Reviewerly/exHarmony huggingface dataset:
Video segmentation on static and dynamic textures using a quaternion framework

This paper presents a new method for video segmentation using deep learning neural networks in the quaternion space into sets of objects, background, static and dynamic textures. We introduce a novel quaternionic anisotropic gradient (QAG) which can combine the color channels and the orientations in the image plane. The local polynomial estimates and the ICI rule are used for QAG calculation. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. To solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. Using the QAGs, we extract the local orientation information in the color images. Second, to improve the segmentation result we applied neural network to this derived orientation information. The presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. Experimental comparisons to state-of-the-art video segmentation methods demonstrate the effectiveness of the proposed approach.
FFDA: A novel Four-Factor Distributed Authentication mechanism

This article presents a novel multi-factor authentication mechanism that makes the authentication process robust to several attacks. The mechanism is using several databases to store the user key and an external USB device that the user carries with him. The system is divided into several groups, each one storing a part of the fragmented user key, as an additional security measure. The proposed mechanism has four factors of authentication but one the same time is easy to use and apply to many modern systems that are used in maritime.
A Blockchain-based System for Secure and Distributed Virtual Network Functions Orchestration

Service provisioning in next-generation networks, such as 5G and 6G, relies on virtualization to carry out multi-domain and multi-tenant connections. In these scenarios, virtual network functions (VNF) orchestration becomes susceptible to security threats once trust between peers cannot be assumed. This paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> proposes a blockchain-based system for an agile, secure, and distributed provisioning of virtual network functions in scenarios with multiple administrative domains. Our proposal employs smart contracts to deliver all stages of a service-level-agreement management life cycle automatically. We develop, implement, and evaluate a prototype of the proposed system using smart contracts running on Hyperledger Fabric. The performance evaluation results show that the system guarantees high-rate VNF provisioning, reaching hundreds of slice requests per second in a trustful way.
Research on Volleyball Players Tracking Based on Improved DeepSORT

In order to reduce the problems of identity switching caused by occlusion in volleyball video, we propose a method named Vol-DeepSORT based on DeepSORT for tracking volleyball players. According to the principle that the number of players is unchanged in a fixed playing area, we improved the DeepSORT algorithm that rematches the new tracks with the vanishing tracks by the minimum Euclidean Distance, so as to realize the recovery of the player identity. We use the improved algorithm Vol-DeepSORT combined with YOLOv5 to detect and track the players. Experimental results show that the MOTA of Vol-DeepSORT is 90.07%, and the frequency of identity switching is reduced by 30% compared with the original DeepSORT. The proposed algorithm can reduce the frequency of identity switching on players to a certain extent and achieve a good tracking performance.
Photonic-Aware Neural Network: a fixed-point emulation of photonic hardware

In this paper we introduce Photonic-Aware Neural Networks, i.e., neural network models compliant with the underlying photonic implementation. By exploiting proper training strategies, we demonstrate a limited F1-score degradation on a surface defect detection problem.
Semantic Segmentation of Low Earth Orbit Satellites using Convolutional Neural Networks

Ground-based imaging of objects in Low Earth Orbit (LEO) is complicated by atmospheric turbulence, which make it difficult to identify key features or components on the object of interest. Many automated image reconstruction techniques are in use, but expert labor is needed to subjectively discern and identify truth features on a partially reconstructed image. In this paper, we present a deep learning approach for semantic segmentation of ground-based images of LEO objects. We investigate the performance under various atmospheric turbulence strengths in terms of the Fried parameter (<tex>$r_{0}$</tex>) and show the viability of this method.
Application of fuzzy inference system (FIS) for assessment and predication of compressive asset of concrete containing fly ash

Fuzzy Inference System (FIS) for forecasting the 90 days compressive asset of cements containing low and high limes cinders have been modified by using MATLAB2016a. For motivation behind to modified this model is 160 specimens and 48 different mixes found form the literatures. Water, cover of the day, crushed stone-I (3–7 mm), Portland cement, Fly Ash (FA) replacement ratio has been used as input parameter in the FIS modelling system. The output is the boundary which is compressive strength of cement. The preparation and testing results advocate that the FIS modelling system have the durable potentially predict 90 days compressive strength of concretes fly ash and maybe used for some other purpose also. This FIS system provide the reasonable results with the comparison of previous studies.
SC-NET: Spatial and Channel Attention Mechanism for Enhancement in Face Recognition

