Reviewerly/exHarmony huggingface dataset:
Detecting SQL injection attacks by binary gray wolf optimizer and machine learning algorithms

Authors:
raw_author_name: Bahman Arasteh
raw_affiliation_string: Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey
raw_author_name: Keyvan Arasteh
raw_affiliation_string: Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey
raw_author_name: Farzad Kiani
raw_affiliation_string: Data Science Application and Research Center (VEBIM), Fatih Sultan Mehmet Vakif University, Istanbul, Turkey
raw_author_name: Mahsa Torkamanian-Afshar
raw_affiliation_string: Computer Engineering Department, Faculty of Engineering, Istanbul Topkapi University, 34087, Istanbul, Turkey

Abstract: Abstract SQL injection is one of the important security issues in web applications because it allows an attacker to interact with the application's database. SQL injection attacks can be detected using machine learning algorithms. The effective features should be employed in the training stage to develop an optimal classifier with optimal accuracy. Identifying the most effective features is an NP-complete combinatorial optimization problem. Feature selection is the process of selecting the training dataset's smallest and most effective features. The main objective of this study is to enhance the accuracy, precision, and sensitivity of the SQLi detection method. In this study, an effective method to detect SQL injection attacks has been proposed. In the first stage, a specific training dataset consisting of 13 features was prepared. In the second stage, two different binary versions of the Gray-Wolf algorithm were developed to select the most effective features of the dataset. The created optimal datasets were used by different machine learning algorithms. Creating a new SQLi training dataset with 13 numeric features, developing two different binary versions of the gray wolf optimizer to optimally select the features of the dataset, and creating an effective and efficient classifier to detect SQLi attacks are the main contributions of this study. The results of the conducted tests indicate that the proposed SQL injection detector obtain 99.68% accuracy, 99.40% precision, and 98.72% sensitivity. The proposed method increases the efficiency of attack detection methods by selecting 20% of the most effective features.
A novel image multitasking enhancement model for underwater crack detection

Authors:
raw_author_name: Junjie Li
raw_affiliation_string: College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, Jiangsu, China; Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, Liaoning, China

Abstract: Remotely Operated Vehicles (ROVs) carrying vision systems provide an efficient solution for the underwater crack search. However, the degradation of underwater images severely limits the prognosis of cracks. For the problem of ROV image multiple degradation in complex underwater environments, a robust and accurate multitask enhancement method for underwater crack images is proposed, which can simultaneously enhance the color, brightness. and deblurring of images. In the model, we propose a depth-residual encoder–decoder and feature calibration module to address low-level feature loss. Meanwhile, we propose a simulation method to construct paired training data. Experiments show that our model outperforms existing methods in image enhancement and provides significant enhancements for downstream tasks. The model has been successfully applied to practical engineering and shows good adaptability, which can well assist ROVs for underwater crack detection. In future work, we will continue to improve the robustness of the ROV crack detection system under more complex noise scenarios.
Quantum random number generator based on LED

Authors:
raw_author_name: Hamid Reza Naeij
raw_affiliation_string: Department of Chemistry, Sharif University of Technology 4 , Tehran, Iran

Abstract: Quantum random number generators (QRNGs) produce random numbers based on the intrinsic probabilistic nature of quantum mechanics, making them true random number generators. In this paper, we design and fabricate an embedded QRNG that produces random numbers based on fluctuations of spontaneous emission and absorption in a light-emitting diode. To achieve a robust and reliable QRNG, we compare some usual post-processing methods and select the finite impulse response method for a real-time device. This device could pass the National Institute of Standards and Technology tests, the generation rate is 1 Mbit/s, and the randomness of the output data is invariant in time.
Synthetic Data for Sentinel-2 Semantic Segmentation

Authors:
raw_author_name: Étienne Clabaut
raw_affiliation_string: Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
raw_author_name: Samuel Foucher
raw_affiliation_string: Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
raw_author_name: Yacine Bouroubi
raw_affiliation_string: Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
raw_author_name: Mickaël Germain
raw_affiliation_string: Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada

