Reviewerly/exHarmony huggingface dataset:
Social media use for offline political action (OPA) and corruption in Africa: Impacts and transmission channels

Despite a growing literature on the determinants of corruption, existing studies are sparse on the channels through which social media curbs corruption using panel data. Social media is captured by the percentages of the population and elites that use social media for offline political actions (OPA). This research uses annual data from a panel of 47 African countries over the period 2000–2018. Results show that social media used by the population for OPA directly curbs executive, judicial and legislative corruption. The use of social media by elites for OPA boosts corruption in the judicial sector. Moreover, social media indirectly curbs corruption through their effects on civil society participation. Reducing corruption in Africa requires inter alia, policies aimed at promoting the use of social media for OPA, the emergence of dynamic and effective civil society participation and the improvement of the quality of democracy.
Understanding the use of spectrum‐based fault localization

Summary Developers spend significant time locating and fixing bugs, which is often performed manually. Although spectrum‐based fault localization (SFL) techniques aim at helping developers to locate faults, they are not yet used in practice. Recent studies have investigated how developers use SFL, presenting different conclusions about their effectiveness and usefulness. We carried out a user study to further enhance the understanding of SFL. We assessed whether SFL improves the developers' performance and to what extent SFL leads developers to inspect faulty code excerpts. We also investigated the intention of the developers to use SFL and how they interact with SFL. Twenty‐six participants performed debugging tasks using real programs, with and without using the Jaguar SFL tool. Using SFL, more developers located and fixed the bugs. SFL also led more developers to inspect the faulty code and locate the faulty method. However, they did not spend less time locating the faults. SFL was well‐accepted by the participants, who showed intention to use it in their daily activities. Our results indicate that SFL is useful even when the fault is not ranked among the first positions, leading developers to reach faulty code regions and find the bugs.
TRACL: Temporal reconstruction and adaptive consistency loss for semi‐supervised video semantic segmentation

Abstract While existing supervised semantic segmentation methods have shown significant performance improvements, they heavily rely on large‐scale pixel‐level annotated data. To reduce this dependence, recent research has proposed semi‐supervised learning‐based methods that have achieved great success. However, almost all these works are mainly dedicated to image semantic segmentation, while semi‐supervised video semantic segmentation (SVSS) has been barely explored. Due to the significant difference between video data and image, simply adapting semi‐supervised image semantic segmentation approaches to SVSS may neglect the inherent temporal correlations in video frames. This paper presents a novel method (named TRACL) with temporal reconstruction (TR) and adaptive consistency loss (ACL) for SVSS, aiming to fully utilize the temporal relations of internal frames in video clip. The authors’ TR method implements the reconstruction from the feature and output levels to narrow the distribution gap between internal video frames. Specifically, considering the underlying data distribution, the authors construct Gaussian models for each category, and use probability density function to obtain the similarity between different feature maps for temporal feature reconstruction. The authors’ ACL can adaptively select two pixel‐wise consistency loss including Flow Consistency Loss and Reconstruction Consistency Loss, providing stronger supervision signals for unlabelled frames during model training. Additionally, the authors extend their method to unlabelled video for more training data by employing mean‐teacher structure. Extensive experiments on three datasets including Cityscapes, Camvid and VSPW demonstrate that the authors’ proposed method outperforms previous state‐of‐the‐art methods.
Secure transmission of ocean images using deep learning‐based data hiding

Abstract Data hiding has become a hot research topic in recent years due to increased attention placed on the copyright protection of ocean images and related digital records. Further, high image volumes put enormous pressure on transmission bandwidth and storage capabilities. This paper proposes an innovative deep learning‐based data‐hiding technique for ocean images. First, a down‐sampling scheme is applied to compress the secret mark before embedding it in the host media. Then, a convolutional neural network is used to embed and recover compressed marks into or from the host ocean image. Finally, a generative adversarial network‐based reconstruction network is used to reconstruct the high‐quality mark image. Our experiments show that the proposed work not only maintains high imperceptibility and robustness against many attacks but also provides better data‐hiding performance than related works.
Synchronous mixed reality (SMR): A personalized virtual‐real fusion framework with high immersion and effective interaction

