Reviewerly/exHarmony huggingface dataset:
Is cybersecurity research missing a trick? Integrating insights from the psychology of habit into research and practice

The idea that people should form positive security habits is gaining increasing attention amongst security practitioners. Habit is a well-studied concept in psychology, but the extent to which the richness of that literature has been fully utilised for security is currently unclear. In order to address this gap, we compared usage of the term "habit"—and connected constructs —in the cybersecurity and habit fields using a co-occurrence networks-based analysis. We aimed to answer three research questions: 1. What is the context within which habit has been discussed in the habit literature and the cybersecurity literature; 2. How does the discussion in these two fields compare; and 3. What are the implications of the outcomes of this analysis for the future research agenda for cybersecurity behaviour? The analysis showed that the habit construct tended to be discussed primarily in the context of other models, rather than on its own. The depth of discussion was therefore limited; resulting gaps in knowledge have important implications for security, like the idea that habits moderate the relationship between intention and behaviour. Given the popularity of the theory of planned behaviour in security research, this represents a key omission. Furthermore, the cybersecurity literature we surveyed contained very little discussion surrounding methods for formation and changing of habits, nor of the role of cues in triggering habitual behaviours. Habits require a different behaviour change approach than intentional behaviours, and many day-to-day security behaviours may in fact be habits. For that reason, these topics represents a potentially productive avenue of research for both security and privacy behaviour.
Monocular Depth Estimation of Old Photos via Collaboration of Monocular and Stereo Networks

Old photos that were captured about a century ago have archaeological and historical significance. Many of the old photos have been successfully digitized, but most of them suffer from severe and complicated distortion. Thus, prior studies have focused on image restoration tasks such as denoising, inpainting, and colorization. In this paper, we pay attention to the depth estimation of old photos, enabling a more enjoyable appreciation of them and helping better understand past human life, activities, and environments. Because most old photos are available as single-view images, monocular depth estimation techniques can be considered a solution. However, most high-performance techniques are based on supervised learning, which requires ground-truth depth maps. Because this kind of supervised learning is not feasible for old photos, in this paper, we present a learning framework that finetunes a pretrained monocular depth estimation network for each old photo. Specifically, the pretrained monocular depth estimation network predicts stereo depth maps for stereo image rendering. Then, the pretrained stereo network predicts depth estimates from the rendered stereo image pair. By extracting reliable depth estimates and using them for supervision of the monocular network, the monocular network can be gradually learned to produce a high-quality depth map of the given old photo. From the qualitative and quantitative performance evaluations on old photos, we demonstrate the effectiveness of the proposed method.
AVFP-MVX: Multimodal VoxelNet With Attention Mechanism and Voxel Feature Pyramid

In the current 3-D object detection tasks, most algorithms are based on pure point cloud. Although LiDAR can provide target location information and contour information for detection, it is sparse, especially for long-distance objects. Besides, camera sensors can provide more detailed target color, texture information, and so on. However, if both point cloud and image data are used for object detection at the same time, the problem of large model capacity and overfitting will occur. Different modes will also produce different gradients for different subnetworks, and the entire network will be difficult to optimize. In order to solve these problems and continuously improve the performance of detection algorithms, this article designs a 3-D object detection method attention mechanism and voxel feature pyramid multimodal VoxelNet (AVFP-MVX), which uses both point cloud and image data to solve the above problems. By referring to MVX-Net, attention mechanism and voxel feature pyramid are used to improve the detection accuracy of 3-D objects. The visualization results show that the overall performance of AVFP-MVX does well, which can accurately select the target object and return to a good bounding box. Comparative tests show that the proposed method detected 91.24%, 80.45%, and 76.91% for the easy, mod, and hard targets of car, respectively, while the average accuracy of pedestrian and cyclist is 62.44% and 67.64%, respectively, which is better than the other methods. The results of ablation experiments show that when the attention mechanism and the voxel feature pyramid network (Voxel-FPN) were added, the detection accuracy of car, pedestrian, and cyclist was increased by 1.87%, 1.85%, and 1.88%, respectively.
Semantic prior-driven fused contextual transformation network for image inpainting

