicle types have created a growing demand for new forms of flight operations. These new and increasingly complex operational paradigms, such as Advanced and Urban Air Mobility (AAM/UAM), present regulatory authorities and the aviation community with several design-and-implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is to develop protocols that can integrate these operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive, autonomous, risk mitigation capability becomes critical to meet this challenge. This paper proposes and evaluates a computationally-efficient path planning approach to perform pre-flight planning and autonomous in-flight re-routing to minimize exposures to selected hazards. In our evaluation, hazards associated with degraded and missing critical GPS navigation data are considered. In this paper, we first present a high-performance computing path planning approach based on an adapted Bellman-Ford algorithm, developed in the CUDA programming language. Using the adapted path planning algorithm, we test this algorithm when encountering issues with GPS quality, and deliver an implementation that can produce flight paths that minimize exposure to risks, while maintaining a low computational burden. In our evaluation, the computation of periodic and aperiodic path updates are evaluated, prioritizing specific events as triggers for updates, based on changes to satellite availability. These critical events can lead to significant exposure to navigational hazards if not dealt with correctly.
SENetCount: An Optimized Encoder-Decoder Architecture with Squeeze-and-Excitation for Crowd Counting

Crowd management is critical to preventing stampedes and directing crowds, especially in India and China, where there are more than one billion people. With the continuous growth of the population, crowded events caused by rallies, parades, tourism, and other reasons occur from time to time. Crowd count estimation is the linchpin of the crowd management system and has become an increasingly important task and challenging research direction. This work proposes an optimized encoder-decoder architecture with the squeeze-and-excitation block for crowd counting, called SENetCount, which includes SE-ResNetCount and SE-ResNeXtCount. The deeper and stronger backbone network increases the quality of feature representations. The squeeze-and-excitation block utilizes global information to impress worthy informative feature representations and suppress unworthy ones selectively. The encoder-decoder architecture with the dense atrous spatial pyramid pooling module recovers the spatial information and captures the contextual information at multiple scales. The modified loss function considers the local consistency measure compared with the foregoing Euclidean loss function. The experiments on challenging datasets prove that our approach is competitive compared to thoughtful approaches, and analyses show that our architecture is extensible and robust.
Server-Side Distinction of User Mobility Using Machine Learning on Incoming Data Traffic

During two decades, there have been a revolution in the field of digital communication and internet access. Today, it became possible for users to access the internet while on the move through an infrastructure of high-speed mobile broadband networks. Technologies such as LTE and 5G became essential. Mobile broadband net-works allow mobility; connection reliability drops during movement. Thus, some failure intolerant processes, such as system updates, necessitates the utilization of a reliable connection. This paper introduces a model that predicts whether the user is mobile or stationery. This is done based on the traffic patterns at the server-side. Distinct network technologies entails distinct nature of traffic patterns. In this paper, machine learning is utilized at the server-side to allow differentiating between data transmitted by a stationary user and data transmitted by a mobile user at the server-side. Supervised training is utilized to train the model. Then, the model was tested and prediction accuracy of this model was 92.6 percent. Finally, the proposed system is a novel work and the first of its kind since it is the first to attempt to predict mobile network user’s mobility at the server-side by utilizing packets’ arrival patterns. The proposed system can be applied at mobile apps and allow them to collect data about the apps users mobility while using this service without needing to access the GPS. Also, it can be used network management and public safety.
General Decentralized Federated Learning for Communication-Computation Tradeoff

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by internode communications and local updates. In this paper, we propose a general DFL framework, which implements both multiple local updates and multiple inter-node communications periodically, to strike a balance between communication efficiency and model consensus. It can provide a general decentralized SGD analytical framework. We establish strong convergence guarantees for the proposed DFL algorithm without the assumption of convex objectives. Based on the balance of communication and computing costs, the convergence upper bound of DFL can be depicted explicitly. Experiment results based on MNIST and CIFAR-10 datasets illustrate the superiority of DFL over traditional decentralized SGD methods.
Power Dispatching Voice Backtracking System Based on Knowledge Base