Abstract: Satellite observations provide critical data for a myriad of applications, but automated information extraction from such vast datasets remains challenging. While artificial intelligence (AI), particularly deep learning methods, offers promising solutions for land cover classification, it often requires massive amounts of accurate, error-free annotations. This paper introduces a novel approach to generate a segmentation task dataset with minimal human intervention, thus significantly reducing annotation time and potential human errors. ‘Samples’ extracted from actual imagery were utilized to construct synthetic composite images, representing 10 segmentation classes. A DeepResUNet was solely trained on this synthesized dataset, eliminating the need for further fine-tuning. Preliminary findings demonstrate impressive generalization abilities on real data across various regions of Quebec. We endeavored to conduct a quantitative assessment without reliance on manually annotated data, and the results appear to be comparable, if not superior, to models trained on genuine datasets.
Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Authors:
raw_author_name: Lei Zhang
raw_affiliation_string: College of Information Science and Engineering, China University of Petroleum (Beijing), Beijing 102249, China

Abstract: Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller.
Ranking‐based architecture generation for surrogate‐assisted neural architecture search

Authors:
raw_author_name: Songyi Xiao
raw_affiliation_string: School of Automation Guangdong University of Technology Guangzhou China

Abstract: Abstract Architectures generation optimization has been received a lot of attention in neural architecture search (NAS) since its efficiency in generating architecture. By learning the architecture representation through unsupervised learning and constructing a latent space, the prediction process of predictors is simplified, leading to improved efficiency in architecture search. However, searching for architectures with top performance in complex and large NAS search spaces remains challenging. In this paper, an approach that combined a ranker and generative model is proposed to address this challenge through regularizing the latent space and identifying architectures with top rankings. We introduce the ranking error to gradually regulate the training of the generative model, making it easier to identify architecture representations in the latent space. Additionally, a surrogate‐assisted evolutionary search method that utilized neural network Bayesian optimization is proposed to efficiently explore promising architectures in the latent space. We demonstrate the benefits of our approach in optimizing architectures with top rankings, and our method outperforms state‐of‐the‐art techniques on various NAS benchmarks. The code is available at https://github.com/outofstyle/RAGS‐NAS .
Dynamic ensemble of regression neural networks based on predictive uncertainty

Authors:
raw_author_name: Yoonhyung Lee
raw_affiliation_string: Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
raw_author_name: Seokho Kang
raw_affiliation_string: Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea

Abstract: In a data-decentralized environment where data are distributed across individual local nodes, inter-node data sharing is often restricted due to security concerns and communication costs. Under these restrictions, using entire data to train a global predictive model is challenging. A viable strategy is to leverage local predictive models trained independently on local nodes. However, this adversely affect generalization performance, particularly when the data distribution varies between local nodes. To address this issue in regression problems, we propose a dynamic ensemble method that combines the predictions of locally trained neural networks without the need to access data from local nodes. For a query instance, the prediction and its uncertainty from each neural network are computed by applying Monte Carlo dropout. Subsequently, smaller/larger weights are assigned to neural networks with higher/lower predictive uncertainty. The final prediction result is derived using the weighted average of the individual predictions. The proposed method is applicable to a data-decentralized environment where data are inaccessible and thus cannot be shared across local nodes. Transmitting query instances to local nodes and receiving predictions and weights from the local nodes are the only requirements, thereby ensuring low communication costs. Experimental results on simulated data-decentralized environments using benchmark regression datasets validated the superior generalization performance of the proposed method compared with baseline methods. The proposed method was found to be more effective for regression neural networks that demonstrate higher performance at their respective local nodes.
The spheroidal trackball: generalising the fixed trackball for virtual camera navigation

Authors:
raw_author_name: Daniel González-Toledo
raw_affiliation_string: Telecommunication Research Institute (TELMA), Universidad de Málaga, Málaga, Spain
raw_author_name: María Cuevas-Rodríguez
raw_affiliation_string: Telecommunication Research Institute (TELMA), Universidad de Málaga, Málaga, Spain
raw_author_name: Luis Molina-Tanco
raw_affiliation_string: Telecommunication Research Institute (TELMA), Universidad de Málaga, Málaga, Spain
raw_author_name: Arcadio Reyes-Lecuona
raw_affiliation_string: Telecommunication Research Institute (TELMA), Universidad de Málaga, Málaga, Spain