Recent advances in image inpainting have achieved impressive performance for generating plausible visual details on small regular image defects or simple backgrounds. However, current solution suffers from the lack of semantic priors for the image and the inability to deduce the image content from distant background, leading to distorted structures and artifacts in the results when inpainting large random irregular complicated images. To address these problems, a semantic prior-driven fused contextual transformation network for image inpainting is proposed as a promise solution. First, the semantic prior generator is put forward to map the semantic features of ground truth images and the low-level features of broken images to semantic priors. Subsequently, an image split-transform-aggregated strategy, named fusion context transformation block, is presented to infer rich multi-scale remote texture features and thus to improve the restored image finesse. Thereafter, an aggregated semantic attention-aware module, consisting of spatially adaptive normalization and enhanced spatial attention is designed to aggregate semantic priors and multi-scale texture features into the decoder to restore reasonable structure. Finally, the mask guided discriminator is developed to effectively discriminate between real and false pixels in the output image to improve the capability of the discriminator and hence to reduce the probability of artifacts containing in the output image. Comprehensive experimental results on CelebA-HQ, Paris Street View, and Places2 datasets demonstrate the superiority of the proposed network over the state-of-the-arts, whose PSNR, SSIM and MAE are improved about 20 %, 12.6 %, and 42 % gains, respectively.
A multi-lingual benchmark for property-based testing of quantum programs

We present a multi-lingual benchmark for (property-based) testing of quantum programs. We report on the methodology used to design our benchmark and the rationale behind its design decisions.
The Efficiency of Tailored Systems for Language Education: An App Based on Scientific Evidence and for Student-Centered Approach

&lt;p style="text-align: justify;"&gt;In the vulnerable and unstable contexts that characterize populations in mobility between countries, especially immigrants and refugees, the second language learning has assumed new traits of difficulty. To help the new generation of learners, mainly with origin in forced immigration, the mobile-assisted learning helps motivation for language learning and reduces anxiety related to language acquisition. Attending to this challenge educational scenario, this review study presents a literature systematic analysis and a concrete technology tool that advocates the student-centered approach. A mobile pedagogical plan was developed for the learning of European Portuguese as a Second Language and as a Foreign Language. A critical review of 38 studies was conducted to understand how the mobile-assisted learning responds to the inclusion and education, especially concerning minorities. Based on previous empirical data with 108 immigrants, we understand the type of tasks that new immigrants have more difficulty learning in Portuguese. It was developed as a mobile app for Android, IOS, computers and tablets: the GoGenius app. In mobile format, individuals can access fourteen themed units with a symmetrical game architecture. These games focused the tasks and themes with priority for new language learners who recently arrived in a hosting country. These units involve a consistent number of working hours that intentionally intend to complement to the contact hours that the subjects have in classroom contexts or in unstable communication contexts (daily communication). This technology project aims to match “tailored” psychological and technological resources. Flipped classroom approach showed how mobile-assisted learning reinforces the educational goals worldwide, specifically for language learning. However, mobile tools should be well structured and centered on students’ needs, especially with migration backgrounds.&lt;/p&gt;
CodeBERT Based Software Defect Prediction for Edge-Cloud Systems

Edge-cloud system is a crucial computing infrastructure for the innovations of modern society. In addition, the high interest in the edge-cloud system leads to various studies for testing to ensure the reliability of the system. However, like traditional software systems, the amount of resources for testing is always limited. Thus, we suggest CodeBERT Based Just-In-Time (JIT) Software Defect Prediction (SDP) model to address the limitation. This method helps practitioners prioritize the limited testing resources for the defect-prone functions in commits and improves the system's reliability. We generate GitHub Pull-Request (GHPR) datasets on two open-source framework projects for edge-cloud system in GitHub. After that, we evaluate the performance of the proposed model on the GHPR datasets in within-project environment and cross-project environment. To the best of our knowledge, it is the first attempt to apply SDP to edge-cloud systems, and as a result of the evaluation, we can confirm the applicability of JIT SDP in edge-cloud project. In addition, we expect the proposed method would be helpful for the effective allocation of limited resources when developing edge-cloud systems.
A New Study of Integration Between Social Robotic Systems and the Metaverse for Dealing with Healthcare in the Post-COVID-19 Situations

With the popularity of the metaverse, researchers are turning to augmented reality and virtual reality to innovate their recent pain points, particularly healthcare issues during COVID-19. At the same time, social robots can be a great tool for alleviating many challenges during the pandemic. However, before the integrated technology’s possibilities for the metaverse and social robots can be suitably harnessed, certain recent developments for integration during the pandemic should be addressed. For this reason, this paper proposes a new systematic summary of pioneering social robotic systems using the metaverse through immersive experiences from an interdisciplinary healthcare perspective during the COVID-19 outbreak. We also highlight social robots to deal with medical healthcare issues during the virus outbreak both elderly adults and younger people. Moreover, we compare recent metaverse-driven social robotic works for dealing with assisted living and healthcare issues through telepresence and remote interaction during COVID-19. Ultimately, we provide a recommendation and forecast a future scenario for the integration between socially interactive robots and metaverse technology to improve and help the quality of life both in the current COVID-19 situation and in the post-COVID-19 society.
Mobile phones and rural women in South Asia and Africa: a systematic review