This paper proposes a spatial and channel attention mechanism module called SC-NET which is a lightweight yet effective method for deep convolutional neural networks. Recently, channel attention mechanism has been researched extensively and proved to be efficient in improvement of performance. However after carrying out rigorous empirical analysis, we find that channel attention and spatial channel attention improve the network&#x0027;s performance more efficiently. Therefore we incorporate both spatial information and cross-channel interaction in our SC-NET architecture. SC-NET is validated through extensive experiments on CASIA- WebFace and VGGFace2 datasets. By comparing our SC-NET with other methods, SC-NET has the best performance. Then when we apply our SC-NET to FaceNet(A Unified Embedding for Face Recognition and Clustering), FaceNet with SC-NET has achieved higher recognition accuracy than the original FaceNet and has reached state-of-the-art performance.
Improvement and optimization of consensus algorithm based on PBFT

This paper proposes an improved consensus algorithm based on PBFT(EBCR-PBFT). Firstly, The Modified Random Select(MRS) function is used to perform preliminary screening of network nodes, so as to solve the problem of multiple and redundant network nodes. Secondly, based on the idea of clustering, logical stratification of blockchain network nodes can reduce communication times and improve network efficiency. Thirdly, the core module of MRS is optimized by FPGA to further improve the efficiency of the algorithm. At the same time, the improved dynamic reputation evaluation model is introduced to evaluate and feedback the node behavior in time to ensure the smooth operation of the system. Experiments show that the proposed and optimized consensus algorithm can effectively improve the consensus efficiency, timely punish malicious nodes, increase the reliability of nodes, communication overhead, throughput, delay and other high performance, has a certain application value.
Auxiliary Regularization Study for Accurate 3D LiDAR Object Detection

LiDAR object detection is a fundamental task for understanding the 3D world. State-of-the-art 3D detectors tend to apply novel regularization approaches to achieve better ranks in challenging leaderboards. However, there is a key question that how these auxiliary regularizations affect the detector without bells and whistles. In this paper, we apply several popular auxiliary losses and guide approaches to a highly popular and widely used 3D detection baseline model to explore how these mechanisms affect 3D object detection. Extensive experiments on KITTI from different perspectives demonstrate that these auxiliary regularizations can bring benefits to the 3D LiDAR object detection.
Self-adaptive Machine Learning Systems: Research Challenges and Opportunities

Today’s world is witnessing a shift from human-written software to machine-learned software, with the rise of systems that rely on machine learning. These systems typically operate in non-static environments, which are prone to unexpected changes, as is the case of self-driving cars and enterprise systems. In this context, machine-learned software can misbehave. Thus, it is paramount that these systems are capable of detecting problems with their machined-learned components and adapting themselves to maintain desired qualities. For instance, a fraud detection system that cannot adapt its machine-learned model to efficiently cope with emerging fraud patterns or changes in the volume of transactions is subject to losses of millions of dollars. In this paper, we take a first step towards the development of a framework for self-adaptation of systems that rely on machine-learned components. We describe: (i) a set of causes of machine-learned component misbehavior and a set of adaptation tactics inspired by the literature on machine learning, motivating them with the aid of two running examples from the enterprise systems and cyber-physical systems domains; (ii) the required changes to the MAPE-K loop, a popular control loop for self-adaptive systems; and (iii) the challenges associated with developing this framework. We conclude with a set of research questions to guide future work.
Development of Blockchain In Smart Grid Using IoT

For Smart Grid management, people rely on Trusted Third Party (TTP) administration-based Centralized systems, which lack security, faith, immutability, and clarity. This paper proposes a Smart Grid management system based on Blockchain and IoT, taking advantage of the ’Ethereum server’ capabilities to solve these problems. To control access privileges, a smart contract is used, and a customized version of IPFS software is used to enforce the established access-control list. To secure Grid management, an application framework for a secure decentralized smart Grid system is proposed using Ethereum and IoT. Every transaction was hashed using Ethereum Server, and identities were authenticated using Metamask to cryptographically recognize account ownership in the Blockchain system. In the private Ethereum network, a gas consumption-based outcome analysis is performed. It achieves transparency, security-regulated access, and process quality, all of which indicate that this activity is more effective.
Leveraging hierarchical semantic‐emotional memory in emotional conversation generation

