Reviewerly/exHarmony huggingface dataset:
Hyperparameter Estimation in Bayesian MAP Estimation: Parameterizations and Consistency

The Bayesian formulation of inverse problems is attractive for three primary reasons: it provides a clear modelling framework; it allows for principled learning of hyperparameters; and it can provide uncertainty quantification.The posterior distribution may in principle be sampled by means of MCMC or SMC methods, but for many problems it is computationally infeasible to do so.In this situation maximum a posteriori (MAP) estimators are often sought.Whilst these are relatively cheap to compute, and have an attractive variational formulation, a key drawback is their lack of invariance under change of parameterization; it is important to study MAP estimators, however, because they provide a link with classical optimization approaches to inverse problems and the Bayesian link may be used to improve upon classical optimization approaches.The lack of invariance of MAP estimators under change of parameterization is a particularly significant issue when hierarchical priors are employed to learn hyperparameters.In this paper we study the effect of the choice of parameterization on MAP estimators when a conditionally Gaussian hierarchical prior distribution is employed.Specifically we consider the centred parameterization, the natural parameterization in which the unknown state is solved for directly, and the noncentred parameterization, which works with a whitened Gaussian as the unknown state variable, and arises naturally when considering dimension-robust MCMC algorithms; MAP estimation is well-defined in the nonparametric setting only for the noncentred parameterization.However, we show that MAP estimates based on the noncentred parameterization are not consistent as estimators of hyperparameters; conversely, we show that limits of finite-dimensional centred MAP estimators are consistent as the dimension tends to infinity.We also consider empirical Bayesian hyperparameter estimation, show consistency of these estimates, and demonstrate that they are more robust with respect to noise than centred MAP estimates.An underpinning concept throughout is that hyperparameters may only be recovered up to measure equivalence, a well-known phenomenon in the context of the Ornstein-Uhlenbeck process.The applicability of the results is demonstrated concretely with the study of hierarchical Whittle-Matérn and ARD priors.
A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning

Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work explores the feasibility of using the D-Wave as a sampler for a machine learning task. We describe a hybrid method that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave for image generation. Our method overcomes two key limitations in the 2000-qubit D-Wave processor, namely the limited number of qubits available to accommodate typical problem sizes for fully connected quantum objective functions, and samples that are binary pixel representations. As a consequence of these limitations we are able to show how we achieved nearly a 22-fold compression factor of grayscale 28 x 28 sized images to binary 6 x 6 sized images with a lossy recovery of the original 28 x 28 grayscale images. We further show how generating samples from the D-Wave after training the RBM, resulted in 28 x 28 images that were variations of the original input data distribution, as opposed to recreating the training samples. We evaluated the quality of this method by using a downstream classification method. We formulated a MNIST classification problem using a deep convolutional neural network that used samples from the quantum RBM to train the MNIST classifier and compared the results with a MNIST classifier trained with the original MNIST training data set, as well as a MNIST classifier trained using classical RBM samples. We also explored using a secondary dataset, the MNIST Fashion dataset and demonstrate the first quantum-generated fashion. Our hybrid autoencoder approach indicates advantage for RBM results relative to the use of a current RBM classical computer implementation for image-based machine learning and even more promising results for the next generation D-Wave quantum system. Our method for compression and image mappings is not constrained to RBMs, the autoencoder part of this method could be coupled with other quantum-based algorithms.
Temporal Properties of Cyberbullying on Instagram

Concurrent with the growth and widespread use of social networking platforms has been a rise in the prevalence of cyberbullying and cyberharassment, particularly among youth. Although cyberbullying is frequently defined as hostile communication or interactions that occur repetitively via electronic media, little is known about the temporal aspects of cyberbullying on social media, such as how the number, frequency, and timing of posts may vary systematically between cyberbullying and non-cyberbullying social media sessions. In this paper, we aim to contribute to the understanding of temporal properties of cyberbullying through the analysis of Instagram data. That is, the paper presents key temporal characteristics of cyberbullying and trends obtained from descriptive and burst analysis tasks. Our results have the potential to inform the development of more effective cyberbullying detection models.
DIAGNOSIS OF CORONAVIRUS DISEASE 2019 (COVID-19) SURVEILLANCE USING C4.5 ALGORITHM