Mobile phones are the most popularly used Information and Communication Technology (ICT) tool globally, although its usage among rural women in South Asia and Africa is remarkably low. This is alarming given that ICTs are fast emerging as a development imperative globally. Thus, it is opportune to generate a clear and comprehensive understanding of rural women's patterns of mobile phone (MP) usage, the challenges therein, and to what extent they are able to negotiate the gender power relations embedded in mobile phones to access emancipatory spaces. This paper attempts to engage with these questions based on a systematic review of studies published in these two regions. The paper concludes that despite the persistence of several barriers to women's MP use, there remains enormous scope for the inclusion of MPs in the everyday lives of rural women, especially for delivering outcomes in the context of livelihood, e-health services, and personal well-being. It also highlights the need for more studies to understand how women negotiate gender power relationships embedded in the use of mobile telephony and access new possibilities and freedoms. These studies need to engage with methodologies that permit more in-depth understanding of the nuanced processes of performing gender and negotiating gendered meanings associated with the use of MPs.
Mitigating Catastrophic Forgetting with Complementary Layered Learning

Catastrophic forgetting is a stability–plasticity imbalance that causes a machine learner to lose previously gained knowledge that is critical for performing a task. The imbalance occurs in transfer learning, negatively affecting the learner’s performance, particularly in neural networks and layered learning. This work proposes a complementary learning technique that introduces long- and short-term memory to layered learning to reduce the negative effects of catastrophic forgetting. In particular, this work proposes the dual memory system in the non-neural network approaches of evolutionary computation and Q-learning instances of layered learning because these techniques are used to develop decision-making capabilities for physical robots. Experiments evaluate the new learning augmentation in a multi-agent system simulation, where autonomous unmanned aerial vehicles learn to collaborate and maneuver to survey an area effectively. Through these direct-policy and value-based learning experiments, the proposed complementary layered learning is demonstrated to significantly improve task performance over standard layered learning, successfully balancing stability and plasticity.
Recurrent Spectral Network (RSN): Shaping a discrete map to reach automated classification

A novel strategy to automated classification is introduced which exploits a fully trained dynamical system to steer items belonging to different categories toward distinct asymptotic attractors. These latter are incorporated into the model by taking advantage of the spectral decomposition of the operator that rules the linear evolution across the processing network. Non-linear terms act for a transient and allow to disentangle the data supplied as initial condition to the discrete dynamical system, shaping the boundaries of different attractors. The network can be equipped with several memory kernels which can be sequentially activated for serial datasets handling. Our novel approach to classification, that we here term Recurrent Spectral Network (RSN), is successfully challenged against a simple test-bed model, created for illustrative purposes, as well as a standard dataset for image processing training.
Sampling-based trajectory (re)planning for differentially flat systems: Application to a 3D gantry crane

In this paper, a sampling-based trajectory planning algorithm for a laboratory-scale 3D gantry crane in an environment with static obstacles and subject to bounds on the velocity and acceleration of the gantry crane system is presented. The focus is on developing a fast motion planning algorithm for differentially flat systems, where intermediate results can be stored and reused for further tasks, such as replanning. The proposed approach is based on the informed optimal rapidly exploring random tree algorithm (informed RRT*), which is utilized to build trajectory trees that are reused for replanning when the start and/or target states change. In contrast to state-of-the-art approaches, the proposed motion planning algorithm incorporates a linear quadratic minimum time (LQTM) local planner. Thus, dynamic properties such as time optimality and the smoothness of the trajectory are directly considered in the proposed algorithm. Moreover, by integrating the branch-and-bound method to perform the pruning process on the trajectory tree, the proposed algorithm can eliminate points in the tree that do not contribute to finding better solutions. This helps to curb memory consumption and reduce the computational complexity during motion (re)planning. Simulation results for a validated mathematical model of a 3D gantry crane show the feasibility of the proposed approach.
Predicting solutions of the stochastic fractional order dynamical system using machine learning

The solution of fractional-order systems has been a complex problem for our research. Traditional methods like the predictor-corrector method and other solution steps are complicated and cumbersome to derive, which makes it more difficult for our solution efficiency. The development of machine learning and nonlinear dynamics has provided us with new ideas to solve some complex problems. Therefore, this study considers how to improve the accuracy and efficiency of the solution based on traditional methods. Finally, we propose an efficient and accurate nonlinear auto-regressive neural network for the fractional order dynamic system prediction model (FODS-NAR). First, we demonstrate by example that the FODS-NAR algorithm can predict the solution of a stochastic fractional order system. Second, we compare the FODS-NAR algorithm with the famous and good reservoir computing (RC) algorithms. We find that FODS-NAR gives more accurate predictions than the traditional RC algorithm with the same system parameters, and the residuals of the FODS-NAR algorithm are closer to 0. Consequently, we conclude that the FODS-NAR algorithm is a method with higher accuracy and prediction results closer to the state of fractional-order stochastic systems. In addition, we analyze the effects of the number of neurons and the order of delays in the FODS-NAR algorithm on the prediction results and derive a range of their optimal values.
SURE: Screening unlabeled samples for reliable negative samples based on reinforcement learning

