Reviewerly/exHarmony huggingface dataset:
Bent functions in the partial spread class generated by linear recurring sequences

Abstract We present a construction of partial spread bent functions using subspaces generated by linear recurring sequences (LRS). We first show that the kernels of the linear mappings defined by two LRS have a trivial intersection if and only if their feedback polynomials are relatively prime. Then, we characterize the appropriate parameters for a family of pairwise coprime polynomials to generate a partial spread required for the support of a bent function, showing that such families exist if and only if the degrees of the underlying polynomials are either 1 or 2. We then count the resulting sets of polynomials and prove that, for degree 1, our LRS construction coincides with the Desarguesian partial spread. Finally, we perform a computer search of all $$\mathcal{PS}\mathcal{}^-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>PS</mml:mi> <mml:msup> <mml:mrow> <mml:mrow /> </mml:mrow> <mml:mo>-</mml:mo> </mml:msup> </mml:mrow> </mml:math> and $$\mathcal{PS}\mathcal{}^+$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>PS</mml:mi> <mml:msup> <mml:mrow> <mml:mrow /> </mml:mrow> <mml:mo>+</mml:mo> </mml:msup> </mml:mrow> </mml:math> bent functions of $$n=8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>8</mml:mn> </mml:mrow> </mml:math> variables generated by our construction and compute their 2-ranks. The results show that many of these functions defined by polynomials of degree $$d=2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> are not EA-equivalent to any Maiorana–McFarland or Desarguesian partial spread function.
FedDQ: A communication-efficient federated learning approach for Internet of Vehicles

Federated Learning (FL) has achieved great success in many intelligent applications of the Internet of Vehicles (IoV), however, a large number of vehicles and increasingly size of models bring challenges to FL-empowered connected vehicles. Federated Distillation (FD) has become a novel paradigm to address communication bottlenecks by exchanging model outputs among devices rather than model parameters. In this paper we investigate several key factors that affect the communication efficiency of FD, including communication frequency, soft-labels quantization, and coding methods. Based on the findings of the preceding analysis, we propose FedDQ, a communication-efficient federated distillation method. Specifically, we propose a controlled averaging algorithm based on control variates to solve the drift problem arising from local updates. Then, we design a new quantization approach and coding method to reduce overhead for both upstream and downstream communications. Extensive experiments on image classification tasks at different levels of data heterogeneity show that our method can reduce the amount of communication required to achieve a fixed performance target by around 2 or 3 orders of magnitude compared to benchmark methods while achieving equivalent or higher classification accuracy.
New robust hybrid Jarratt-Butterfly optimization algorithm for nonlinear models

The nonlinear system of equations (NSE) is the basis of many engineering and scientific models. However, these models must have accurate solutions to be successful. Several optimization algorithms, including Butterfly Optimization Algorithm (BOA), can be applied to solving NSE problems. However, optimization algorithms lack accurate solutions due to their limitations, including trapping at local optima and divergence problems. In this work, a novel hybrid approach that is called Jarratt Butterfly Optimization Algorithm (JBOA) is presented to solve NSE. The proposed JBOA is developed by combining Jarratt's iterative method and the BOA. Combining the two methods has enhanced BOA's search mechanism and convergence speed. It also overcomes Jarratt's limitations, including selecting initial values, trapping at local optima, and divergence problems. This results in solving NSE in more accurate solutions and fewer iterations. JBOA's efficiency was evaluated based on eight benchmark systems, two of which represent real-world applications. Further, JBOA was compared to several optimization algorithms, including the original BOA algorithm, Particle Swarm Optimization (PSO), Harris Hawk Optimization (HHO), Equilibrium Optimization (EO), and Ant Lion Optimizer (ALO). Furthermore, the performance of JBOA and Jarratt's method has been compared. Finally, the experimental results support the superiority of JBOA as it significantly outperformed all other compared optimization algorithms in all benchmark systems in terms of best solutions, average fitness values, stability, and convergence speed.
Self-supervised spectral clustering with exemplar constraints

As a leading graph clustering technique, spectral clustering is one of the most widely used clustering methods that captures complex clusters in data. However, some of its deficiencies, such as the high computational complexity in eigen decomposition and the guidance without supervised information, limit its real applications. To get rid of the deficiencies, we propose a self-supervised spectral clustering algorithm. In this algorithm, we define an exemplar constraint which reflects the relations between objects and exemplars. We provide the related analysis to show that it is more suitable for unsupervised learning. Based on the exemplar constraint, we build an optimization model for self-supervised spectral clustering so that we can simultaneously learn clustering results and exemplar constraints. Furthermore, we propose an iterative method to solve the new optimization problem. Compared to other existing versions of spectral clustering algorithms, the new algorithm can use the low computational costs to discover a high-quality cluster structure of a data set without prior information. Furthermore, we did a number of experiments of algorithm comparison and parameter analysis on benchmark data sets to illustrate that the proposed algorithm is very effective and efficient.
Motion-blurred image restoration framework based on parameter estimation and fuzzy radial basis function neural networks

