Reviewerly/exHarmony huggingface dataset:
Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution

Graph convolutional networks (GCNs) have achieved great success on dealing with data of non-Euclidean structures. Their success directly attribute to effective fitting graph structures to data such as in social media and knowledge databases. For image processing applications, the use of graph structures and GCNs have not been fully explored. In this paper, we propose a novel encoder-decoder network with added graph convolutions by converting feature maps to vertexes of a pre-generated graph to synthetically construct graph-structured data. By doing this, we inexplicitly apply graph Laplacian regularization to the feature maps, making them more structured. The experiments show it significantly boosts performance for the task of image restoration, including deblurring and super-resolution. We believe that it opens up opportunities for GCN-based approaches in many applications.
Factors Affecting the Organizational Adoption of Secure Community Cloud in KSA

Cloud computing is an innovative technology and gaining popularity in variety of organization to meet their computing requirements. Saudi Arabia is considered as the Gulf region’s high economy and the future market for cloud computing technologies. Analysis of its economic benefits, adequacy from technical, organizational, and environmental viewpoints, and possible challenges should precede the implementation of new technologies. Latest research and developments on cloud computing are causally linked to the emphasis on applications, advantages, expenditures, and technology. The community cloud (CC) is an impressive replacement for different organizations as it promotes cost reduction and quality enhancement. However, this research was performed to study the perception of IT and telecommunications company employees and computer users who support community cloud. The current state of community cloud adoption in KSA is analyzed. Present challenges impact the adoption of community cloud in Saudi Arabia and define the motivation factors. Questionnaires were used to accomplish these goals. Questionnaires were designed to accomplish these goals and it is implemented using survey. Questions about confidence are also a key issue. Issues such as data privacy, integrity, and availability are the factors influencing the organization’s acceptance of community cloud.
Quantum state smoothing as an optimal Bayesian estimation problem with three different cost functions

Quantum state smoothing is a technique to estimate an unknown true state of an open quantum system based on partial measurement information both prior and posterior to the time of interest. In this paper, we show that the smoothed quantum state is an optimal Bayesian state estimator, that is, it minimizes a Bayesian expected cost function. Specifically, we show that the smoothed quantum state is optimal with respect to two cost functions: the trace-square deviation from and the relative entropy to the unknown true state. However, when we consider a related cost function, the linear infidelity, we find, contrary to what one might expect, that the smoothed state is not optimal. For this case, we derive the optimal state estimator, which we call the lustrated smoothed state. It is a pure state, the eigenstate of the smoothed quantum state with the largest eigenvalue. We illustrate these estimates with a simple system, the driven, damped two-level atom.
Efficient ESPI method to identify vibration characteristics of transparent films

Electronic speckle pattern interferometry (ESPI) has been frequently used to study vibration characteristics of engineering structures. However, efficient ESPI methods for identifying the vibration characteristics of transparent membranes are rarely reported. A convenient approach to determine the resonant frequencies and the vibration modes of transparent films is proposed. A quasi-common-path ESPI (QCP-ESPI) is developed to detect out-of-plane deformation and visualize vibrational modes of a vibrating transparent diaphragm. An amplitude-modulation-based method is proposed for quick and accurate determining resonance of oscillating objects during frequency scanning. Experimental performances demonstrate the feasibility and efficiency of the QCP-ESPI and amplitude-modulation-based methods for determining the resonant frequency and vibration modes of transparent objects.
An Equal Data Setting for Attention-Based Encoder-Decoder and HMM/DNN Models: A Case Study in Finnish ASR

Standard end-to-end training of attention-based ASR models only uses transcribed speech. If they are compared to HMM/DNN systems, which additionally leverage a large corpus of text-only data and expert-crafted lexica, the differences in modeling cannot be disentangled from differences in data. We propose an experimental setup, where only transcribed speech is used to train both model types. To highlight the difference that text-only data can make, we use Finnish, where an expert-crafted lexicon is not needed. With 1500h equal data, we find that both ASR paradigms perform similarly, but adding text data quickly improves the HMM/DNN system. On a smaller 160h subset we find that HMM/DNN models outperform AED models.
Weather forecasting based on data-driven and physics-informed reservoir computing models