Abstract Virtual‐real fusion (VRF) technology plays a crucial role in the meta‐universe by bridging the gap between virtual environments (VEs) and physical environments (PEs). However, the current VRF system has the problem of single function and fixed integration, limiting the popularization and application of VRF technology. Therefore, this paper proposes a framework named synchronous mixed reality (SMR) to personalize the VRF system and maintain the balance between interaction efficiency and environmental immersion. We combine an instance segmentation algorithm with an interaction prediction algorithm to achieve this balance. To evaluate the effectiveness of the SMR framework, we design three scenarios based on the interaction properties of physical objects and measure environmental immersion, systematic interaction efficiency, and user experience. Our results demonstrate that the SMR framework meets diverse needs in various PEs while balancing immersion and interaction efficiency. Additionally, our framework significantly reduces collisions and negative emotions experienced by users in VEs. We anticipate that this framework will serve as a guide for constructing virtual‐real fusion systems in the future.
Reparative futures of smart urban governance: A speculative design approach for multispecies justice

In recent years, urban governance within smart city contexts has increasingly turned towards processes and practices of “futuring” to guide city visions and planning. Particularly with the increased focus on sustainability within smart urban governance, these futuring practices often rely on environmental monitoring technologies to guide reparative actions for environmental harms and promote multispecies justice. Yet, these futuring practices often overlook or are disconnected from the past where these environmental harms and injustices were sustained. Little attention has been paid to how smart urban governance might consider both past and future to better account for multispecies justice. This paper introduces a speculative design methodology within a reparative futures framework as it explores multispecies justice in smart urban governance. It reports on a methodological study conducted in Brisbane, Australia, in which participants—including policymakers, environmental lawyers, scientists, urban planners, and climate activists—undertook a speculative futuring design process intending to address past harms faced by multiple species and to repair relations within a more-than-human world. Direct engagement with the past in futuring practices helps us reflect on how smart urban governance might take accountability and institute better reparative actions to materialise multispecies justice.
Understanding markers of trust within the online stolen data market: An examination of vendors’ signaling behaviors relative to product price point

Abstract Research Summary The current study examined 1055 stolen data products across 40 vendors on the Open and Dark Web to determine whether different product‐ and vendor‐level behaviors predicted vendors’ trustworthiness as reflected in their product price point. Understanding the mechanisms that convey trust in the underground marketplace is crucial as it could help law enforcement target serious actors and disrupt the larger marketplace. Findings suggest the online stolen data market may resemble an uninformative cost condition where buyers are unable to accurately differentiate credible sellers due to the obscure nature of signaling behaviors. Policy Implications Law enforcement would benefit from designing fake shops and deceptive forum posts that transmit mixed signals to complicate market participants’ process of interpreting trust signals as intended. These interventions would generate high levels of risk that encourage both buyers and sellers to exit the online illicit marketplace without needing law enforcement arrests. Law enforcement could also target prominent market facilitators to generate a larger disruption that prevents actors from continuing their illicit behavior.
Deep Neural Networks in Natural Language Processing for Classifying Requirements by Origin and Functionality: An Application of BERT in System Requirements

Abstract Given the foundational role of system requirements in design projects, designers can benefit from classifying, comparing, and observing connections between requirements. Manually undertaking these processes, however, can be laborious and time-consuming. Previous studies have employed Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art natural language processing (NLP) deep neural network model, to automatically analyze written requirements. Yet, it remains unclear whether BERT can sufficiently capture the nuances that differentiate requirements between and within design documents. This work evaluates BERT’s performance on two requirement classification tasks (one inter- document and one intra-document) executed on a corpus of 1,303 requirements sourced from five system design projects. First, in the “parent document classification” task, a BERT model is fine-tuned to classify requirements according to their originating project. A separate BERT model is then fine-tuned on a “functional classification” task where each requirement is classified as either functional or nonfunctional. Our results also include a comparison with a baseline model, Word2Vec, and demonstrate that our model achieves higher classification accuracy. When evaluated on test sets, the former model receives a Matthews correlation coefficient (MCC) of 0.95, while the latter receives an MCC of 0.82, indicating BERT’s ability to reliably distinguish requirements. This work then explores the application of BERT’s representations, known as embeddings, to identify similar requirements and predict requirement change.
Blind motion deblurring using improved DeblurGAN