• Overall structural framework designed for the restoration of motion-blurred images is proposed with the aid of PSO-based parameter estimation and image quality assessment. • The proposed Image Restoration Framework has a complete function which can effectively enhance the restored image quality. • Blur parameter estimation algorithm based on PSO (BPPO) is employed to optimize the motion-blurred parameter estimation. • A polynomial-based radial basis function neural network is used as image quality evaluation method to evaluate restored image quality for efficient classification. The restoration of motion-blurred images has always been a complex problem in image restoration. The current single blurred image algorithm cannot very well solve the estimation error of motion blur parameters. A comprehensive motion-blurred image restoration framework is proposed, which includes motion-blurred data generation, blur parameter estimation, and image quality assessment of restored images. First, we designed and used four image data sets with different degrees of blurring. We innovatively propose a blur parameter estimation algorithm based on the particle swarm optimization (B-PSO) algorithm. The Naturalness Image Quality Evaluator (NIQE) is used as the fitness function of the PSO algorithm. The framework also introduces a polynomial-based radial basis function neural network (P-RBFNN) as a new image quality assessment (IQA) method , with good image classification performance. Test results from public datasets show that the proposed framework can accurately estimate blur parameters. The peak signal-to-noise ratio (PSNR) reaches 29.976dB, the structural similarity (SSIM) reaches 0.9044, and the classification rate is 96%. The proposed restoration framework produces the best image restoration results.
Data-Driven Federated Autonomous Driving

Intelligent vehicles optimize road traveling through their reliance on autonomous driving applications to navigate. These applications integrate machine learning to extract statistical patterns and sets of rules for the vehicles to follow when facing decision-making scenarios. The immaturity of such systems, caused by the lack of a diverse dataset, can lead to inaccurate on-road decisions that could affect road safety. In this paper, we devise a decentralized scheme based on federating autonomous driving companies in order to expand their access to data and resources during the learning phase. Our scheme federates companies in an optimal manner by studying the compatibility of the federations’ dataset in the federations formation process, without exposing private data to rivalries. We implement our scheme for evaluation against other formation mechanisms. Experiments show that our approach can achieve higher model accuracy, reduce model loss, and increase the utility of the individuals on average when compared to other techniques.
Automatic Jamming Signal Classification in Cognitive UAV Radios

The integration of Cognitive Radio (CR) with Unmanned Aerial Vehicles (UAVs) is an effective step towards relieving the spectrum scarcity and empowering the UAV with a high degree of intelligence. The dynamic nature of CR and the dominant line-of-sight links of UAVs poses serious security challenges and make the CR-UAV prone to a variety of attacks as malicious jamming. Joint jammer detection and automatic jammer classification is a powerful approach against the physical layer threats by identifying multiple jammers attacking the network that realize a crucial stage towards efficient interference management. This paper proposes a novel method for joint detection and automatic classification of multiple jammers attacking with different modulation schemes. The method is based on learning a representation of the radio environment encoded in a Generalized Dynamic Bayesian Network (GDBN) whilst multiple GDBN models represent various jamming signals under different modulation schemes. The CR-UAV performs multiple predictions online in parallel and evaluates multiple abnormality measurements based on a Modified Markov Jump Particle Filter (M-MJPF) to select the best-fit model that explains the detected jammer and recognize the modulation scheme accordingly. The simulated results demonstrate that the proposed GDBN-based method outperforms Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Stacked Autoencoder (SAE) in terms of classification accuracy and achieves a higher degree of explainability of its own decisions by interpreting causes and effects at hierarchical levels under the Bayesian learning and reasoning processes.
A Framework for Infectious Disease Monitoring With Automated Contact Tracing—A Case Study of COVID-19

