Reviewerly/exHarmony huggingface dataset:
Single image mixed dehazing method based on numerical iterative model and DehazeNet

As one of the most common adverse weather phenomena, haze has caused detrimental effects on many computer vision systems. To eliminate the effect of haze, in the field of image processing, image dehazing has been studied intensively, and many advanced dehazing algorithms have been proposed. Physical model-based and deep learning-based methods are two competitive methods for single image dehazing, but it is still a challenging problem to achieve fidelity and effectively dehazing simultaneously in real hazy scenes. In this work, a mixed iterative model is proposed, which combines a physical model-based method with a learning-based method to restore high-quality clear images, and it has good performance in maintaining natural attributes and completely removing haze. Unlike previous studies, we first divide the image into different regions according to the density of haze to accurately calculate the atmospheric light for restoring haze-free images. Then, dark channel prior and DehazeNet are used to jointly estimate the transmission to promote the final clear haze-free image that is more similar to the real scene. Finally, a numerical iterative strategy is employed to further optimize the atmospheric light and transmission. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic datasets and real-world datasets. Moreover, to indicate the universality of the proposed method, we further apply it to the remote sensing datasets, which can also produce visually satisfactory results.
Learnable Uncertainty under Laplace Approximations

Laplace approximations are classic, computationally lightweight means for constructing Bayesian neural networks (BNNs). As in other approximate BNNs, one cannot necessarily expect the induced predictive uncertainty to be calibrated. Here we develop a formalism to explicitly train the uncertainty in a decoupled way to the prediction itself. To this end, we introduce uncertainty units for Laplace-approximated networks: Hidden units associated with a particular weight structure that can be added to any pre-trained, point-estimated network. Due to their weights, these units are inactive---they do not affect the predictions. But their presence changes the geometry (in particular the Hessian) of the loss landscape, thereby affecting the network's uncertainty estimates under a Laplace approximation. We show that such units can be trained via an uncertainty-aware objective, improving standard Laplace approximations' performance in various uncertainty quantification tasks.
Conversion of Unstructured to Structured: A Solution Using Data Science and NOSQL

Exponentially rise of unstructured data is a question to all data scientists today. The graph of unstructured data is at such height that it consumes most of the storage of all clouds present today. Analysis through unstructured data is not easy due its high complexity and abstraction computations. There are different forms of unstructured data found inside web and different operations are done for their conversion. In this paper we tried to touch most of the unstructured data types and their related solution for specific conversion using data science analytics. Mongodb, famous for horizontal scaling is also used here for collection and storage of scalable and high volume data.
Uso de videojuegos en alumnos de la escuela secundaria y factores asociados

Video game use among secondary school students and associated factors
ChainLineNet: Deep-Learning-Based Segmentation and Parameterization of Chain Lines in Historical Prints

The paper structure of historical prints is sort of a unique fingerprint. Paper with the same origin shows similar chain line distances. As the manual measurement of chain line distances is time consuming, the automatic detection of chain lines is beneficial. We propose an end-to-end trainable deep learning method for segmentation and parameterization of chain lines in transmitted light images of German prints from the 16th Century. We trained a conditional generative adversarial network with a multitask loss for line segmentation and line parameterization. We formulated a fully differentiable pipeline for line coordinates’ estimation that consists of line segmentation, horizontal line alignment, and 2D Fourier filtering of line segments, line region proposals, and differentiable line fitting. We created a dataset of high-resolution transmitted light images of historical prints with manual line coordinate annotations. Our method shows superior qualitative and quantitative chain line detection results with high accuracy and reliability on our historical dataset in comparison to competing methods. Further, we demonstrated that our method achieves a low error of less than 0.7 mm in comparison to manually measured chain line distances.
Innovative solutions for effective conflict resolution in higher education institutions

The article discusses innovative solutions for effective conflict resolution in higher education institutions. In the analysis of conflict situations, the predominance of two methods of conflict resolution in the organization, the method of adaptation and conciliation. The results of the use of coordination mechanisms as a method of managing an acute conflict situation are described.
Toward quantum simulating non-Abelian gauge theories

In this article, we review some of the recent developments toward the future goal of quantum computing or quantum simulating lattice-QCD. This includes a novel theoretical framework developed for non-Abelian gauge theories that is the first necessary step toward this goal. We also review some immediate applications of this framework in the context of both digital and analog quantum simulations of SU(2) lattice gauge theory coupled with staggered fermions.
JomNFCSignIN: Seamlessly Register Your Class Attendance

