Reviewerly/exHarmony huggingface dataset:
CFCA-SET: Coarse-to-Fine Context-Aware SAR-to-EO Translation With Auxiliary Learning of SAR-to-NIR Translation

Satellite Synthetic Aperture Radar (SAR) images are immensely valuable because they can be obtained regardless of weather and time conditions. However, SAR images have fatal noise and less contextual information, thus making it harder and less interpretable. So, translation of SAR to Electro-Optical (EO) images is highly required for easier interpretation. In this paper, we propose a novel coarse-to-fine context-aware SAR-to-EO image translation (CFCA-SET) framework and a misalignment-resistant loss for the misaligned pairs of SAR-EO images. With our auxiliary learning of SAR-to-Near-Infrared translation, CFCA-SET consists of a two-stage training: (i) the low-resolution SAR-to-EO translation is learned in the coarse stage via a local self-attention module that helps diminish the SAR noise, and (ii) the resulting output is used as guidance in the fine stage to generate the SAR colorization of high resolution. Our proposed auxiliary learning of SAR-to-NIR translation can successfully lead CFCA-SET to learn distinguishable characteristics of various SAR objects with less confusion in a context-aware manner. To handle the inevitable misalignment problem between SAR and EO images, we newly design a misalignment-resistant loss function. Extensive experimental results show that our CFCA-SET can generate more recognizable and understandable EO-like images compared to other methods in terms of nine image quality metrics. Our CFCA-SET surpasses the state-of-the-art methods for two (QXS and CASET) datasets with the improvements: PSNR (3.6%, 29%), ERGAS (7.4%, 30%), SSIM (15%, 15%), SAM (21%, 38%), Ds (16%, 13%), QNR (1.5%, 3.1%), CHD (18%, 12%), LPIPS (4.2%, 8%), and FID (9.0%, 33%).
Vulnerability in Free Space QKD Due to Detection Coupling Mismatch

Practical implementations of QKD protocols involve devices which are not perfect, and an eavesdropper may exploit this to gain information leading to attacks. Here we have considered the effects of coupling mismatch between the detectors. We find possible information leakage to Eve due to coupling mismatch at the receiver’s detectors in terms of mutual information between the eavesdropper and receiver. The experiment has been performed for the Gaussian and Laguerre-Gaussian modes of the signal. This aspect becomes essential while implementing free space QKD using a satellite. The results suggest that accounting for detection coupling mismatch is crucial to avoid side-channel attacks.
Thought Processes as Data in Eye Care Education and Research

Working memory is a component of the human memory system in which all thought processes take place during an activity. Asking participants to verbalize their thoughts (think-aloud method) is a well-established technique in cognitive psychology and computer science for obtaining access to mental processes in order to investigate in-performance cognitions. Few attempts have been made to use such data in eye care research. This article provides a concise overview of the think-aloud technique and discusses potential research areas where this method can produce useful data.
A study of innovative strategies for teaching English to college students based on tiered probit

Abstract This paper analyzes the current situation of university English teaching and constructs a trinity of EGP+ESP+EIC multiple teaching innovation systems for university English based on the principles of English teaching innovation. In order to analyze this innovative teaching system of English, a stratified statistical model and a spatial autoregressive model are combined, and four ordinal stratified spatial autoregressive Probit models are innovatively constructed to model data where the dependent variable is ordinal, and there is a spatial dependency situation with a stratified structure. The analysis shows that in the process of continuous innovation of English teaching mode, the number of people who are dissatisfied with English teaching decreases by 30%, the number of people who are very satisfied increases by 35%, and the number of people who are very satisfied accounts for 42.5% of the total number of people and 90% of the total number of people who are satisfied in the final stage of innovation of English teaching in university. The English teaching innovation strategy for university students based on this study is feasible.
A Self-Adaptation Mechanism for Variability Management in Dynamic Software Product Lines