Abstract With the rapid development of the scale and structure of the power grid, the power dispatching center has built a complete dispatching exchange communication network, which can meet the daily dispatching communication needs, but various problems are also emerging in the dispatching process. Aiming at the problem of high integration of physical power grid and information system, this paper proposes a voice backtracking system for power dispatching based on knowledge base, which includes knowledge base establishment, knowledge storage, knowledge question answering sorting and knowledge base front-end question answering. Firstly, the basic model of power dispatching knowledge processing is trained, and then the knowledge is extracted from the domain corpus, and finally, the knowledge question answering is realized by using the ranking model. Through the experimental comparison, it is proved that the accuracy of the method in this paper is higher than that of the method in the postprocessing by adding the MRC model in the pre-processing of knowledge extraction, and it is better than other methods in the comparative test. It provides technical support for power system voice dispatching management and provides a reference for other areas of voice management.
DTC with Interval Type-2 Fuzzy for Induction Motor Drive

Direct torque control (DTC) used for the quickresponse of induction motor by controlling the torque. Operation with conventional DTC has some disadvantages of variable switching frequency with high ripple in torque. So, DTC-SVPWM with Proportional Integral (PI) controller is used which provides constant switching frequency with low ripples in torque. With the induction motor drive’s poor dynamic performance, the PI direct torque control (PIDTC) produces significant torque ripples. To improve the torque ripples of induction motor drive (IMD), interval type 2 fuzzy (IT2F) direct torque control is used. In IT2FDTC, PI were replaced to fuzzy 2 controller. By making use of IF and THEN rules in the Mamdani method for IT2FDTC the data were used effectively. For type reduction in IT2FDTC centroid method is preferred. The IT2FDTC has less flux and torque ripples, as well as lower current total harmonic distortion (THD). Furthermore, when compared to PIDTC, IT2FDTC has a faster dynamic performance of the IMD. Continuous tuning of gain is required in PI controller; hence the performance would be difficult. So, fuzzy technique is proposed for better performance. In this proposed work the speed is settled early and torque ripples are minimized than the conventional PIDTC. The comparison table of PIDTC and IT2FDTC is also included.
Research on Real-time and High-precision Positioning Method of Ground Target through UAV Stereo Vision and Spatial Information Fusion

Background: Positioning accuracy is the most important index of the reconnaissance positioning system. Positioning accuracy involves many factors, such as the position, attitude and motion state of the flight platform, the pointing accuracy of the stable platform, and various coordinate transformations. A reasonable fusion strategy can guarantee stable positioning accuracy, so it is very important to study the precise fusion of machine vision, 3D geographic data and UAV airborne positioning information to realize the optimal combination of positioning data and complete the accurate and rapid positioning of the ground target. Methods: In this paper, a location model based on stereo vision and spatial information fusion method is proposed. It fully integrates visual information, satellite positioning information and spatial geographic information, greatly improves positioning accuracy, and through a real-time processing algorithm, significantly improves real-time positioning. Results: Through the related experiments, positioning accuracy and real-time ability of positioning could reach about three meters. Conclusion: The proposed real-time and high-precision positioning method of the ground target through UAV stereo vision and spatial information fusion is proposed showing significant improvement compared to other traditional methods.
Classification Methods for Labelled Data in Machine Learning

In the fast-growing web technology and breakthrough techniques of acquiring data from the user without letting them know and using this data to tackle any other problem, data scientist often has to go through a lot of hustles to get the correct and meaningful data. To minimize the efforts and work efficiently there are various ways in machine learning algorithms to do so and one of them is classification. The motive of this paper is to help and enlighten how one can work with labelled data and classification models and make the best use out of them either for study or for working on live projects which can be as sensitive as detecting cancer in a patient.
A Convex Optimization Approach to Real-Time Merging Control of eVTOL Vehicles for Future Urban Air Mobility