For many classification tasks, particularly in the bioinformatics field, only experimentally validated positive samples are available, and experimentally validated negative samples are not recorded. The lack of negative samples poses a challenge for using machine learning to perform such tasks. To address this problem, we propose a novel deep reinforcement learning-based model to screen reliable negative samples from unlabeled samples, named SURE. The model has two modules: a sample selector and a sample inspector. The sample selector screens reliable negative samples from unlabeled samples by two reinforcement strategies (learn to identify positive samples and reduce sample noise) and feeds the screened samples into the sample inspector. The sample inspector classifier provides rewards to the sample selector. The two modules are trained together to optimize the sample selector and sample inspector strategies. In this paper, we focus on one popular issue in the bioinformatics field: the ncRNA-protein interaction (NPI) prediction task, which lacks reliable negative samples. Thirty datasets for NPI prediction are used to test the screening effect of SURE. The experimental results show that our model has a robust negative sample screening capability and is superior to all outstanding sample screening methods used in the NPI prediction task. In addition, we refine 5 NPI datasets containing reliable negative samples screened by SURE, and a web server (ww.csbg-jlu.info/sure) is available offering the NPI prediction refined by SURE.
dcor: Distance correlation and energy statistics in Python

This article presents dcor, an open-source Python package dedicated to distance correlation and other statistics related to energy distance.These energy statistics include distances between distributions and the associated tests for homogeneity and independence.Some of the most efficient algorithms for the estimation of these measures have been implemented relying on optimization techniques such as vectorization, compilation, and parallelization.The performance of these estimators is evaluated by comparison with alternative implementations in other packages.The package is also designed to be compatible with the packages conforming the scientific Python ecosystem.With that purpose in mind, dcor is an early adopter of the Python array API standard.
Supplementing recurrent neural networks with annealing to solve combinatorial optimization problems

Abstract Combinatorial optimization problems can be solved by heuristic algorithms such as simulated annealing (SA) which aims to find the optimal solution within a large search space through thermal fluctuations. This algorithm generates new solutions through Markov-chain Monte Carlo techniques which can result in severe limitations, such as slow convergence and a tendency to stay within the same local search space at small temperatures. To overcome these shortcomings, we use the variational classical annealing (VCA) framework that combines autoregressive recurrent neural networks (RNNs) with traditional annealing to sample solutions that are uncorrelated. In this paper, we demonstrate the potential of using VCA as an approach to solving real-world optimization problems. We explore VCA’s performance in comparison with SA at solving three popular optimization problems: the maximum cut problem (Max-Cut), the nurse scheduling problem (NSP), and the traveling salesman problem (TSP). For all three problems, we find that VCA outperforms SA on average in the asymptotic limit by one or more orders of magnitude in terms of relative error. Interestingly, we reach large system sizes of up to 256 cities for the TSP. We also conclude that in the best case scenario, VCA can serve as a great alternative when SA fails to find the optimal solution.
Asymmetric Markovian control of quantum entanglement

Abstract Expanding utilization of entangled states in quantum technologies, such as quantum information, is a motivating force of developing new methods for enhancement and stabilization of quantum entanglement. This study focuses on using asymmetric lawsto control the entangled states of a quantum systemconsistingof two atoms, each confined in a cavity.The effect of asymmetry laws has been explored in three different scenarios. First, the effect of an asymmetric drive Hamiltonian on a closed quantum system, in which neither the cavity nor the atoms exhibit losses, is studied. Here, the eigenvalues and eigenstates of the total system Hamiltonian have been obtained and the time evolution of the system state has been derived. Also, the fidelity of the system in terms of the asymmetric drive Hamiltonian has been derived analytically. In the second scenario, the stationary solution of an open quantum system, which includes losses in a master equation approach, is derived and the concurrence is studied in terms of the asymmetric drive Hamiltonian and coupling constant. The last scenario is devoted to applying feedback rulesto an open quantum system where some heuristic feedback control laws have been proposed. The simulation results show the concurrence boosting in a larger range of driving field and feedback strength when applying the introduced feedback rules.