Variability management is crucial for companies that need to offer multiple product variants quickly. However, the increasing complexity of software-intensive systems has made variability management increasingly challenging. This challenge is compounded by the need for such systems to run continuously and adapt to changes in the environment and user needs at runtime. To address this challenge, Dynamic Software Product Line (DSPL) Engineering has emerged as a strategy for managing variability in complex and dynamic environments. The key challenge in DSPL engineering is to manage product configurations at runtime by detecting changes in the context and adapting accordingly. In this paper, we propose an adaptation mechanism for DSPL feature models that supports dynamic variability and is based on the MAPE-K model. The mechanism transforms feature model constraints into rules that enable the activation of each feature and annotates contexts in the corresponding features to be activated when changes occur. We have implemented the mechanism in the DyMMer 2.0 modeling tool and evaluated its performance using various DSPL feature models. Additionally, we performed a preliminary evaluation with a proof-of-concept study with an expert to assess its practical usage. Our results demonstrate the effectiveness and practicality of the proposed mechanism in managing variability in complex and dynamic environments.
An Experiment on How Feature Dependent Variables Affect Configurable System Comprehensibility

Background: #ifdefs allow developers to define source code related to features that should or should not be compiled. A feature dependency occurs in a configurable system when source code snippets of different features share code elements, such as variables. Variables that produce feature dependency are called dependent variables. The dependency between two features may include just one dependent variable or more than one. It is reasonable to suspect that a high number of dependent variables and their use make the analysis of variability scenarios more complex. In fact, previous studies show that #ifdefs may affect comprehensibility, especially when their use implies feature dependency. Aims: In this sense, the main goal of our experiment is to evaluate if the comprehensibility of configurable system source code varies according to the number of dependent variables. Testing this hypothesis is important so that we can recommend practitioners and researchers the extent to which writing #ifdef code with dependencies is harmful. Method: We executed a controlled experiment with 12 participants who analyzed programs trying to specify their output. We quantified comprehensibility using metrics based on time and attempts to answer tasks correctly, participants’ visual effort, and participants’ heart rate. Results: Our results indicate that the higher the number of dependent variables the more difficult it was to understand programs with feature dependency. Conclusions: In practice, our results indicate that comprehensibility is more negatively affected in programs with higher number of dependent variables and when these variables are defined at a point far from the points where they are used.
Smart energy-load matching strategy for distribution networks based on deep learning and K-means clustering algorithm

The traditional energy-saving and load matching strategies for distribution networks have the problem of low accuracy in predicting the capacity of power equipment. Therefore, a new intelligent energy load matching strategy is proposed, which uses deep learning algorithms and K-means clustering algorithms to process and standardize power data, extract data features, and construct a capacity prediction model for energy storage devices in distribution networks. By finetuning the model structure network, the load condition of the distribution device is predicted, and the dynamic matching of source and load is achieved. Experimental verification shows that the matching effect of this strategy is superior to traditional methods, with a significant reduction in unit output and a high source load matching rate. This method has good application prospects in improving the energy utilization efficiency and reliability of distribution networks.
What Factors Affect the Build Failures Correction Time? A Multi-Project Study

Continuous Integration (CI) is a widely adopted practice in modern software engineering that involves integrating developers’ local changes with the project baseline daily. Despite its popularity, recent studies have revealed that integrating changes can be time-consuming, requiring significant effort to correct errors that arise. This can lead to development activities being paused, including the addition of new features and fixing bugs, while developers focus on analyzing and correcting build failures. In this study, we investigate the factors that influence the time taken to correct build failures in CI. Specifically, we analyze the impact of developer activity, project characteristics, and build complexity on build failure correction time. To conduct our analysis, we collected data from 18 industrial projects of a software company, calculating 13 metrics for each project based on the literature on build failures analysis. We used association rules, a data mining technique, to examine the relationship between the defined factors and build failure correction time. Our findings reveal significant correlations between the factors studied and the duration of build failure correction time. Specifically, we found that more experienced developers require less time to correct build failures, while build failures that originate in the early stages of the project are resolved more quickly. Additionally, we observed that build failures with more lines and modified files tend to have longer correction times. Overall, this study sheds light on the factors that impact build failure correction time in CI. By identifying these factors, our findings can help software development teams optimize their CI processes and minimize the impact of build failures on development activities.
Search Mechanism for Encapsulate Classes with Factory Refactoring Opportunities

Software systems must evolve to avoid its degradation. The refactoring technique associated with the application of design patterns help software consistent evolution. A refactoring opportunity consists of a source code that can be altered by refactoring to improve its quality. In this sense, this study presents a mechanic to search refactoring opportunities to Encapsulate Classes with Factory refactoring pattern. Some clues and refactoring steps which aid to solve the indicated problems are described. We conducted a case study with a set of open-source projects to evaluate the opportunities identified by the mechanic.
Feature selection in an interactive search-based PLA design approach