Throughout human history, deadly infectious diseases emerged occasionally. Even with the present-day advanced healthcare systems, the COVID-19 has caused more than six million deaths worldwide (as of 27 July 2022). Currently, researchers are working to develop tools for better and effective management of the pandemic. “Contact tracing” is one such tool to monitor and control the spread of the disease. However, manual contact tracing is labor-intensive and time-consuming. Therefore, manually tracking all potentially infected individuals is a great challenge, especially for an infectious disease like COVID-19. To date, many digital contact tracing applications were developed and used globally to restrain the spread of COVID-19. In this work, we perform a detailed review of the current digital contact tracing technologies. We mention some of their key limitations and propose a fully integrated system for contact tracing of infectious diseases using COVID-19 as a case study. Our system has four main modules—1) case maps; 2) exposure detection; 3) screening; and 4) health indicators that take multiple inputs like users’ self-reported information, measurement of physiological parameters, and information of the confirmed cases from the public health, and keeps a record of contact histories using Bluetooth technology. The system can potentially evaluate the users’ risk of getting infected and generate notifications to alert them about the exposure events, risk of infection, or abnormal health indicators. The system further integrates the Web-based information on confirmed COVID-19 cases and screening tools, which potentially increases the adoption rate of the system.
A Hybrid Approach to Recommending Universal Decimal Classification Codes for Cataloguing in Slovenian Digital Libraries

In this article we present a hybrid approach to recommending the Universal Decimal Classification (UDC) codes for unclassified documents. By recommending UDC codes to librarians, we can provide the decision support as part of a semi-automatic cataloguing process. As current approaches to recommending UDC codes are scarce and limited to certain fields of expertise, our motivation was to create a hybrid approach which covers all fields of expertise within the UDC hierarchy. The cascade hybrid approach combines the BM25 ranking function with a multi-label classifier based on a BERT (Bidirectional Encoder Representations from Transformers) deep neural network architecture. Additionally, lists of the most commonly used UDC codes within a document’s origin organization are used as a final content-based filtering method. The BM25 ranking function is used to create an initial list of recommendations. The first cascade step re-ranks the initial list of recommendations using the list of recommendations produced by the multi-label BERT-based classifier. The second cascade step re-ranks the resulting recommendation list from the first cascade step using a list of most commonly used UDC codes within the document’s organization. Finally, post-processing steps are applied to obtain the final list of recommended UDC codes. We present in detail the UDC structure, the used text corpus of documents and the functioning of our hybrid recommendation approach. We perform the analysis of the generated recommendation lists for the corpus from the Slovenian Open-Access Infrastructure using the metrics hit-ratio, normalized discounted cumulative gain, mean reciprocal rank and mean average precision. Our hybrid recommendation approach improves reference scores of individual methods for these metrics.
Skeleton Extraction and Greedy-Algorithm-Based Path Planning and its Application in UAV Trajectory Tracking

Space research is of great significance to increasingly decentralized and distributed space systems, and path planning in space systems has become a research hotspot for maintaining their safety, security, and reliability. To explore the passable path connecting the starting point and the target point, and optimize a smooth trajectory that can be tracked by unmanned aerial vehicles (UAVs) in 3-D space, a skeleton-extraction- and greedy-algorithm-based path planning has been proposed to guide the flight of UAVs. First, the rapidly exploring random tree (RRT) has been introduced for path search. To speed up the path search process, the spatial skeleton extraction method has been introduced to calculate the skeleton of free space The greedy algorithm has been utilized to increase the RRT expansion and reduce unnecessary bends in the path. The skeleton extraction and greedy-algorithm-based Lazy RRT and RRT-Connect have been proposed to build compared experiments. Second, the minimum snap has been applied to generate a smooth flight trajectory, and the flight time is allocated according to the distance between the waypoints. Third, the UAV Simulink model has been established, and the spatial position of the optimized trajectory is tracked. The experimental results prove that the skeleton extraction can significantly speed up the search process, and greedy algorithm can shorten the path length effectively. The minimum snap combined with the time allocation strategy can produce a smooth and feasible path. The UAV Simulink model also proves that the classic proportion-differentiation controller can accurately track the generated trajectory. The greedy algorithm and skeleton extraction reduce the average path length of RRT by 9.780 and 8.251%, respectively. The greedy algorithm and skeleton extraction reduce the average path length of RRT, Lazy RRT, and RRT-Connect by about 10–13%. The greedy algorithm and skeleton extraction reduce the time consumption of RRT by 62.781 and 36.276%, respectively.
Efficient seismic image super-resolution