Abstract: Abstract Virtual trackball techniques have become a standard in 3D applications, particularly for interfaces with limited degrees of freedom such as touchscreens or mice. The fact that we are used to them does not mean that they cannot be improved upon. Recent research has highlighted the significance of considering users’ mental models of a preferred rotation axis, as it can improve performance, perceived usability and perceived workload. Building upon these findings, this paper introduces the spheroidal trackball framework—a novel method for orbiting the virtual camera around elongated objects. The paper presents the mathematical formulation and the evaluation of the technique. The formulation offers enough information to implement the approach. The evaluation shows the advantages of this approach over the fixed spherical trackball for this class of objects, in terms of task performance, usability and perceived workload. This research constitutes an advancement in the refinement of 3D user interaction techniques, opening new avenues of innovation in this still evolving field.
An efficient secure predictive demand forecasting system using Ethereum virtual machine

Authors:
raw_author_name: Himani Saraswat
raw_affiliation_string: Department of Computer Science and Engineering Graphic Era Hill University Dehradun India
raw_author_name: Mahesh Manchanda
raw_affiliation_string: Department of Computer Science and Engineering Graphic Era Hill University Dehradun India
raw_author_name: Sanjay Jasola
raw_affiliation_string: Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

Abstract: Abstract Predictive demand forecasting plays a pivotal role in optimizing supply chain management, enabling businesses to effectively allocate resources and minimize operational inefficiencies. This paper introduces a novel approach to enhancing demand forecasting processes by leveraging the Ethereum virtual machine within a blockchain framework. The proposed system capitalizes on the inherent security, transparency, and decentralized nature of blockchain technology to create a secure and efficient platform for predictive demand forecasting. The system leverages the Ethereum virtual machine to establish a secure, decentralized, and tamper‐resistant platform for demand prediction while ensuring data integrity and privacy. By utilizing the capabilities of smart contracts and decentralized applications within the Ethereum ecosystem, the proposed system offers an efficient and transparent solution for demand forecasting challenges. The current research focused on Ethereum virtual machine characteristics, features, components, and implementation details. A secured framework for the prediction of demand forecasting systems is proposed. Finally, the authors tried to validate and optimize the gas cost by using distinguished statistics and analysis.
Identifying exploitable memory objects for out‐of‐bound write vulnerabilities

Authors:
raw_author_name: Runhao Li
raw_affiliation_string: National University of Defense Technology Changsha China
raw_author_name: Bin Zhang
raw_affiliation_string: National University of Defense Technology Changsha China
raw_author_name: Chaojing Tang
raw_affiliation_string: National University of Defense Technology Changsha China

Abstract: Abstract Exploiting an out‐of‐bounds write vulnerability in general‐purpose applications has become a current research focus. Given the large scale of code in programs, selecting appropriate memory objects for exploitation is challenging. This letter proposes a corrupted data propagation‐guided fuzzing method. By tracking the propagation process of corrupted data among memory objects, a multi‐level fuzzing schedule is proposed to search the execution paths. Experimental results show that this proposed method, EMOFuzz, can effectively identify exploitable objects under various overflow lengths, significantly enhancing the efficiency of exploitability analysis.
Recent advances in blockchain technology: prospects, applications and constraints in the minerals industry

Authors:
raw_author_name: John A. Adebisi
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Namibia, Ongwediva, Namibia