Attendance in educational institutions worldwide has become a major requirement. The evidence is showing that the traditional pen-paper method lost a tremendous amount of time and numerous forgeries occur. Smartphones are increasingly widespread with technology and Near Field Communication is a crucial factor for the payment and other contactless methods of transferring information. This allowed faculty members to reduce their workload and to focus more on teaching. This proposed study will explain how the application will help the students participate in the class with ease and efficiency. Besides that, it will also allow the lecturers to have more time to spend. The application will track student attendance, follow the rules of the university, and provide a significantly cheaper operating and set-up infrastructure. The data are gathered by interview sessions and surveys from various government and private universities using a mixed-mode method and with very optimistic results.
Fast Facial Animation from Video

Real time facial animation for virtual 3D characters has important applications such as AR/VR, interactive 3D entertainment, pre-visualization and video conferencing. Yet despite important research breakthroughs in facial tracking and performance capture, there are very few commercial examples of real-time facial animation applications in the consumer market. Mass adoption requires realtime performance on commodity hardware and visually pleasing animation that is robust to real world conditions, without requiring manual calibration. We present an end-to-end deep learning framework for regressing facial animation weights from video that addresses most of these challenges. Our formulation is fast (3.2 ms), utilizes images of real human faces along with millions of synthetic rendered frames to train the network on real-world scenarios, and produces jitter-free visually pleasing animations.
Heterogeneous Risk Minimization

Machine learning algorithms with empirical risk minimization usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training data, which are not stable under distributional shifts. Recently, some invariant learning methods for out-of-distribution (OOD) generalization have been proposed by leveraging multiple training environments to find invariant relationships. However, modern datasets are frequently assembled by merging data from multiple sources without explicit source labels. The resultant unobserved heterogeneity renders many invariant learning methods inapplicable. In this paper, we propose Heterogeneous Risk Minimization (HRM) framework to achieve joint learning of latent heterogeneity among the data and invariant relationship, which leads to stable prediction despite distributional shifts. We theoretically characterize the roles of the environment labels in invariant learning and justify our newly proposed HRM framework. Extensive experimental results validate the effectiveness of our HRM framework.
Detection of fake-video uploaders on social media using Naive Bayesian model with social cues

Abstract With the rapid development of the Internet, the wide circulation of disinformation has considerably disrupted the search and recognition of information. Despite intensive research devoted to fake text detection, studies on fake short videos that inundate the Internet are rare. Fake videos, because of their quick transmission and broad reach, can increase misunderstanding, impact decision-making, and lead to irrevocable losses. Therefore, it is important to detect fake videos that mislead users on the Internet. Since it is difficult to detect fake videos directly, we probed the detection of fake video uploaders in this study with a vision to provide a basis for the detection of fake videos. Specifically, a dataset consisting of 450 uploaders of videos on diabetes and traditional Chinese medicine was constructed, five features of the fake video uploaders were proposed, and a Naive Bayesian model was built. Through experiments, the optimal feature combination was identified, and the proposed model reached a maximum accuracy of 70.7%.
The continuous wavelet derived by smoothing function and its application in cosmology

The wavelet analysis technique is a powerful tool and is widely used in broad disciplines of engineering, technology, and sciences. In this work, we present a novel scheme of constructing continuous wavelet functions, in which the wavelet functions are obtained by taking the first derivative of smoothing functions with respect to the scale parameter. Due to this wavelet constructing scheme, the inverse transforms are only one-dimensional integrations with respect to the scale parameter, and hence the continuous wavelet transforms (CWTs) constructed in this way are more ready to use than the usual scheme. We then apply the Gaussian-derived wavelet constructed by our scheme to computations of the density power spectrum for dark matter, the velocity power spectrum and the kinetic energy spectrum for baryonic fluid. These computations exhibit the convenience and strength of the CWTs. The transforms are very easy to perform, and we believe that the simplicity of our wavelet scheme will make CWTs very useful in practice.
Virtual Animals as Diegetic Attention Guidance Mechanisms in 360-Degree Experiences