Coronavirus Disease 2019 (COVID-19) has become a pandemic in Indonesia as a non-natural disaster in the form of disease outbreaks which must be undertaken as a response. The Ministry of Health in the Republic of Indonesia published a guidebook for prevention and control of COVID-19 in its response efforts. This guideline is intended for health officials as a reference in preparing for COVID-19. This handbook contains early detection and response activities to identify conditions of PDP, ODP, OTG, or confirmed cases of COVID-19. The efforts made are adjusted to the world situation progress from COVID-19 which is monitored by the World Health Organization (WHO). From the results of documentation studies that have been carried out on the COVID-19 pandemic in Indonesia, there are several problems that must be resolved from the prevention of the disease outbreak COVID-19. Lack of knowledge and awareness of the general public in the prevention and control of COVID-19 is one of the factors increasing the spread of that virus in Indonesia. Furthermore, there are difficulties in carrying out surveillance, early detection, contact tracing, infection prevention or control, and risk communication or people empowerment. This is due to the lack of implementation and testing on artificial intelligence methods for COVID-19 diagnosis that can be used by the public. The purpose of this research is to make a diagnosis of surveillance classification which includes PDP, ODP, and OTG using the C4.5 algorithm. The results showed that the diagnosis of the COVID-19 surveillance category using the C4.5 algorithm was successfully modeled into a decision tree with PDP, ODP, and OTG classification. The testing process in a confusion matrix with 3 (three) classes produces an accuracy rate of 92.86% which is included in the excellent classification category.
GLStyleNet: exquisite style transfer combining global and local pyramid features

Recent studies using deep neural networks have shown remarkable success in style transfer, especially for artistic and photo‐realistic images. However, these methods cannot solve more sophisticated problems. The approaches using global statistics fail to capture small, intricate textures and maintain correct texture scales of the artworks, and the others based on local patches are defective on global effect. To address these issues, this study presents a unified model [global and local style network (GLStyleNet)] to achieve exquisite style transfer with higher quality. Specifically, a simple yet effective perceptual loss is proposed to consider the information of global semantic‐level structure, local patch‐level style, and global channel‐level effect at the same time. This could help transfer not just large‐scale, obvious style cues but also subtle, exquisite ones, and dramatically improve the quality of style transfer. Besides, the authors introduce a novel deep pyramid feature fusion module to provide a more flexible style expression and a more efficient transfer process. This could help retain both high‐frequency pixel information and low‐frequency construct information. They demonstrate the effectiveness and superiority of their approach on numerous style transfer tasks, especially the Chinese ancient painting style transfer. Experimental results indicate that their unified approach improves image style transfer quality over previous state‐of‐the‐art methods.
Multidimensional Entanglement Generation with Multicore Optical Fibers

Quantum communication will undoubtedly be deployed in telecommunication networks of the future. The current development in classical communication is motivated by the rapidly approaching limit of existing optical fibers, known as the ``capacity crunch''. A promising solution is $s\phantom{\rule{0}{0ex}}p\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}e\ensuremath{-}d\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}v\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}s\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}n$ $m\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}l\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}p\phantom{\rule{0}{0ex}}l\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}x\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}g$, in which using the spatial modes supported in modern optical fibers increases their information capacity. The authors exploit related technologies to provide a source of entangled photons compatible with this latest trend in classical communication, which may serve as a standard for the next generation of entanglement-based quantum communication systems.
Entanglement Quantification of Correlated Photons Generated by Three-Level Laser with Parametric Amplifier and Coupled to a Two-Mode Vacuum Reservoir

In this paper, the detailed inseparability criteria of entanglement quantification of correlated two-mode light generated by a three-level laser with a coherently driven parametric amplifier and coupled to a two-mode vacuum reservoir is thoroughly analyzed. Using the master equation, we obtain the stochastic differential equation and the correlation properties of the noise forces associated with the normal ordering. Next, we study the squeezing and the photon entanglement by considering different inseparability criteria. The various criteria of entanglement used in this paper show that the light generated by the quantum optical system is entangled and the amount of entanglement is amplified by introducing the parametric amplifier into the laser cavity and manipulating the linear gain coefficient.
JDI-T: Jointly trained Duration Informed Transformer for Text-To-Speech without Explicit Alignment