In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behavior of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the outperformance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.
Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.
RHOASo: An Early Stop Hyper-Parameter Optimization Algorithm

This work proposes a new algorithm for optimizing hyper-parameters of a machine learning algorithm, RHOASo, based on conditional optimization of concave asymptotic functions. A comparative analysis of the algorithm is presented, giving particular emphasis to two important properties: the capability of the algorithm to work efficiently with a small part of a dataset and to finish the tuning process automatically, that is, without making explicit, by the user, the number of iterations that the algorithm must perform. Statistical analyses over 16 public benchmark datasets comparing the performance of seven hyper-parameter optimization algorithms with RHOASo were carried out. The efficiency of RHOASo presents the positive statistically significant differences concerning the other hyper-parameter optimization algorithms considered in the experiments. Furthermore, it is shown that, on average, the algorithm needs around 70% of the iterations needed by other algorithms to achieve competitive performance. The results show that the algorithm presents significant stability regarding the size of the used dataset partition.
OSSR-PID: One-Shot Symbol Recognition in P&amp;ID Sheets using Path Sampling and GCN

In this paper, we focus on recognition of line-drawn symbols in engineering drawings with only one prototypical example per symbol available for training. In particular, Piping and Instrumentation Diagrams (P&ID) are ubiquitous in several manufacturing, oil and gas enterprises for representing engineering schematics and equipment layout. There is an urgent need to extract and digitize information from P&IDs without the cost of annotating a varying set of symbols for each new use case. A robust one-shot learning approach for symbol recognition i.e., localization followed by classification, would therefore go a long way towards this goal. Our method works by sampling pixels sequentially along the different contour boundaries in the image. These sampled points form paths which are used in the prototypical line diagram to construct a graph that captures the structure of the contours. Subsequently, the prototypical graphs are fed into a Dynamic Graph Convolutional Neural Network (DGCNN) which is trained to classify graphs into one of the given symbol classes. Further, we append embeddings from a Resnet-34 network which is trained on symbol images containing sampled points to make the classification network more robust. Since, many symbols in P&ID are structurally very similar to each other, we utilize Arcface loss during DGCNN training which helps in maximizing symbol class separability by producing highly discriminative embeddings. During inference time, a similar line based sampling procedure is adopted for generating sampled points across P&ID image. The images consist of components attached on the pipeline (straight line). The sampled points segregated around the symbol regions are used for the classification task. The proposed pipeline, named OSSR-PID, is fast and gives outstanding performance for recognition of symbols on a synthetic dataset of 100 P&ID diagrams. We also compare our method against prior-work that uses full supervision (not one-shot) on a real-world private dataset of 12 P&ID sheets and obtain comparable/superior results. Remarkably, it is able to achieve such excellent performance using only one prototypical example per symbol.
Synthesis Speech Based Data Augmentation for Low Resource Children ASR

Successful speech recognition for children requires large training data with sufficient speaker variability. The collection of such a training database of children’s voices is challenging and very expensive for zero/low resource language like Punjabi. In this paper, the data scarcity issue of the low resourced language Punjabi is addressed through two levels of augmentation. The original training corpus is first augmented by modifying the prosody parameters for pitch and speaking rate. Our results show that the augmentation improves the system performance over the baseline system. Then the augmented data combined with original data and used to train the TTS system to generate synthesis data and extended dataset is further used for augmented by generating children’s utterances using text-to-speech synthesis and sampling the language model with methods that increase the acoustic and lexical diversity. The final speech recognition performance indicates a relative improvement of 50.10% with acoustic and 57.40% with language diversity based augmentation in comparison to that of the baseline system respectively.
ISPY: Automatic Issue-Solution Pair Extraction from Community Live Chats