360-degree experiences such as cinematic virtual reality and 360-degree videos are becoming increasingly popular. In most examples, viewers can freely explore the content by changing their orientation. However, in some cases, this increased freedom may lead to viewers missing important events within such experiences. Thus, a recent research thrust has focused on studying mechanisms for guiding viewers' attention while maintaining their sense of presence and fostering a positive user experience. One approach is the utilization of diegetic mechanisms, characterized by an internal consistency with respect to the narrative and the environment, for attention guidance. While such mechanisms are highly attractive, their uses and potential implementations are still not well understood. Additionally, acknowledging the user in 360-degree experiences has been linked to a higher sense of presence and connection. However, less is known when acknowledging behaviors are carried out by attention guiding mechanisms. To close these gaps, we conducted a within-subjects user study with five conditions of no guide and virtual arrows, birds, dogs, and dogs that acknowledge the user and the environment. Through our mixed-methods analysis, we found that the diegetic virtual animals resulted in a more positive user experience, all of which were at least as effective as the non-diegetic arrow in guiding users towards target events. The acknowledging dog received the most positive responses from our participants in terms of preference and user experience and significantly improved their sense of presence compared to the non-diegetic arrow. Lastly, three themes emerged from a qualitative analysis of our participants' feedback, indicating the importance of the guide's blending in, its acknowledging behavior, and participants' positive associations as the main factors for our participants' preferences.
All-Optical Synapse With Directional Coupler Structure Based on Phase Change Material

With the rapid growth of the artificial neural network, the operational efficiency of von Neumann computing architecture is limited by the separation of memory and processor, and the exploration of the efficient hardware mimicking bionic neurons and synapses has become a matter of great urgency. Moreover, the circuit implements for this architecture have potential limitations such as slow switching speed, high computational power consumption, and high interconnection loss. As an alternative, neuromorphic engineering in the photonic domain has recently gained widespread international attention. In this work, we propose an all-optical synaptic device based on a directional coupler structure, which can control the degree of light field distribution variation by changing the state of the phase change material Ge <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Sb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Te <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sub> (GST) distributed in discrete islands on one port. The mode distribution and propagation of the field have been carefully analyzed with pulsed light of different power. More importantly, it is possible to flexibly design the weight update of the synaptic devices by varying the size and location of the GST islands. Verified by the Spiking Neural Network, we can improve the recognition accuracy of handwritten figures from the original 50% to 91% by improving the linearity and accuracy of the synapse weights. This may provide a new solution for future low-power non-volatile photonic integrated circuits.
Real-time simulation of light-driven spin chains on quantum computers

In this work, we study the real-time evolution of periodically driven (Floquet) systems on a quantum computer using IBM quantum devices. We consider a driven Landau-Zener model and compute the transition probability between the Floquet steady states as a function of time. We find that for this simple one-qubit model, Floquet states can develop in real-time, as indicated by the transition probability between Floquet states. Next, we model light-driven spin chains and compute the time-dependent antiferromagnetic order parameter. We consider models arising from light coupling to the underlying electrons as well as those arising from light coupling to phonons. For the two-spin chains, the quantum devices yield time evolutions that match the effective Floquet Hamiltonian evolution for both models once readout error mitigation is included. For three-spin chains, zero-noise extrapolation yields a time dependence that follows the effective Floquet time evolution. Therefore, the current IBM quantum devices can provide information on the dynamics of small Floquet systems arising from light drives once error mitigation procedures are implemented.
Exploring Disentanglement with Multilingual and Monolingual VQ-VAE

This work examines the content and usefulness of disentangled phone and speaker representations from two separately trained VQ-VAE systems: one trained on multilingual data and another trained on monolingual data. We explore the multi- and monolingual models using four small proof-of-concept tasks: copy-synthesis, voice transformation, linguistic code-switching, and content-based privacy masking. From these tasks, we reflect on how disentangled phone and speaker representations can be used to manipulate speech in a meaningful way. Our experiments demonstrate that the VQ representations are suitable for these tasks, including creating new voices by mixing speaker representations together. We also present our novel technique to conceal the content of targeted words within an utterance by manipulating phone VQ codes, while retaining speaker identity and intelligibility of surrounding words. Finally, we discuss recommendations for further increasing the viability of disentangled representations.
An empirical study of Linespots: A novel past‐fault algorithm

This paper proposes the novel past-faults fault prediction algorithm Linespots, based on the Bugspots algorithm. We analyze the predictive performance and runtime of Linespots compared to Bugspots with an empirical study using the most significant self-built dataset as of now, including high-quality samples for validation. As a novelty in fault prediction, we use Bayesian data analysis and Directed Acyclic Graphs to model the effects. We found consistent improvements in the predictive performance of Linespots over Bugspots for all seven evaluation metrics. We conclude that Linespots should be used over Bugspots in all cases where no real-time performance is necessary.
Heterogeneous multipartite entanglement purification for size-constrained quantum devices

