Reviewerly/exHarmony huggingface dataset:
A Secure BlockChain Framework for IoT Healthcare

The medical industry is growing significantly along with emerging technologies like IoT., The fundamental idea behind integrating IoT into healthcare facilities is to enable it remotely accessible. The contact between patient and his doc tor is easy as well detection of disease. The purpose of this research is to provide a secure healthcare framework. This framework used blockchain technique to encrypt the patients' data on cloud servers. The blockchain is adopted in the healthcare industry since data is vulnerable to attacks and patients' record is having sensitive information that should be secure from attacks. For the last few years, different blockchain models have been put forth in the health system. However, these models' decentralized nature-a hallmark of blockchain technology-has led to the emergence of an issue known as illness overlaps as the chains expands. This problem is addressed using a blockchain, which has the ability to add a new block of data in the chain whenever there is an updating in patients' record. Additionally, multilayer blockchain approach is considered to secure the sensitive patient's data. This strategy protects data as well as maintain trust for the user. In this framework, AES 128-bit key is generated which is then provides support to SHA-256. Data is arranged into different blocks and then each block is encrypted to prevent unauthorized access. A system is created to demonstrate how the suggested approach is applied in the real healthcare sector. The efficiency of the proposed framework is compared with the RSA and DSA that are the two widely used encryption methods. Such algorithms, which are essential to blockchain technology and are used in the proposed work, are later evaluated by comparing their performances to one another. According on research findings, the framework has the shortest encryption and decryption time, making its efficiency more reliable and effective. Additionally, it is more durable and effective.
A Secure Framework for IoT Healthcare Data Using Hybrid Encryption

Internet of Things (IoT) plays a vital role in the growth of medical healthcare industry. Therefore, to secure the medical data as well as privacy of users is of major concern. In this paper a hybrid novel encryption technique is used to secure the medical data in IoT infrastructure. In this paper, hybrid encryption of Elliptic Curve Cryptography (ECC), Serpent and Advanced Encryption Standard (AES) is used to improve the security measures as well as data integrity of medical healthcare data. Security Analysis as well as performance comparisons are illustrated in this paper to prove the efficiency of proposed method. It is clearly shown in results that the proposed hybrid encryption method is more efficient than the state-of-art method.
Rotating Machinery Fault Diagnosis Based on Multi-sensor Information Fusion Using Graph Attention Network

Multi-sensor information acquisition system can reflect the operation status of machinery more comprehensively and reliably, but also demands higher requirements on data analysis algorithms. Unlike previous deep learning models, the emerging Graph Neural Network (GNN) has a remarkable performance in mining graph structure and patterns, effectively integrating multiple node relationships and features. This paper presents a fault diagnosis algorithm based on multi-sensor information fusion using the modified Graph Attention Network-GATv2. Firstly, the dependencies between multi-sensor signals are explicitly extracted by the Grow-Shrink (GS) algorithm, where the topology of the constructed graph can characterize different failure states of the equipment. During the aggregation process, the attention mechanism in the GATv2 assigns higher weights to informative nodes for the effective fusion of multi-sensor information. Experiments show that the proposed diagnosis framework can yield more expressive multi-sensor representations, and the diagnostic accuracy is improved significantly compared to the single-sensor graph.
Performance Analysis Of Dual Axis Solar Tracking System Actuated Through Serial Manipulators

Solar power being the most promising form of renewable energy, sun trackers significantly increase the photovoltaic (PV) system's ability to generate power. A dual-axis solar tracker is proposed here in order to demonstrate effective solar power. To maximize power output, the tracker actively monitors the sun and adjusts its location at the desired angle for maximum output. Light dependant resistors and Arduino-operated control circuit drive linear manipulators for the movement of solar panels, a cloud monitoring system is attached here for the power generation and load of varying information. Static and dynamic variations of the panel were done and maximum power and efficiency was recorded and analyzed.
A Multi-Layer Data Security Model in Cloud

The field of cloud computing is constantly growing. Users can use cloud to access the resources of the computer system without directly maintaining them. In order to deal with current security and privacy issues, this research main objective is to provide a data security model based which is based on hybrid encryption and steganography. A multi stage data security model is used which is based on Rivest-Shamir-Adleman (RSA), identity-based encryption (IBE), Advanced Encryption Standard (AES), and Least Significant Bit steganography (LSB). The four phases includes data sharing, data backup and recovery, steganography, and data encryption to protect and secure data. By defending data integrity, confidentiality, and privacy from intruders, this method increases data redundancy on cloud, flexibility of cloud, efficiency, and security of cloud.
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks