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyEfficient seismic image super-resolutionAuthors: Adnan HamidaMotaz AlfarrajAbdullatif A. Al-ShuhailSalam A. ZummoAdnan HamidaKing Fahd University of Petroleum and MineralsSearch for more papers by this author, Motaz AlfarrajKing Fahd University of Petroleum and Minerals SDAIA-KFUPM Joint Research Center for Artificial IntelligenceSearch for more papers by this author, Abdullatif A. Al-ShuhailKing Fahd University of Petroleum and MineralsSearch for more papers by this author, and Salam A. ZummoKing Fahd University of Petroleum and Minerals and Center for Communications and SensingSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3746892.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSeismic field data suffer from Low Resolution (LR) image quality. This is due to the limited number of sensors used in a given seismic survey, as well as physical and technological limitations of the sensors. To obtain high-resolution (HR) images, either higher density acquisitions are carried out, or advanced sensors are used in the field. Both of which introduce financial, computational, and memory costs. Moreover, to improve the resolution of already acquired seismic field data, another survey would be needed, and this is a time-consuming high-cost approach. We propose the application of state-of- the-art Deep Learning based Convolutional Neural Network (CNN) models that are built for image super-resolution (SR) to increase the resolution of seismic images. Since seismic images interpretation benefits from image analysis algorithms that utilize Human Visual System (HVS) characteristics, objective as well as subjective image quality assessment will be carried out to evaluate the SR model used for seismic images.Keywords: image super-resolution, deep learning, seismic images, convolutional neural networks, pixel attentionPermalink: https://doi.org/10.1190/image2022-3746892.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Adnan Hamida, Motaz Alfarraj, Abdullatif A. Al-Shuhail, and Salam A. Zummo, (2022), "Efficient seismic image super-resolution," SEG Technical Program Expanded Abstracts : 1659-1663. https://doi.org/10.1190/image2022-3746892.1 Plain-Language Summary Keywordsimage super-resolutiondeep learningseismic imagesconvolutional neural networkspixel attentionPDF DownloadLoading ...
AR4FSM: Mobile Augmented Reality Application in Engineering Education for Finite-State Machine Understanding

Students in the twenty-first century are accustomed to using technology in all aspects of their lives and have never known a world without it; the classroom is no exception. Augmented reality (AR) is a technology that bridges the virtual and physical worlds to make learning more engaging and enjoyable. In this paper, we present a mobile application aimed at novice learners that makes use of technology for the teaching and learning of computer system engineering concepts. Currently, students typically learn about finite-state machine (FSM) concepts from lectures, tutorials, and practical hands-on experience combined with commercial timing simulation tools. We aimed to enhance these traditional, lecture-based instruction and information delivery methods. We developed an AR-based FSM visualization tool called AR4FSM to help students more easily grasp concepts through immersion and natural interaction with an FSM. We used a blend of multimedia information, such as text, images, sound, and animations superimposed on real-world-state machine diagrams, presenting the information in an interactive and compelling way. An experiment with 60 students showed that the app was perceived positively by the students and helped to deliver FSM-related concepts in a way that was easier to understand than traditional, lecture-based teaching methods. This instruction methodology not only engaged the students but also motivated them to learn the material. The findings of this study have inspired us to use this application to teach FSM topics in the classroom.
Study on Integration of FastAPI and Machine Learning for Continuous Authentication of Behavioral Biometrics

The traditional practices of security are failing slowly; new systems are needed to protect the information in the cyber world. The user authentication should be such that the systems are continuously learning and improving, and development should be fast paced without consuming too much time. The currently used continuous authentication systems have significant weaknesses in the huge data handling mechanisms including the run-time overhead taken in analyzing the user profiles. The objective of this work is to overcome these weaknesses to be able to handle multiple requests simultaneously, improve the overall performance, and decrease the cost of the behavioral biometrics-based authentication systems. In other words, we aim to create a machine learning algorithm to create user-profiles that are capable to user’s behavioral data of 64 bytes per second. The algorithm would provide over millions of user-profile recognitions per day through predictive techniques. To reach this target, we integrate the biometric behavior detection machine learning (ML) model, that doesn’t natively run on the web, with frontend using FastAPI services. These services enable the users to access the model detection using the web browser for continuous authentication using behavioral biometrics. The evaluation and the experimental results showed that the performance of the ML model by using the FastAPI has been improved by almost 45% as compared to Flask.
Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

Fraud detection for credit/debit card, loan defaulters and similar types is achievable with the assistance of Machine Learning (ML) algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions.Fraudulent cases are scant in the comparison of non-fraudulent observations, almost in all the datasets.In such cases detecting fraudulent transaction are quite difficult.The most effective way to prevent loan default is to identify non-performing loans as soon as possible.Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence.In this paper, the rendering of different machine learning algorithms such as Decision Tree, Random Forest, linear regression, and Gradient Boosting method are compared for detection and prediction of fraud cases using loan fraudulent manifestations.Further model accuracy metric have been performed with confusion matrix and calculation of accuracy, precision, recall and F-1 score along with Receiver Operating Characteristic (ROC )curves.