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an interactive optimization problem with many conflicting factors. Incorporating Decision Makers’ (DM) preferences during the search process may help the algorithms find more adequate solutions for their profiles. Interactive approaches allow the DM to evaluate solutions, guiding the optimization according to their preferences. However, this brings up human fatigue problems caused by the excessive amount of interactions and solutions to evaluate. A common strategy to prevent this problem is limiting the number of interactions and solutions evaluated by the DM. Machine Learning (ML) models were also used to learn how to evaluate solutions according to the DM profile and replace them after some interactions. Feature selection performs an essential task as non-relevant and/or redundant features used to train the ML model can reduce the accuracy and comprehensibility of the hypotheses induced by ML algorithms. This work aims to select features of an ML model used to prevent human fatigue in an interactive search-based PLA design approach. We applied four selectors and through results we were able to reduce 30% of features, obtaining an accuracy of 99%.
Construction of distribution transformer operation reliability prediction model based on big data

Distribution transformer is an important equipment in distribution network. The running state of distribution transformer directly affects the quality, safety and stability of power supply. In order to improve the prediction speed of distribution transformer operation reliability and ensure the stable operation of substation equipment, a distribution transformer operation reliability prediction model based on big data technology is constructed. Calculate the reliability characteristic quantity of transformer according to the predicted demand. Setting logistics forecasting nodes based on power dispatching data results. Combined with big data technology, the orthogonal prediction matrix is constructed, and the prediction model is constructed by using big data redundancy processing method. The test results show that the response time is less than 1.3s, the prediction accuracy is 96.37%, and the model is stable. It is proved that the proposed method can effectively improve the reliability prediction effect of transformer and has practical application value.
Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation

This study proposes a novel hybrid computational approach that integrates the artificial dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal representation of the Exact Boolean k Satisfiability (EB k SAT) logical rule. The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB k SAT logic representation. To assess the performance of the proposed hybrid computational model, a specific Exact Boolean k Satisfiability problem is constructed, and simulated data sets are generated. The evaluation metrics employed include the global minimum ratio (GmR), root mean square error (RMSE), mean absolute percentage error (MAPE), and network computational time (CT) for EB k SAT representation. Comparative analyses are conducted between the results obtained from the proposed model and existing models in the literature. The findings demonstrate that the proposed hybrid model, ADA-HNN-EB k SAT, surpasses existing models in terms of accuracy and computational time. This suggests that the ADA algorithm exhibits effective compatibility with the HNN for achieving an optimal representation of the EB k SAT logical rule. These outcomes carry significant implications for addressing intricate optimization problems across diverse domains, including computer science, engineering, and business.
Capturing the Customer Experience Culture of High-End Motorcycle Helmets Through the Cyberspace

The demand for premium full-face helmets is growing. To win the market, the pace of product development has to match the rapid change of customers’ culture. As technologies are reshaping the ways we work, studying customer experience is no longer restricted to the real world. To this end, this research demonstrates the collection of data in and through the cyberspace to gain insight on customer experience culture: (1) helmet online reviews; (2) subject matter expert interviews through the web. We performed two consecutive studies: (1) analyzing 15-month online reviews to identify critical customer values and (2) conducting online semi-structured interviews with two domain experts to obtain a comprehensive understanding of the identified customer values. Results showed that collecting data in and through the cyberspace was effective in identifying customer experience culture. Customers of premium full-face helmets cared more about the comfort, the noise deduction, and the usability issues when wearing helmets. The outcomes of the study provide insight on conducting remote user experience studies via the cyberspace.
Single Image Rain Removal Algorithm Based on U-Net and Vision Transformer

Abstract Rain will significantly lower image quality, which will undoubtedly have an impact on how well outdoor computer vision systems operate, such as autonomous driving. This paper proposes a two-branch deep neural network consisting of an attention guided U-Net and Vision Transformer, which could capture intra-field details and cross-patch relationships to obtain good global and local deraining effects. In order to ensure both branches pose positive effects on the target derain task, we specifically design a patch boot image module to achieve complemented feature fusion, which adaptively selects informative intra-field regions guided by the patch-wise importance map. Moreover, Wasserstein distance between the prediction and reference image is applied as the objective function to pursue better image quality measurement. In comparison to existing algorithms, the suggested method may more effectively remove rain and restore the background details, according to qualitative and quantitative results on the public datasets Rain200L and Rain200H. On the aforementioned datasets, the peaks of the structural similarity (SSIM) and signal-to-noise ratio (PSNR) were 32.55/0.9476 and 26.12/0.8826, respectively.
Merging Augmented-Reality Interface with Smart Building Controls: Considerations for the Building User Interface