We propose a new design of a neural network for solving a zero shot super resolution problem for turbulent flows. We embed Luenberger-type observer into the network’s architecture to inform the network of the physics of the process, and to provide error correction and stabilization mechanisms. In addition, to compensate for decrease of observer’s performance due to the presence of unknown destabilizing forcing, the network is designed to estimate the contribution of the unknown forcing implicitly from the data over the course of training. By running a set of numerical experiments, we demonstrate that the proposed network does recover unknown forcing from data and is capable of predicting turbulent flows in high resolution from low resolution noisy observations.
Accelerated Performance and Accelerated Learning with Discrete-Time High-Order Tuners

We consider two high-order tuners that have been shown to have accelerated performance, one based on Polyak’s heavy ball method and another based on Nesterov’s acceleration method. We show that parameter estimates are bounded and converge to the true values exponentially fast when the regressors are persistently exciting. Simulation results corroborate the accelerated performance and accelerated learning properties of these high-order tuners in comparison to algorithms based on normalized gradient descent.
Informative Path Planning in Random Fields via Mixed Integer Programming

We present a new mixed integer formulation for the discrete informative path planning problem in random fields. The objective is to compute a budget constrained path while collecting measurements whose linear estimate results in minimum error over a finite set of prediction locations. The problem is known to be NP-hard. However, we strive to compute optimal solutions by leveraging advances in mixed integer optimization. Our approach is based on expanding the search space so we optimize not only over the collected measurement subset, but also over the class of all linear estimators. This allows us to formulate a mixed integer quadratic program that is convex in the continuous variables. The formulations are general and are not restricted to any covariance structure of the field. In simulations, we demonstrate the effectiveness of our approach over previous branch and bound algorithms.
A Quantum Optimal Control Problem with State Constrained Preserving Coherence

In this work, we address the problem of maximizing fidelity in a quantum state transformation process controlled in such a way as to keep decoherence within given bounds. We consider a three-level Λ-type atom subjected to Markovian decoherence characterized by non-unital decoherence channels. We introduce fidelity as the performance index for the quantum state transformation process since the goal is to maximize the similarity of the final state density operator with the one of the desired target state. We formulate the quantum optimal control problem with state constraints where the latter reflect the fact that the decoherence level remains within a pre-defined bound. Optimality conditions of Pontryagin’s Maximum Principle in Gamkrelidze’s form are given. These provide a complete set of relations enabling the computation of the optimal control strategy. This is a novel approach in the context of quantum systems.
Vehicle Detecting and Tracking Application Based on YOLOv5 and DeepSort for Bayer Data

Recently, with the advancement of vehicle electronics hardware and the rapid development of artificial intelligence technology, intelligent transportation technology has begun to play a very significant role in the development of vehicle technology. Vehicle tracking technology based on deep learning has also received more and more attention, especially since the rapid development of deep neural networks. A significant contribution of this paper is an evaluation of the performance of neural networks in relation to the type of input data, and an exploration of future development possibilities. Based on YOLOv5 and DeepSort, a vehicle detection and tracking algorithm was chosen to test the algorithm's performance on the Bayer data. The results are discussed and analyzed.
Embedding Adaptive Features in the ArduPilot Control Architecture for Unmanned Aerial Vehicles