Abstract To develop a fast and effective image deblurring method, the blind recovery of motion‐blurred images based on DeblurGAN(GAN, Generative Adversarial Networks) is researched.Firstly, the number of residual modules in the DeblurGAN network is changed, and an attempt is made to optimize the network structure to achieve better results in the blind recovery of motion‐blurred images. Secondly, an image deblurring method based on the improved DeblurGAN network is proposed.This paper makes corresponding adjustments to the generator and discriminator networks to change their input and output size to 512 × 512 while keeping the overall structure of the network unchanged, and the experimental results show that the quality of the restored images has been greatly improved. In addition, the images were divided into four classes of images,to achieve improved restoration of blurred images with specific content. Experimental results on the benchmark GoPro dataset validate that the improved DeblurGAN achieves more than 1.5 dB improvement than DeblurGAN in terms of peak signal‐to‐noise ratio (PSNR) as trained utilizing the same amount of data, and structural similarity assessment (SSIM) evaluation means and maximum values increased between 0.2 and 0.3. The improved DeblurGAN is more effective in terms of both blur removal and detail recovery.
Gerakan Literasi Madrasah Sebagai Upaya Menumbuhkan Minat baca Siswa Madrasah Tsanawiyah

Writing this article aims to analyze and describe the strategy of the Madrasah Literacy Movement (GLM) to foster reading interest among MTs students. Ma'arif Daruttaqwa. This study uses a qualitative method with a case study approach, data collection through interviews, and documents. data analysis using the Miles and Huberman model, namely data reduction, data display and conclusion verification. The results of this research are strategies to increase students' interest in reading including 1. Program 15 reading before learning begins. 2. Improvement of the library and procurement of textbooks and non-learning. 3. Motivation from administrators and teachers and the surrounding environment. Besides the three things above, giving assignments to students is also able to foster interest in reading in madrasah students.
Beyond the dawn of virtualized learning environments: A comparative study of video and augmented reality information delivery on student engagement and knowledge retention

Abstract Background Education is, after gaming, the second largest sector embracing augmented reality (AR) at an accelerated pace, yet studies on AR's potential as an efficient learning environment had mixed results. Objectives This study's primary objective is to test students' interaction with graphical 3D elements in AR and its impact on information retention. The second objective is to compare information retention from AR engagement with information retention from exposure to identical 3D content delivered via instructional video. Finally, the third objective explores students' perceptions of AR as a learning environment. Methods Sixty‐five college students participated in a two‐arm experiment followed by semi‐structured interviews. Results and Conclusion AR can efficiently deliver a meaningful learning experience and promotes information retention. Nevertheless, AR's interactive features and novelty may distract learners while they engage with the virtual environment. Objective data obtained through eye tracking and video monitoring showed similar levels of engagement with a 3D model for both environments. While both AR and video groups showed significant knowledge gain from pre to post‐intervention, the video format was overall significantly more effective than AR in increasing topic‐related information retention. However, participants preferred the AR learning environment significantly more than the video. Takeaways Although effective, AR is still a distracting instructional environment. Designing learning experiences tailored explicitly for AR environments requires additional research to ensure its successful deployment as a learning tool.
Two tales of platoon intelligence for autonomous mobility control: Enabling deep learning recipes

Abstract This paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness.
Leaky echo state network based on methane topology applied to time series prediction

Abstract In this paper, a proposed echo state network (ESN) model called F‐ESN is based on methane topology. Due to the neurons in the reservoir of the ESN model being random and sparsely connected, the stability of the system structure could be better, and the prediction accuracy could be higher. To solve this issue, the reservoir's neuronal connectivity is modified to a certain methane structure. A master neuron governs each little unit and communicates with the others to pass information. In addition, MFO optimized by adaptive dynamic operators is used to optimize the three parameters of ESN : leakage rate ( a ), spectral radius of the connection weight matrix (ρ), and input scale factor . To verify the effectiveness of the proposed method, low‐frequency sin time series, high‐frequency SIN time series, and MG time series are simulated. The experimental results show that the methane topology can further improve the prediction accuracy of leakage echo state networks.
Operator visual attention allocation prediction in a robotic arm teleoperation interface