To the best of our knowledge, this is the first paper focused on real-time, model-based optimization for merging control of air vehicles under the urban air mobility (UAM) concept of operations. The merging control of UAM vehicles flying along different airways or air corridors would become a bottleneck in the development and implementation of future UAM systems. In this preliminary work, we consider the merging control of two electric vertical take-off and landing (eVTOL) vehicles and formulate an optimal control problem that incorporates the flight dynamics of the vehicle and collision avoidance constraint. Two objective functions, i.e., minimum flight time and minimum control cost, are considered in the problem formulation. The problems are first solved using a general-purpose optimal control solver (GPOPS). Then, we introduce several new control variables to reduce the nonlinearity of the dynamic system, apply a lossless convexification technique to relax the resulting nonconvex control constraint, linearize the nonlinear terms in the dynamics, and develop a sequential convex programming method as an alternative approach to the UAM merging control problem considered in this paper. Simulation results are presented to demonstrate the performance of the developed methods.
Offline Signature Verification: A Study on Total Variation versus CNN

This paper studies offline handwritten signature verification and the authenticity of a given signature. The research in this paper develops and compares two algorithms that predict forgery and authentic signatures based on the acquired set of images. For the first method, we use total variation technique as a measure of contiguity in the signatures to test its ability to verify the genuineness of a signature. Convolutional Neural Networks (CNN) was chosen as a second approach for signature validation. CNN is a powerful class of deep learning architecture. The algorithms described in the paper have been proven to be low cost as well as to make predictions with high accuracy in handwritten signature authentication.
Design of a Computer Stereo Vision Based Road Marking System

Existing methods to put white and yellow marking on the road are based on manual measurement of the road dimensions and manual marking of guide lines. It is a time consuming, cumbersome and labour intensive process. This was the motivation for this work to develop a system which can automatically apply road markings according to the width of the road. In this paper, a stereo-vision based road marking system is presented. Computer stereo-vision has been used to measure the width of the road which helps us decide the number of lanes on the road. To generate the disparity map, a semi-global matching algorithm has been used, incorporating the advantages of both local and global matching algorithms. Efficient algorithms have been used for camera calibration and to estimate the road width. For deciding the number of lanes, the standards laid down by Indian Road Congress (IRC) have been followed..
The Iso-RSA Cryptographic Scheme

This paper shows one way that can be used to strengthen the RSA cryptographic scheme which was published in 1978 by Ron Rivest, Adi Shamir, and Len Adleman. We use the isonumber theory of the first kind which was developed by Santilli where the unit one of normal arithmetic is replaced with a new unit called an isounit. The proposed cryptosystem possesses both characteristics of symmetric and asymmetric encryption and therefore can be seen as a hybrid public-key and private-key cryptographic system that benefits from both types of the cryptographic method with a substantial increase in security of the associated ciphers. Additionally, applications and implementations of the Iso-RSA cryptosystem for the encryption and decryption of information encoded into positive integers or binary digits of 0s or 1s are implemented, and simulation results show that our proposed cryptographic system produces highly secure encrypted information. A wide range of other applications in security mechanisms and security services which include data confidentiality, authentications, data integrity, digital signature, key exchange, and others can be implemented using our proposed Iso-RSA cryptographic scheme.
Improving Accessibility of Remote Drone Control with a Streamlined Computer Vision Approach

The purpose of this work is to develop a method for classifying hand signals and using the pre-diction output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNNs) were applied. The hand signals chosen were the numerical hand signs for one through five for two-dimensional movement with a separate idle signal, and a fist for land. A script was created to automate one minute of training image capture for each class. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18). The training process completed in three minutes and 43 seconds with five epochs and a final overall validation accuracy of over 99%. Implemented with the drone control, the classification performed as desired at approximately 60 predictions per second on desktop and 20 predictions per second on a Nvidia Jetson Nano.
Event Extraction for aviation accident reports through attention-based multi-label classification