The operation of Unmanned Aerial Vehicles (UAVs) is often subject to state-dependent alterations and unstructured uncertainty factors, such as unmodelled dynamics, environmental weather disturbances, aerodynamics gradients, or changes in inertia and mass due to payloads.While a large number of autopilot solutions have been proposed to operate UAVs, none of these solutions is able to counteract the effects of state-dependent and unstructured uncertainties online by parameter estimation and adaptive control techniques.This work presents a systematic integration of adaptive control into ArduPilot, a popular open-source autopilot suite maintained by a large community of UAV developers.Adaptation features are embedded in the ArduPilot control structure without altering the original architecture, to allow users to use the autopilot suite as usual.Tests show that the proposed adaptive ArduPilot provides consistent improved performance in several uncertain flight conditions.The source code of the proposed adaptive ArduPilot is released at https://github.com/Friend-Peng/Adaptive-ArduPilot-Autopilot. I. INTRODUCTIONThe possible applications of Unmanned Aerial Vehicles (UAVs) have been increasing steadily, so that several companies and research centers are experimenting the use of UAVs in daily-life tasks, e.g. for last-mile delivery of goods, monitoring of traffic systems, inspection of infrastructures, and so on.At the same time, realizing these tasks is not trivial, as the existence of gaps in guidance, navigation, and control of UAVs is recognized even in simpler tasks [1].Nowadays, a large number of off-the-shelf autopilots propose guidance, navigation, and control solutions for UAVs: examples include PX4, ArduPilot, AscTec, Navio2, among others [2]- [4].Despite these autopilots being maintained by large communities, their capabilities of dealing with uncertainties in the UAV or in its environment are still limited [5].These limits become more substantial as the tasks become more complex and unstructured.
Balanced Cooperative Target Search of Mobile Sensor Fleet under Localization Uncertainty

This paper deals with the problem of multiple sensors installed on mobile platforms cooperating to search for the target while each sensor is subject to localization uncertainty. The balance between a couple of conflicting objectives: i) suppressing the growth of self-localization uncertainty as an individual mobile agent; ii) reducing target uncertainty as a team, is sought under the information-theoretic framework. The conventional particle filter approach that concerns only the target state is augmented with self-localization by jointly estimating both. The self-localization is facilitated by introducing additional radio signals emitted from the origin, which should be a practical assumption considering limited resources and the restricted capability of small mobile sensors in case of a GNSS outage. The proposed method is featured with the numerical calculation of mutual information based on the principle of kernel conditional density estimation, and the unified comparison scheme between conflicting objectives. The numerical experiment shows that the proposed probing control law can automatically switch between target ascertaining and observability enhancing based on the mutual information-based utility function in a distributed fashion.
Deep Learning based Face Recognition for Security Robot

For indoor security robots, face recognition is an important ability. However, face recognition is suffered from the limitation by environment uncertainties, the factors including perceptual aliasing, occlusion, illumination changes and significant viewpoint changes. These uncertainties will affect the recognition accuracy and processing time, which will cause the security concerns. This paper proposes a convolutional neural networks-based face recognition system for the mobile robots to perform visual perception and control tasks. The trained model proposed in this paper (i.e., FaceNet) is compared and tested against two different algorithms, VGGNet and AlexNet. With image streaming, images are transferred to the cloud for GPU computing. In addition, the Cartographer SLAM algorithms is used for the indoor simultaneous localization and mapping. The experimental results show that the accuracy of proposed face recognition system under the conditions of four different illumination is 88%, which proves the feasibility of the method. Through the cloud GPU, the local computation and processing time can be reduced. The established mobile robot system can perform the indoor navigating and simultaneous localization and mapping.
Performance Analysis of Fault-Tolerant Multiagent Coordination Mechanisms

Performance evaluation of multi-agent systems (MAS) embraces several challenges due to uncertain operational environments, such as software/hardware failures and unfaithful communications that facilitate the spread of deceptive messages. One way to smooth the impact of reliability and security potential issues in MAS is to enforce different coordination mechanisms among agents (i.e., coordinating multiple agents that need to perform a sequence of actions to maximize a system-level reward), and evaluate their efficiency. In this paper, we propose a tool-based approach, namely COORDINATE, that simulates and compares the performance characteristics of different coordination mechanisms for MAS while considering fault-tolerant and corrupted agents. A Smart Hospital is adopted as illustrative example to show the need of performance-based analysis results pointing out which coordination mechanism is more efficient. Experimental results indicate that the inter-arrival time of tasks to be accomplished, the failure probability of agents, and the ratio of faithful to malicious agents contribute to determining the efficiency of different coordination mechanisms. When varying these parameters in the considered scenarios, the system latency can be reduced up to 4.2× by selecting the optimal coordination mechanism. This way, software developers are informed on the system peculiarities that trigger the switching among such coordination mechanisms for a performance-based optimal solution.
Uncertainty is not sufficient for identifying noisy labels in training data for binary segmentation of building footprints