Abstract: One of the most important sectors of the world economy is the mining and metals sector. Although, many operational and commercial procedures continue to be ineffective and out-of-date, which results in crucial data omissions, security flaws, and sometimes corruption. Given that the mining industry wants to increase the emphasis on ethical and open mining practices, the industry has been looking for ways to incorporate these practices. It is envisioned that such practices will contribute to the modernisation of supply chains in addition to helping to reduce risks related to sustainability and reputation. The application of blockchain technology in the minerals industry capable of tracking natural resources has been discussed in this study, giving a much-needed layer of transparency of this technology. However, there are a lot of difficulties and problems that come up when thinking about blockchain technology; if it is to advance as an industry standard, stakeholders will need to evaluate the usefulness and scalability of the technology. While blockchain technology can be applied to a wide range of areas in the minerals industry, this study shines more light on the application of this technology to conflict minerals tracking, mineral resources reporting cheating scandals, rock mechanics designs and monitoring strategy, blasting designs and operation, mine ventilation designs and applications, mining machinery maintenance and management, and mining surveying. The study reveals that blockchain technology is starting to be seriously evaluated and used by a variety of stakeholders, even though broad acceptance has not yet been attained.
Removal of salt and pepper noise using adaptive switching modified decision-based unsymmetric trimmed median filter optimized with Hyb-BCO-FBIA

Authors:
raw_author_name: S. Mohan
raw_affiliation_string: Department of Electronics and Communication Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamilnadu, India
raw_author_name: B. Paulchamy
raw_affiliation_string: Department of Electronics and Communication Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, India

Abstract: An intriguing area in the IP (image processing) is the recovery of noisy photographs from the noise caused by the salt and pepper. As the mistake rate rises and the image format varies, the issue with the current task does not go away. In this study, Salt and Pepper Denoising, Hybrid Balancing Composite motion Optimization with Adaptive Switching Modified Decision based Unsymmetric Trimmed Median Filter and Forensics-Based Investigation Algorithm is proposed (R-SPN-ASMD-UTMF-Hyb-BCO-FBIA). Initially, the input images are obtained from boat-types-recognition dataset, cat-breeds-dataset, cars-image-dataset, butterfly-images 40-species dataset and birds-200 dataset. The images are pre-processed through an ASMD-UTMF filter. ASMD-UTMF does not expose any adoption of optimization systems to calculate the optimal parameters. Therefore, the proposed Hyb-BCO-FBIA is employed for optimizing the ASMD-UTMF weight parameters. The suggested system is implemented on MATLAB and the assessment metrics as Mean Square Error (MSE), Structural similarity index measurement (SSIM), Peak signal to noise ratio (PSNR), Normalized cross-correlation (NC), Image Enhancement Factor (IEF), Mean Square Error (MSE) are analysed. The proposed method attains higher PSNR, NC related with other SOTA (State-Of-The Art) methods.
Mass robotics: How do people communicate with, use, and feel about Alexa? A cross-cultural, user perspective

Authors:
raw_author_name: Autumn Edwards
raw_affiliation_string: School of Communication at Western Michigan University, USA
raw_author_name: Chad Edwards
raw_affiliation_string: School of Communication at Western Michigan University, USA
raw_author_name: Leopoldina Fortunati
raw_affiliation_string: Department of Mathematics, Computer Science and Physics, University of Udine, Italy
raw_author_name: Anna Maria Manganelli
raw_affiliation_string: Department of Philosophy, Sociology, Pedagogy and Applied Psychology, University of Padova, Italy
raw_author_name: Federico de Luca
raw_affiliation_string: Social Statistics and Demography, Economic, Social & Political Sciences, University of Southampton, Southampton, UK

Abstract: For the first time, voice-based assistants (VBAs) allow studying the mass consumption of a robotic product in a ‘natural’ environment. The present paper investigates users' perspectives concerning Alexa, informed by the scholarly literature on the diffusion and appropriation of digital media, VBAs, and social robots, in general, in the domestic sphere. Besides CASA, a paradigm widely applied in robotics, we draw on theories on users and theories on the use of users. We explored individuals' use of Alexa in two countries—the US and Italy— through an online survey. Results indicated that: (1) Alexa's use follows the same modalities of the previous digital media; (2) Alexa's use is in an early phase of domestication, and thus, the role of users in reshaping this technological artifact is still limited; and (3) updates to the CASA paradigm are needed.