Abstract Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the generation of semantically coherent and emotionally reasonable responses. However, most previous works generate emotional responses solely from input posts, which do not take full advantage of the training corpus and suffer from generating generic responses. In this study, we introduce a h ierarchical s emantic‐ e motional m emory module for e motional c onversation generation (called HSEMEC), which can learn abstract semantic conversation patterns and emotional information from the large training corpus. The learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation generation. Comprehensive experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual evaluation. For reproducibility, we release the code and data publicly at: https://github.com/siat‐nlp/HSEMEC‐code‐data .
Short‐term prediction of photovoltaic power generation based on neural network prediction model

Abstract Real‐time monitoring and accurate prediction of photovoltaic (PV) power generation operation parameters are essential to ensure stable operation. In this paper, a set of online PV power generation parameter measurement and monitoring devices characterized by simple structure, high sampling accuracy, small data fluctuations, and ease of measurement, are designed. Sensors based on the zero‐flux principle are employed in the real‐time collection of the output electrical signals in the process of PV power generation, realizing the accurate collection of electric parameter signals. Next, the basic structure and working principle of PV cells are analyzed, a mathematical model of PV cells for engineering purposes is established, a wavelet neural network is selected to predict the short‐term PV power generation, and particle swarm optimization and adding momentum are used to optimize the weight of wavelet neural network (WNN) as well as the parameters of the wavelet basis function. Finally, the historical power generation data and meteorological data of the power station are taken as the training samples to train and simulate the prediction sub‐models of different weather types to verify the effectiveness and accuracy of the PV power generation short‐term prediction model for optimizing the WNN based on the particle swarm optimization algorithm. The research results of this paper can realize real‐time monitoring of the output parameters and accurate prediction and evaluation of power generation during the operation of the PV‐power‐generation system.
Modeling and Analysis of Intermittent Federated Learning Over Cellular-Connected UAV Networks

Federated learning (FL) is a promising distributed learning technique particularly suitable for wireless learning scenarios since it can accomplish a learning task without raw data transportation so as to preserve data privacy and lower network resource consumption. However, current works on FL over wireless networks do not profoundly study the fundamental performance of FL over wireless networks that suffers from communication outage due to channel impairment and network interference. To accurately exploit the performance of FL over wireless networks, this paper proposes a novel intermittent FL model over a cellular-connected Unmanned Aerial Vehicle (UAV) network, which characterizes communication outage from UAV (clients) to their server and data heterogeneity among the datasets at UAVs. We propose an analytically tractable framework to derive the uplink outage probability and use it to devise a simulation-based approach so as to evaluate the performance of the proposed intermittent FL model. Our findings reveal how the intermittent FL model is impacted by uplink communication outage and UAV deployment. Extensive numerical simulations are provided to show the consistency between the simulated and analytical performances of the proposed intermittent FL model.
Multi-timescale Sensor Fusion and Control

Networked autonomous systems are a rapidly expanding area of research and development across academic, commercial, and military endeavors. Significant challenges exist in extending traditional detection and estimation methods to such distributed systems of sensors when we relax assumptions on full communications connectivity and global observability of the network. Global observability can be interpreted as a persistent coverage of all degrees of freedom associated with a object&#x0027;s feature vector - this can be satisfied by a combination of physical diversity of homogeneous sensors and/or diversity across sensing domains for heterogeneous sensors, and the role of resource allocation across the network is to determine configurations, and reconfigurations, of platforms that achieve said diversity. In a general heterogeneous sensor network, persistent global observability across the entire area of operations requires control decisions at a much longer timescale than the feature estimate updates that provide locally full rank observability. In this paper, we temporally separate the long-timescale resource allocation control process from the parameter estimation through the use of a decentralized Partially Observable Markov Decision Process (POMDP) control model that employs consensus estimates on object features as observations and benchmark this multi-timescale approach against centralized Linear Quadratic Gaussian (LQG) control for a fully connected network with simultaneous estimation and control updates.
Developing Algorithms for Image Steganography and Increasing the Capacity Depending on Choosing the Best Pixels