Obtaining high quality labels is a major challenge for the application of deep neural networks in the remote sensing domain. A common way of acquiring labels is the usage of crowd sourcing which can provide much needed training data sets but also often contains incorrect labels which can affect the training process of a deep neural network significantly. In this paper, we exploit uncertainty to identify a certain type of label noise for semantic segmentation of buildings in satellite imagery. That type of label noise is known as “omission noise,” i.e., missing labels for whole buildings which still appear in the satellite image. Following the literature, uncertainty during training can help in identifying the “sweet spot” between generalizing well and overfitting to label noise, which is further used to differentiate between noisy and clean labels. The differentiation between clean and noisy labels is based on pixel-wise uncertainty estimation and beta distribution fitting to the uncertainty estimates. For our study, we create a data set for building segmentation with different levels of omission noise to evaluate the impact of the noise level on the performance of the deep neural network during training. In doing so, we show that established uncertainty-based methods to identify noisy labels are in general not sufficient enough for our kind of remote sensing data. On the other hand, for some noise levels, we observe some promising differences between noisy and clean data which opens the possibility to refine the state-of-the-art methods further.
AOA: Adaptive Overclocking Algorithm on CPU-GPU Heterogeneous Platforms

Abstract Although GPUs have been used to accelerate various convolutional neural network algorithms with good performance, the demand for performance improvement is still continuously increasing. CPU/GPU overclocking technology brings opportunities for further performance improvement in CPU-GPU heterogeneous platforms. However, CPU/GPU overclocking inevitably increases the power of the CPU/GPU, which is not conducive to energy conservation, energy efficiency optimization, or even system stability. How to effectively constrain the total energy to remain roughly unchanged during the CPU/GPU overclocking is a key issue in designing adaptive overclocking algorithms. There are two key factors during solving this key issue. Firstly, the dynamic power upper bound must be set to reflect the real-time behavior characteristics of the program so that algorithm can better meet the total energy unchanging constraints; secondly, instead of independently overclocking at both CPU and GPU sides, coordinately overclocking on CPU-GPU must be considered to adapt to real-time load balance for higher performance improvement and better energy constraints. This paper proposes an Adaptive Overclocking Algorithm (AOA) on CPU-GPU heterogeneous platforms to achieve the goal of performance improvement while the total energy remains roughly unchanged. AOA uses the function $$F_k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>k</mml:mi> </mml:msub> </mml:math> to describe the variable power upper bound and introduces the load imbalance factor W to realize the CPU-GPU coordinated overclocking. Through the verification of several types convolutional neural network algorithms on two CPU-GPU heterogeneous platforms (Intel $$^\circledR $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>®</mml:mo> </mml:msup> </mml:math> Xeon E5-2660 &amp; NVIDIA $$^\circledR $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>®</mml:mo> </mml:msup> </mml:math> Tesla K80; Intel $$^\circledR $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>®</mml:mo> </mml:msup> </mml:math> Core™i9-10920X &amp; NIVIDIA $$^\circledR $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>®</mml:mo> </mml:msup> </mml:math> GeForce RTX 2080Ti), AOA achieves an average of 10.7% performance improvement and 4.4% energy savings. To verify the effectiveness of the AOA, we compare AOA with other methods including automatic boost, the highest overclocking and static optimal overclocking.
Measuring the Readability of Geometric Proofs: The Area Method Case

Abstract Using an approach, inspired by our modernisation of Lemoine’s Geometrography , this paper proposes a new readability criterion for formal proofs produced by automated theorem provers for geometry. We analyse two criteria to measure the readability of a proof: the criterion given by Chou et al. and the one given by Wiedijk. After discussing the limitations of these two criteria, we introduce a novel approach, which provides a new criterion. We conclude discussing some future work.
A Hybrid Campus Security System Combined of Face, Number-Plate, and Voice Recognition

Campus or institution security has been a prominent area of study in recent decades. Facial identification, voice verification, and vehicle license plate recognition have all been used individually in the literature to prevent attackers from entering the facility. Although several academics have agreed that a hybrid recognition system may significantly increase security, hybrid systems are not often discussed in the literature. To overcome this issue, we presented a hybrid driver and vehicle identification module in this study, which can detect both the driver and the vehicle. We applied face recognition and speech verification for driver recognition. Multi-task Cascaded Convolutional Networks were used to crop the faces for facial recognition, while FaceNet was employed for face identification. A three-layer Long Short-Term Memory model was used for voice verification. Finally, Tesseract has been used for car number plate identification. According to the results of the experiments, the suggested approach is capable of detecting both drivers and cars with 0 percent mistake every time, which is a critical improvement for assuring the security of the institutions.