We propose Jointly trained Duration Informed Transformer (JDI-T), a feed-forward Transformer with a duration predictor jointly trained without explicit alignments in order to generate an acoustic feature sequence from an input text. In this work, inspired by the recent success of the duration informed networks such as FastSpeech and DurIAN, we further simplify its sequential, two-stage training pipeline to a single-stage training. Specifically, we extract the phoneme duration from the autoregressive Transformer on the fly during the joint training instead of pretraining the autoregressive model and using it as a phoneme duration extractor. To our best knowledge, it is the first implementation to jointly train the feed-forward Transformer without relying on a pre-trained phoneme duration extractor in a single training pipeline. We evaluate the effectiveness of the proposed model on the publicly available Korean Single speaker Speech (KSS) dataset compared to the baseline text-to-speech (TTS) models trained by ESPnet-TTS.
Tensor Field Graph-Cut for Image Segmentation: A Non-Convex Perspective

Image segmentation is a key component of image analysis, which refers to the process of partitioning the image into multiple segments. Graph cut is widely used in image segmentation by constructing a graph that the minimal cut of this graph would lead to partition the corresponding pixels of the different objects. In this paper, we reconstruct the graph cut problem as a special non-convex optimization problem instead of the traditional maximum flow problem. We extend this non-convex problem to the hypergraph method and combine it with a tensor field based on a directional bilateral filter bank to achieve segmentation in grayscale images. Accordingly, an efficient minimization algorithm is proposed to solve this non-convex problem with global convergence. Furthermore, we have selected the data of BSDS300 and BSDS500 as tests. Experimental results and evaluation index tests further demonstrate the superiority of the proposed method.
Atmospheric Effects on Satellite-to-Ground Quantum Key Distribution using Coherent States

Satellite-based quantum cryptography has already been demonstrated using discrete variable technology. Nonetheless, there is great interest in using weak coherent pulses to perform quantum key distribution (QKD) in the continuous variable (CV) paradigm. In this work, we study the feasibility of performing coherent-state CV-QKD via the satellite-to-ground channel. We use numerical methods to simulate atmospheric turbulence and compare the results with ground-based experimental data so as to confirm the validity of our approach. We find the results obtained from the numerical simulations agree well with the experimental data and represent an improvement over the state-of-the-art analytical models. Using the simulation results we then derive QKD key rates and find that useful non-zero key rates can be found over a limited range of zenith angles. Determination of QKD key rates using experimentally validated simulations of low-zenith-angle atmospheric channels represents an important step towards proving the feasibility of real-world satellite-to-Earth CV-QKD.
Using virtual worlds as a platform for collaborative meetings in healthcare: a feasibility study

Abstract Background Healthcare teams often consist of geographically dispersed members. Virtual worlds can support immersive, high-quality, multimedia interaction between remote individuals; this study investigated use of virtual worlds to support remote healthcare quality improvement team meetings. Methods Twenty individuals (12 female, aged 25–67 [ M = 42.3, SD = 11.8]) from 6 healthcare quality improvement teams conducted collaborative tasks in virtual world or face-to-face settings. Quality of collaborative task performances were measured and questionnaires and interviews were used to record participants’ experiences of conducting the tasks and using the virtual world software. Results Quality of collaborative task outcomes was high in both face-to-face and virtual world settings. Participant interviews elicited advantages for using virtual worlds in healthcare settings, including the ability of the virtual environment to support tools that cannot be represented in equivalent face-to-face meetings, and the potential for virtual world settings to cause improvements in group-dynamics. Reported disadvantages for future virtual world use in healthcare included the difficulty that people with weaker computer skills may experience with using the software. Participants tended to feel absorbed in the collaborative task they conducted within the virtual world, but did not experience the virtual environment as being ‘real’. Conclusions Virtual worlds can provide an effective platform for collaborative meetings in healthcare quality improvement, but provision of support to those with weaker computer skills should be ensured, as should the technical reliability of the virtual world being used. Future research could investigate use of virtual worlds in other healthcare settings.
Hand-Crafted Features vs Deep Learning for Pedestrian Detection in Moving Camera

Detecting pedestrians and other objects in images taken from moving platforms is an essential task needed for many applications such as smart surveillance systems and intelligent transportation systems.However, most detectors in this domain still rely on handcrafted features to separate the foreground objects from the background.While these types of methods have presented good results with good response times, they still have some weaknesses to overcome.In recent years, alternative object detection methods are being proposed, with deep learning based approaches rising in popularity thanks to their promising results.In this paper, we propose two pedestrian detectors for use in images taken from a moving vehicle: The first detector uses a block matching algorithm and handcraft features for pedestrian detection, and the second uses a Faster R-CNN deep detector.We also compare both systems' performances to other state-of-the-art pedestrian detectors.Our results show that although handcraft feature-based approach achieves good results within acceptable detection times, it suffers from a high false positive rate.However, we found that Faster R-CNN detector performs better in terms of precision and recall, but these improved results come at a cost of detection time.
Portrait style transfer using deep convolutional neural networks and facial segmentation