This paper looks at how emerging interactive extended reality (XR) technologies are being integrated with smart systems and the IoT in the context of the built environment. Specifically, it investigates the ways to extend this interoperability between AR and IoT into areas of construction, building use, facility management, and post-occupancy evaluation. The synergy between augmented reality (AR) and the Internet of Things (IoT) technologies has a potential to go beyond mere visualization of captured data and provide active controls of building services in a user-specific context/perspective. These controls together with building environmental data overlays can serve as a building user interface connecting virtual and physical assets as well as users and building construction professionals.
Extended object tracking when irregular star convex mutations

Abstract In this paper, for extended object tracking in the case of mutation of irregular stars convex shape, using Random Hypersurface Model (RHM ) to model the object, then the target shape is expressed as parameter by radial function. Considering that using circle as a prior shape information requires a long filter consumption time, the RHM-CC-IOU-UKF algorithm proposed in this paper uses the center contour method to correct the prior shape information of the object, and then uses the Intersection over Union (IOU) method to improve the object tracking accuracy in the mutation case. The object estimation shape is updated when combined with a simple filtering algorithm. Eventually, the effectiveness of this algorithm is demonstrated by simulation experiments in two scenarios.
A Framework for False Image Detection with Sample-Oriented Intelligent Adversarial

Abstract To develop algorithms capable of automatically detecting and evaluating the authenticity of images and videos, researchers have focused on false image detection algorithms. These algorithms aim to identify the authenticity of images by distinguishing between real images and forged ones generated using false generation algorithms. In this paper, the main focus is on implementing a single-frame authenticated image detector using the concept of migration learning. The detector utilizes Inception ResNet v2, a target classification network pre-trained on a self-built military scene dataset. To enhance the dataset, a series of graphical enhancement algorithms are employed, enabling the classification network to learn the crucial differences between real and forged images. Additionally, Focal Loss is introduced to balance the dataset for various GAN-based image generation algorithms. As a result, the final forged target image detector achieves an impressive classification accuracy of 0.8908 on a large-scale sample test set.
Spatial Behavior Data in Virtual Conference: Proximity and Network Analysis

Recent advances in virtual environments have extended real-life activities to virtual spaces, thereby generating enormous amounts of data on spatial behaviors. Spatial behaviors in physical spaces have been extensively studied; however, in virtual spaces, it is limited to measuring proxemic distances or visualizing spatial diffusion. Therefore, to understand users’ spatial behaviors in virtual spaces, a novel network approach that connects users and spaces is proposed in this study. Using a proxemics dataset from a virtual conference, positional connectivity between users and spatial units was measured using Euclidean and temporal measurements. A bipartite network and projections were used to extract Close-Proximity Interactions. The proposed method quantified user activities using topological features. In addition, log analysis revealed observations of spatial interactions in virtual spaces without a dedicated tool, such as an eye tracker. The results of this study can improve the understanding of remote-learning activities in virtual spaces. Its application can help education managers, designers, and marketers to assess user interactions based on specific contexts in virtual spaces.
C-SwinIR: Dental CT super-resolution reconstruction fused with SwinIR

Abstract Super-resolution reconstruction (SR) of dental computed tomography (Dental CT) images is a innovative and challenging task. To address the limitations of Dental CT in obtaining high-resolution (HR) images due to equipment constraints and noise interference, we propose a Dental CT SR method called C-SwinIR based on SwinIR. Firstly, the self-calibrated convolutions network (SCNet) is introduced to solve the problem of detail loss in shallow feature graphs and improve the ability to recover details. Subsequently, the cross-shaped windows (CSWin) self-attention transformer structure is used to replace the original transformer structure, which improves the ability of the model to obtain context information. Eventually, the integration of efficient channel attention (ECA-Net) module effectively realizes the local cross-channel interaction, accelerates the model convergence and solves the problem of gradient explosion in training. Experimental results show that our proposed method is superior to the original SwinIR network by achieving a higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) by 0.287 and 0.003 respectively. Additionally, it decreases mean squared error (MSE) by 1.108. By using this method, clinicians can obtain clearer details and textures from Dental CT, which effectively assist in diagnosis.