Abstract In digital interactive interfaces with high visual workloads, it is important for operators to allocate their limited attentional resources appropriately to ensure efficient information collection. The salience, effort, expectancy, value (SEEV) model, which combines top‐down and bottom‐up attention mechanisms for predicting attention allocation, has been validated in research areas such as piloting, driving, and surgical operations. However, the validity of the SEEV model in the field of robotic arm teleoperation has not yet been thoroughly studied. The primary purpose of this study was to confirm the feasibility of the SEEV model for operator visual attention allocation prediction in a robotic arm teleoperation scenario. The improved ITTI algorithm, distance‐measuring tool, Delphi method, and lowest ordinal algorithm were adopted to qualify the four factors of the SEEV model, which also contributed to salience and expectancy quantification methods. Accordingly, an attention allocation prediction model in a robotic arm teleoperation scene was constructed. To verify the validity of the prediction model, 20 participants were recruited to control the robotic arm using V‐REP simulation software, and their fixation durations were recorded using an eye tracker as an attention allocation indicator. Participants controlled the robotic arm according to the experimental requirements and operational tasks, such as grasping and placing the target. The results demonstrated that the theoretical data based on the SEEV prediction model are significantly related to the proportion of fixation durations. The experiment verifies the suitability of the SEEV prediction model, and it is anticipated to be utilized in the optimization of interactive interfaces for robotic arm teleoperation.
Nonparametric point cloud filter

Abstract This paper proposes a nonparametric point cloud filter to address the issue that existing point cloud filtering methods cannot retain important point cloud features after filtering and often require complex parameter adjustments. Firstly, a nonparametric clustering method is proposed to cluster various features of the point cloud and filter out isolated outliers. Then, the manifold distance truncation method is adopted to remove the outlier cluster generated by the point cloud clustering to complete point cloud filtering. Additionally, the proposed nonparametric clustering algorithm is compared with four of the latest clustering algorithms, including K‐means clustering and OPTICS clustering to verify the rationality of the clustering features. Finally, the filtering results of the nonparametric point cloud filter are compared with those of statistical filtering and two other recently proposed point cloud filters to demonstrate its improved filtering effect and algorithm stability. The experimental results indicate that the proposed nonparametric point cloud filter can achieve better‐filtering results and retain more point cloud features without adjusting parameters.
Greedy optimization of resistance-based graph robustness with global and local edge insertions

Abstract The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph G . We consider two optimization problems of adding k new edges to G such that the resulting graph has minimal total effective resistance (i.e., is most robust)—one where the new edges can be anywhere in the graph and one where the new edges need to be incident to a specified focus node. The total effective resistance and effective resistances between nodes can be computed using the pseudoinverse of the graph Laplacian. The pseudoinverse may be computed explicitly via pseudoinversion, yet this takes cubic time in practice and quadratic space. We instead exploit combinatorial and algebraic connections to speed up gain computations in an established generic greedy heuristic. Moreover, we leverage existing randomized techniques to boost the performance of our approaches by introducing a sub-sampling step. Our different graph- and matrix-based approaches are indeed significantly faster than the state-of-the-art greedy algorithm, while their quality remains reasonably high and is often quite close. Our experiments show that we can now process larger graphs for which the application of the state-of-the-art greedy approach was impractical before.
Why do young people start and continue with ethical hacking? A qualitative study on individual and social aspects in the lives of ethical hackers

Abstract Research Summary This qualitative interview study examined individual and social aspects in the lives of 15 hackers who started with ethical hacking before the age of 18. We examined what motivated and stimulated them to start with ethical hacking and how and why they continued. Results show that individual aspects, such as an early interest in ICT (Information and Communication Technology), the motivation to make systems more secure, and thinking of reporting vulnerabilities as a moral duty, contribute to the initiation of ethical hacking. Social aspects, such as role models, mainly play a role in the further development of the ethical hacking career. For example, peers, parents, friends, and hacker communities have a motivating and stimulating effect on young ethical hackers. Similarly, positive reactions from public and private system owners can further stimulate the development and self‐image of ethical hackers. Policy Implications In contrast to criminal hackers, ethical hackers improve cybersecurity, for example, by reporting weaknesses or errors in computer systems. Nowadays, an increasing number of public and private organizations around the world are creating cybersecurity policies and practices around ethical hacking. The interest in hacking usually arises at an early age, making it important to gain knowledge about what stimulates and motivates young people to start and continue with ethical hacking, to inform these policies and practices. Our results show that recognition and reactions from system owners are important in the ethical hacking career. Our recommendations based on these results could stimulate more young people to take the ethical path. Simultaneously, this increases the security of public and private organizations, reducing opportunities for criminal hackers.