When standard neural style transfer approaches are used in portrait style transfer, they often inappropriately apply textures and colours in different regions of the style portraits to the content portraits, leading to unsatisfied transfer results. This paper presents a portrait style transfer method to transfer the style of one image to another. It first proposes a combined segmentation method for the portrait parts, which segments both the style portrait and the content portrait into masks of seven parts automatically, including background, face, eyes, nose, eyebrows, mouth and foreground. These masks are extracted to capture elements of the styles for objects in the style image and to preserve the structure in the content portrait. This paper then proposes an augmented deep Convolutional Neural Network (CNN) framework for portrait style transfer. The masks of seven parts are added into a trained deep convolutional neural network as feature maps in certain selected layers in the augmented deep CNN model. An improved loss function is proposed for the training of the portrait style transfer. Results on various images show that our method outperforms the state-of-the-art style transfer techniques.
On Modeling the Physical World as a Collection of Things: The W3C Thing Description Ontology

This document presents the Thing Description ontology, an axiomatization of the W3C Thing Description model. It also introduces an alignment with the Semantic Sensor Network ontology and evaluates how this alignment contributes to semantic interoperability in the Web of Things.
Robustness of Eye Movement Biometrics Against Varying Stimuli and Varying Trajectory Length

Recent results suggest that biometric identification based on human's eye movement characteristics can be used for authentication. In this paper, we present three new methods and benchmark them against the state-of-the-art. The best of our new methods improves the state-of-the-art performance by 5.2 percentage points. Furthermore, we investigate some of the factors that affect the robustness of the recognition rate of different classifiers on gaze trajectories, such as the type of stimulus and the tracking trajectory length. We find that the state-of-the-art method only works well when using the same stimulus for testing that was used for training. By contrast, our novel method more than doubles the identification accuracy for these transfer cases. Furthermore, we find that with only 90 seconds of eye tracking data, 86.7% accuracy can be achieved.
Counterfeit Currency Detection using Deep Convolutional Neural Network

Counterfeit money refers to fake or imitation currency that is produced with an idea to deceive. According to recent reports, demonetization led to all-time high inflow of fake notes into banks, resulting in a spike in suspicious transactions. The existing works to detect a counterfeit note are mostly based on image processing techniques. This paper deals with Deep Learning in which a convolution neural network(CNN) model is built with a motive to identify a counterfeit note on handy devices like smart phones, tablets. The model built was trained and tested on a self-generated dataset. Images are acquired using the smart phone camera and fed to the CNN network. The results obtained are encouraging and can be improvised by further research and improvements in the architecture of Deep CNN model. The testing accuracy obtained is about 85.6%, training and the validation accuracy were 98.57% and 96.55% respectively.
Evaluation of Key Security Issues Associated with Mobile Money Systems in Uganda

Smartphone technology has improved access to mobile money services (MMS) and successful mobile money deployment has brought massive benefits to the unbanked population in both rural and urban areas of Uganda. Despite its enormous benefits, embracing the usage and acceptance of mobile money has mostly been low due to security issues and challenges associated with the system. As a result, there is a need to carry out a survey to evaluate the key security issues associated with mobile money systems in Uganda. The study employed a descriptive research design, and stratified random sampling technique to group the population. Krejcie and Morgan’s formula was used to determine the sample size for the study. The collection of data was through the administration of structured questionnaires, where 741 were filled by registered mobile money (MM) users, 447 registered MM agents, and 52 mobile network operators’ (MNOs) IT officers of the mobile money service providers (MMSPs) in Uganda. The collected data were analyzed using RStudio software. Statistical techniques like descriptive analysis and Pearson Chi-Square test was used in data analysis and mean (M) &gt; 3.0 and p-value &lt; 0.05 were considered statistically significant. The findings revealed that the key security issues are identity theft, authentication attack, phishing attack, vishing attack, SMiShing attack, personal identification number (PIN) sharing, and agent-driven fraud. Based on these findings, the use of better access controls, customer awareness campaigns, agent training on acceptable practices, strict measures against fraudsters, high-value transaction monitoring by the service providers, developing a comprehensive legal document to run mobile money service, were some of the proposed mitigation measures. This study, therefore, provides a baseline survey to help MNO and the government that would wish to implement secure mobile money systems.