Collaborative live chats are gaining popularity as a development communication tool. In community live chatting, developers are likely to post issues they encountered (e.g., setup issues and compile issues), and other developers respond with possible solutions. Therefore, community live chats contain rich sets of information for reported issues and their corresponding solutions, which can be quite useful for knowledge sharing and future reuse if extracted and restored in time. However, it remains challenging to accurately mine such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the problem of issue-solution pair extraction from developer live chat data, and propose an automated approach, named ISPY, based on natural language processing and deep learning techniques with customized enhancements, to address the problem. Specifically, ISPY automates three tasks: 1) Disentangle live chat logs, employing a feedforward neural network to disentangle a conversation history into separate dialogs automatically; 2) Detect dialogs discussing issues, using a novel convolutional neural network (CNN), which consists of a BERT-based utterance embedding layer, a context-aware dialog embedding layer, and an output layer; 3) Extract appropriate utterances and combine them as corresponding solutions, based on the same CNN structure but with different feeding inputs. To evaluate ISPY, we compare it with six baselines, utilizing a dataset with 750 dialogs including 171 issue-solution pairs and evaluate ISPY from eight open source communities. The results show that, for issue-detection, our approach achieves the F1 of 76%, and outperforms all baselines by 30%. Our approach achieves the F1 of 63% for solution-extraction and outperforms the baselines by 20%. Furthermore, we apply ISPY on three new communities to extensively evaluate ISPY&#x2019;s practical usage. Moreover, we publish over 30K issue-solution pairs extracted from 11 communities. We believe that ISPY can facilitate community-based software development by promoting knowledge sharing and shortening the issue-resolving process.
Rating Prediction With Review Network Feedback: A New Direction in Recommendation

Recommendation systems usually make a personalized recommendation with explicit feedback (i.e., ratings, reviews, and description on products) or implicit feedback (i.e., searching activity, clicking products, and viewing products). Implicit feedback indicates a customer’s preferences, and explicit feedback indicates the satisfaction level from the purchased products. All these feedbacks are direct and generated by individual customers. Interpersonal relations or interactions among customers can be regarded as indirect feedback that can influence customers greatly. We introduce a new class of feedback named review network feedback to bridge this gap. This feedback is based on the concept of a review network where customers are nodes and their interaction in terms of reviewing the same product creates the edges. Review network feedback embodied the reliability and positional influence of customers in their review network. Extensive experiments on Amazon.com online review datasets establish the superiority of our model over popular baselines when we consider review network feedback.
Decorrelated Debiased Converted Measurement Kalman Filter for Phased Array Radar Tracking System

In this article, the target tracking of phased array radars with measurements specified in the direction cosine coordinate system is considered. The converted measurement Kalman filter is a widely adopted technique since it allows tracking in the coordinate system in which the dynamics of targets are described. The converted measurement Kalman filter has two types of inherent bias: conversion and estimation biases. The compensation of conversion bias has been examined by earlier studies using the debiasing technique in the presence of estimation bias. This article, therefore, introduces decorrelated debiased converted measurement Kalman filter (DDCMKF) in the direction cosine coordinate system to relieve the estimation bias. The decorrelation between the Kalman gain and measurement residual by applying conditioning on predicted position estimates is first introduced. The range-rate measurement is subsequently incorporated as a pseudo-measurement form after the derivation of decorrelated converted position. Finally, a sequential DDCMKF in direction cosine coordinates is developed using the derived converted position and range-rate measurement models. Adopting sequential filtering for incorporating the range-rate measurement reduces computation complexity and alleviates its strong nonlinearity. Numerical simulations are conducted to evaluate the performance of DDCMKF against other types of tracking filters in the direction cosine coordinate system. Simulation results show that the proposed DDCMKF achieves preferable error performance while maintaining consistency, especially in short-range tracking with relatively large direction cosine errors.
Traditional Mask Augmented Reality Application