The entanglement resource required for quantum information processing comes in a variety of forms, from Bell states to multipartite GHZ states or cluster states. Purifying these resources after their imperfect generation is an indispensable step towards using them in quantum architectures. While this challenge, both in the case of Bell pairs and more general multipartite entangled states, is mostly overcome in the presence of perfect local quantum hardware with unconstrained qubit register sizes, devising optimal purification strategies for finite-size realistic noisy hardware has remained elusive. Here we depart from the typical purification paradigm for multipartite states explored in the last twenty years. We present cases where the hardware limitations are taken into account, and surprisingly find that smaller `sacrificial' states, like Bell pairs, can be more useful in the purification of multipartite states than additional copies of these same states. This drastically simplifies the requirements and presents a fundamentally new pathway to leverage near term networked quantum hardware.
Diegetic and Non-diegetic Health Interfaces in VR Shooter Games

The player’s health is one of the most pervasive components in computer games. However, in virtual reality games, it is unclear how different representations of player health function compared to traditional flat-screen games. Because the viewpoint changes based on the player’s head movement, non-diegetic UI elements may not be ideal. Also, the sense of embodiment in VR provides opportunities to experiment with diegetic ways of communicating the player’s health. To investigate different implementations of player health in VR games, we developed three health interfaces and evaluated them in a shooter game. The health interfaces included: 1) A non-diegetic health bar, visible on the screen at all times, 2) A diegetic health value on a virtual wristwatch, and 3) A diegetic physical interface, where lost health results in trembling and slower movement. 37 participants played the game using all three health interfaces and provided feedback. We found that all three interfaces had their own strengths. The non-diegetic health bar was seen as suitable for multi-player games, while the wristwatch was seen as suitable for single-player, story-driven games. The physical interface was liked for its impact on gameplay, and was also seen as suitable for story-driven games.
Imaginarity-free quantum multiparameter estimation

Multiparameter quantum estimation is made difficult by the following three obstacles. First, incompatibility among different physical quantities poses a limit on the attainable precision. Second, the ultimate precision is not saturated until you discover the optimal measurement. Third, the optimal measurement may generally depend on the target values of parameters, and thus may be impossible to perform for unknown target states. We present a method to circumvent these three obstacles. A class of quantum statistical models, which utilizes antiunitary symmetries or, equivalently, real density matrices, offers compatible multiparameter estimations. The symmetries accompany the target-independent optimal measurements for pure-state models. Based on this finding, we propose methods to implement antiunitary symmetries for quantum metrology schemes. We further introduce a function which measures antiunitary asymmetry of quantum statistical models as a potential tool to characterize quantumness of phase transitions.
Do Animation Direction and Position of Progress Bar Affect Selections?

Web sites and applications show visual feedback such as a progress bar or a throbber to signal that users should wait. These moving objects guide the eye. We hypothesized that users would end up looking near the end position of the visual feedback after waiting. As a result that users might be more likely to select an object near the end position of the visual feedback. This study focused on using a progress bar as visual feedback for waiting and on the design to influence the users’ subsequent choice. We tested our hypotheses using a progress bar with different display positions and animation directions. The results suggest that changing the progress bar’s position on a display or the animation’s direction may bias the subsequent selection position.
A machine learning-based malicious bot detection framework for trend-centric twitter stream

There is an increase in evidence about the generation of social media content by autonomous entities called bots. These automated entities may be computerized algorithms or a human-assisted account to boost a trend or control political campaigns. People do not create bots due to malicious intent always. Several bots are benign and often play helpful roles. Detection of these bots have many implications. Most techniques available to date perform bot detection at the account level. This paper aims to uncover social bots that are active in trending events on Twitter. We have designed an automated malicious bot detection framework to serve this purpose. Our main contributions include converting unlabelled Twitter extracted trend-centric dataset to labelled dataset using semi-supervised machine learning techniques. Selection of attributes useful for bot classification using Entropy and Information Gain. Formulation of rule hierarchy after feature selection and threshold estimation of respective features. We have finalized the suspected bots after applying the rule hierarchically on the initial dataset. In addition to this, validation of the alleged bots given by the proposed framework is performed by plotting timeline tweets of suspected users, acts as an enhancement. This model is useful for bot detection in trend-centric Twitter stream. The research carried out will serve as an advancement to the successful detection of bots in trend-centric datasets.