Steganography is a vital technique for transferring confidential information via an insecure network. In addition, digital images are used as a cover to communicate sensitive information. The Least Significant Bit (LSB) method is one of the simplest ways to insert secret data into a cover image. In this paper, the secret text is compressed twice by an Arithmetic coding algorithm, and the resulting secret bits are hidden in the cover pixels of the image corresponding to the pixels of each of the following three methods, one of three methods is used in each experiment: The first method, the edges of the image are modified to increase the number of edges, in the second method the lighter-colored regions are selected, and in the third method, the two methods are combined together to increase security and keep the secret message unrecognized. Hiding in each of the previous methods is done by using the LSB technique in the last 2-bit. The correction approach is used to increase the stego image's imperceptibility. The experimental results show that with an average message size of 29.8 kb, the average Peak Signal-to-Noise Ratio (PSNR) for the second proposed (Light regions) method equals 62.76 dB and for the third proposed (Edge and region) method equals 62.72 dB, which is a reasonable result when compared to other steganographic techniques.
A Lightweight LiDAR-Camera Sensing Method of Obstacles Detection and Classification for Autonomous Rail Rapid Transit

Detecting obstacles accurately is a vital part of realizing autonomous driving. But utilizing single sensors, for example, RGB camera or LiDAR itself, is difficult to meet the needs of autonomous driving under the condition of sparse environmental sensing data. In contrast, equipments that can collect data in large quantities are expensive and difficult to be utilized in practical applications. Hence, under the premise of ensuring low application costs, the difficult task is to obtain high-precision three-dimensional information of obstacles in real-time from sparse data. To overcome this problem, a real-time detection technique that combines LiDAR data and image information for obstacles detection is proposed in this article. It can detect vehicles close to the ART in sparse data in real-time. The sparse point cloud is de-noised initially and the ground is separated from the initial cloud data. The remained cloud, which includes surrounding vehicles, are processed by the clustering algorithm according to the structural relationship and scanning mechanism of the LiDAR. Then the processed LiDAR data is fused with the classification information generated from images based on a neural network to locate suspicious obstacles. The ratio values between the central values of vehicles and image width are obtained in the image coordinate system. Then the category information and ratio values are mapped on the processed LiDAR clusters during ART operation. The method is tested and verified in the real field. The evaluation results show that the proposed method can achieve an average accuracy of 85.5% for obstacle classification with a cost-effective sensing suit.
(Explainable) Artificial Intelligence in Aerospace Safety-Critical Systems

AI techniques, encompassing machine learning, have made enormous progress over the last decade with several models already implemented across various aerospace applications, such as, aircraft design, operation, production and maintenance, as well as air traffic control. But the question is: Are there any behaviour validations of such AI models which could help establish assurances that they will continue to perform as specified when deployed in a real-time environment? With Explainable AI (XAI), a sub-field of AI, possibilities of exposing complex AI models to human users/operators in an interpretable and understandable ways are opening. This paper tends to explore valid answers to such questions that perplex the Aerospace AI community in fully capturing the essence of complex AI models (the black boxes) through various known XAI approaches and classes. Accordingly, various techniques, for instance, white-box AI, Black-box AI, model agnostic, fuzzy logic, and knowledge graphs, are investigated to find their efficacy levels in terms of explainability. In addition, the XAI requirements are clearly laid down for safety-critical systems from the perspective of creators, guarantors, and interpreters. Finally, this paper puts forth a comparison of various degrees of explain ability with the standard elements of Intelligence Community Directive (ICD) to set out the capabilities of XAI that would be required to build trust in complex AI models.
Evaluation of Vehicle Quality Performance using Random forest in Comparison with KNN to measure the Accuracy, Recall, and Precision

The major goal of this paper is to detect the performance of Random Forest algorithm in detection of vehicle quality performance by equating it with the K-Nearest Neighbor algorithm (KNN). Materials and Methods: Proposed work uses a total sample size of 1208, and the dataset is collected from UCI repository with accurate quality 604 samples and inaccurate quality 604 samples with two groups. Group 1 with RF algorithm and group 2 with KNN. The samples are separated into training data (n = 906 [75%]) and test data (n = 302 [25%]). Calculation of samples is done using G power analysis with alpha (0.05), power (80%), and environment ratio are two different groups in clincalc. Results: In the proposed model Random Forest achieved accuracy, recall and precision of 90.38%, 92.53% and 82.08% respectively compared to 83.33%, 91.83% and 79.25% by the KNN algorithm with a significance of 0.000 (p&#x003C;0.05). Conclusion: In this research, Random Forest shows better results than the KNN algorithm in car evaluation dataset with more features considered.