In the aviation domain, historical aviation accidents are recorded to prevent catastrophe in the future. The national transportation safety board (NTSB) provides a large volume of accident reports and designs aviation event taxonomy to summarize what happens during the accidents. By utilizing the event taxonomy, we are able to design tools to analyze the accident reports, such as information retrieval systems, fault tree analysis, and causal chain analysis. However, one of the challenges is how to appropriately identify the accident reports with those event taxonomies. Traditionally, domain experts will label those events manually, which is time-consuming and subjective. In this paper, we propose the formulate the event labeling task as a sequence generation task. By adopting an advanced sequence generation framework, our experiment results show that we can achieve promising results on those frequent events.
GASLA: Enhancing the Applicability of Sign Language Translation

This paper studies an important yet overlooked applicability issue in existing American sign language (ASL) translation systems. With excessive sensing data collected for each ASL word already, current designs treat every to-be-recognized sentence as new and collect their sensing data from scratch, while the amounts of sentences and the data samples per sentence are large usually. It takes a long time to complete the data collection for each single user, e.g., hours to a half day, which brings non-trivial burden to the end users inevitably and prevents the broader adoption of the ASL systems in practice. In this paper, we figure out the reason causing this issue. We present GASLA atop the wearable sensors to instrument our design. With GASLA, the sentence-level sensing data can be generated from the word-level data automatically, which can be then applied to train ASL systems. Moreover, GASLA has a clear interface to be integrated to existing ASL systems for overhead reduction directly. With this ability, sign language translation could become highly lightweight in both initial setup and future new-sentence addition. Compared with around 10 per-sentence data samples in current systems, GASLA requires 2–3 samples to achieve a similar performance.
Evaluation of focal loss based deep neural networks for traffic sign detection

With advancements in autonomous driving, demand for stringent and computationally efficient traffic sign detection systems has increased. However, bringing such a system to a deployable level requires handling critical accuracy and processing speed issues. A focal loss-based single-stage object detector, i.e RetinaNet, is used as a trade-off between accuracy and processing speed as it handles the class imbalance problem of the single-stage detector and is thus suitable for traffic sign detection (TSD). We assessed the detector’s performance by combining various feature extractors such as ResNet-50, ResNet-101, and ResNet-152 on three publicly available TSD benchmark datasets. Performance comparison of the detector using different backbone includes evaluation parameters like mean average precision (mAP), memory allocation, running time, and floating-point operations. From the evaluation results, we found that the RetinaNet object detector using the ResNet-152 backbone obtains the best mAP, while that using ResNet-101 strikes the best trade-off between accuracy and execution time. The motivation behind benchmarking the detector on different datasets is to analyse the detector’s performance on different TSD benchmark datasets. Among the three feature extractors, the RetinaNet model trained using the ResNet-50 backbone is an excellent model in memory consumption, making it an optimal choice for low-cost embedded devices deployment.
Noise-Resilient Federated Learning: Suppressing Noisy Labels in the Local Datasets of Participants

Federated Learning (FL) is a novel paradigm of collaboratively training a model using local datasets of multiple participants. FL maintains data privacy and keeps local datasets confined to the participants. This poster presents a novel noiseresilient federated learning approach that suppresses the negative impact of noisy labels in the local datasets of the participants. The approach starts with the estimation of noise ratio without using prior information about the concentration of noisy labels. Next, the server generates different groups of participants using the estimated noise ratio. The FL-based training starts with the group having the least noise ratio, and subsequent groups are added later. We also introduce a noise robust loss function that incorporates dynamic variables to reduce the impact of noisy labels. The proposed approach reduces the overall training time and achieves adequate accuracy despite noisy labels.
Simulation Framework for Autonomous Search and Rescue UAVs

This paper presents the capabilities of the Simcenter-based simulation framework for modeling and validating hybrid autonomous aircraft systems and flight missions. This is accomplished by modeling the drone subsystems in Simcenter Amesim, setting up the simulation scenario and sensor modeling in Simcenter Prescan, and using a Simulink-based co-simulation framework. In addition, an existing object avoidance algorithm was included and adapted to the simulation to recreate and validate aircraft performance in emergency situations where an obstacle is detected along the desired flight trajectory.