Reviewerly/exHarmony huggingface dataset:
Variability Management in Self-Adaptive Systems through Deep Learning: A Dynamic Software Product Line Approach

Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mapping, we build on the identified gaps in the variability management of self-adaptive systems; we propose a framework that improves the adaptive capability of self-adaptive systems through feature model generation, variation point generation, the selection of a variation point, and runtime variability management using deep learning and the monitor–analysis–plan–execute–knowledge (MAPE-K) control loop. We compute the permutation of domain features and obtain all the possible variation points that a feature model can possess. After identifying variation points, we obtain an adaptation rule for each variation point of the corresponding product line through a two-stage training of an artificial neural network. To evaluate our proposal, we developed a test case in the context of an air quality-based activity recommender system, in which we generated 11 features and 32 possible variations. The results obtained with the proof of concept show that it is possible to manage identifying new variation points at runtime using deep learning. Future research will employ generating and building variation points using artificial intelligence techniques.
Marginal effects for non-linear prediction functions

Abstract Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models such as generalized linear models, the estimated coefficients cannot be interpreted as a direct feature effect on the predicted outcome. Hence, marginal effects are typically used as approximations for feature effects, either as derivatives of the prediction function or forward differences in prediction due to changes in feature values. While marginal effects are commonly used in many scientific fields, they have not yet been adopted as a general model-agnostic interpretation method for machine learning models. This may stem from the ambiguity surrounding marginal effects and their inability to deal with the non-linearities found in black box models. We introduce a unified definition of forward marginal effects (FMEs) that includes univariate and multivariate, as well as continuous, categorical, and mixed-type features. To account for the non-linearity of prediction functions, we introduce a non-linearity measure for FMEs. Furthermore, we argue against summarizing feature effects of a non-linear prediction function in a single metric such as the average marginal effect. Instead, we propose to average homogeneous FMEs within population subgroups, which serve as conditional feature effect estimates.
Nonlinear erasing of propagating spin-wave pulses in thin-film Ga:YIG

Nonlinear phenomena are crucial for magnon-based information processing, but the nonlinear interaction between two spin-wave signals requires their spatiotemporal overlap, which can be challenging for directional processing devices. Our study focuses on a gallium-substituted yttrium iron garnet film, which exhibits an exchange-dominated dispersion relation and, thus, provides a particularly broad range of group velocities compared to pure YIG. Using time- and space-resolved Brillouin light scattering spectroscopy, we demonstrate the excitation of time-separated spin-wave pulses at different frequencies from the same source, where the delayed pulse catches up with the previously excited pulse and outruns it due to its higher group velocity. By varying the excitation power of the faster pulse, the outcome can be finely tuned from a linear superposition to a nonlinear interaction of both pulses, resulting in a full attenuation of the slower pulse. Therefore, our findings demonstrate the all-magnonic erasing process of a propagating magnonic signal, which enables the realization of complex temporal logic operations with potential application, e.g., in inhibitory neuromorphic functionalities.
Detecting SQL injection attacks by binary gray wolf optimizer and machine learning algorithms

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

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

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

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.
Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration.
Predictive Factors for Difficulty in Simulation Methodology in Teacher Education

Background Simulation methodology in teacher education offers a wide range of practice opportunities in a controlled environment. However, even though not much has been written about the benefits of simulation in teacher training, even less has been said about the difficulties perceived by the participants in doing simulation. Method In this study we conducted an exploratory longitudinal study which includes 205 postgraduate participants scattered around the globe who took part in a computer mediated simulation in the years 2019 to 2022 as an assessment tool in a master’s degree for teacher education. Aside from the positive impact on learning, which has already been disseminated (autor1), some recurring difficulties have been identified and presented in this study. A mixed analysis is performed based on the triangulation between qualitative and quantitative approaches. The qualitative data were analysed using a qualitative content analysis method through an open question about participants’ perceptions post-treatment, video recordings and individual final reports. The quantitative data was gathered through a Likert-type questionnaire and analysed using statistical methods. Findings Findings indicate that (1) simulation entails several concerns about task overload and time consumption along with lack of understanding of what the simulation phases involve (learning focus vs performance focus); and (2) simulation can cause uneasiness and anxiety related to the use of technological devices and teamwork. Contributions Thus, these results have several implications for research, theory and practice when it comes to applying simulation as a pedagogical strategy.
Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

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.
THE IMPROVEMENT OF THE ASSESSMENT OF DIGITAL TRANSFORMATIONS IN SOCIOECONOMIC SYSTEMS USING AI

This article aims to improve the methods of assessing digital transformations in socioeconomic systems. The relevance of the research topic is driven by the rapid development of digital technologies and their impact on the economy and society, which contributes to sustainable development. Research methods include analysing statistical data, expert methods, and modelling the dynamics of digital transformations. The results show that throughout the study period of 2017-2021, six out of eight dynamic indicators reflect that digital transformations in socioeconomic systems meet installed criteria besides the involvement of government institutions in providing access to electronic democracy tools and the percentage of the adult population subscribed to mobile phones. Six out of eight dynamic indicators show a trend, suggesting an increase in these specific indicators over time. Additionally, the consolidated dynamic indicator of digital transformations in socioeconomic systems meets the criteria, pointing towards an overall improvement over time for indicators of digital transformations in socioeconomic systems. It is important to note that this does not necessarily imply a simultaneous improvement in all individual indicators. Consequently, enhancing the indicators experiencing deterioration over time is crucial to improve the overall situation. This emphasises the need to make managerial decisions regarding the correction of digital transformations in socioeconomic systems in those areas, the indicators of which decreased during 2017-2021. Identifying key aspects of these problems allows for defining directions to improve the digital transformations. The practical value of the study lies in the potential use of refined scientific and methodological approaches to obtaining information necessary for making managerial decisions to promote digital transformations.
Ranking‐based architecture generation for surrogate‐assisted neural architecture search

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

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

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

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

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

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.