The industrial revolution 4.0 has become a challenge for various sectors in mastering information technology, one of which is the arts and culture sector. Cultural arts that are quite widely spread and developed in Indonesia are traditional masks. Traditional masks are one of the oldest and most beautiful cultures in Indonesia. However, with the development of the era to the digital world in the era of the industrial revolution 4.0, this beloved culture is fading due to the entry of foreign cultures and technological developments. Many young people who succeed the nation do not understand this cultural art, namely traditional masks. So those cultural arts such as traditional masks can still keep up with the development of digital technology in industry 4.0, we conduct research to use technology to preserve this traditional mask culture. The research uses the ADDIE method starting with Analyze, Design, Develop, Implement, and Evaluate. We took some examples of traditional masks such as Malangan masks, Cirebon masks, and Panji masks from several regions in Indonesia. This research implements marker-based Augmented reality technology and makes a traditional mask book that can be a means of augmented reality.
Technical Debt Forecasting Based on Deep Learning Techniques

Technical debt (TD) is a metaphor commonly used to reflect the consequences of quality compromises that can derive short-term benefits but may result in quality decay of software products in the long run. While a broad variety of methods and tools have been proposed over the years for the identification and quantification of TD during the software development cycle, it is not until recently that researchers have turned their interest towards methods aiming to forecast the future TD evolution of a software project. Predicting the future value of TD could facilitate decision-making tasks regarding software maintenance and assist developers and project managers in taking proactive actions regarding TD repayment. In previous relevant studies, time series analysis and Machine Learning techniques have been employed in order to generate meaningful TD forecasts. While these approaches have been proven capable of producing reliable TD predictions, their predictive performance has been observed to decrease significantly for long-term predictions. To this end, in the present paper we investigate whether the adoption of Deep Learning may lead to more accurate long-term TD prediction. For this purpose, Deep Learning models are constructed, evaluated, and compared based on a dataset of five popular real-world software applications. The results of our analysis indicate that the adoption of Deep Learning results in TD forecasting models with sufficient predictive performance up to 150 steps ahead into the future.
Phase-matching quantum key distribution based on pulse-position modulation

In order to further improve the performance of the phase-matching quantum key distribution (PM-QKD) protocol, in this paper, a more efficient protocol named PPM-PM-QKD is proposed which adopts pulse-position modulation (PPM) to realize the high-dimensional encoding. PPM technology utilizes the empty pulses in the weak coherent source, and can improve the performance of the protocol. The simulation results show the improvement compared with the original PM-QKD protocol. Moreover, the protocol inherits the advantages of the original PM-QKD protocol, and it can not only exceed the PLOB bound, but also has a higher robust to noise and channel disturbance.
Do individual characteristics influence the beta-elliptic modeling errors during ellipse drawing movements?

This paper investigates whether age, gender, and degree of familiarity with writing have an influence on the Beta-elliptic model errors during hand-drawing on a graphical tablet. A database of elliptical hand drawing movements was built within a sample of 99 participants aged between 19 and 85 years. Using the Beta-elliptic model, the velocity profile was modeled by overlapped Beta functions and the drawing trajectory was segmented between velocity extrema and each segment geometry was modeled by elliptic arcs. Average absolute and relative geometric, curvature and curvilinear velocity errors were 0.27 mm, 0.68%, 4.54 mm, 0.48%, 4.68 mm/s, and 8.79% respectively. Statistical analyses revealed not significant or low correlation between modeling errors and age and movement velocity, and no significant or low error differences according to gender or degree of familiarity with writing.
An Enhanced Photonic Quantum Finite Automaton

In a recent paper we have described an optical implementation of a measure-once one-way quantum finite automaton recognizing a well-known family of unary periodic languages, accepting words not in the language with a given error probability. To process input words, the automaton exploits the degree of polarization of single photons and, to reduce the acceptance error probability, a technique of confidence amplification using the photon counts is implemented. In this paper, we show that the performance of this automaton may be further improved by using strategies that suitably consider both the orthogonal output polarizations of the photon. In our analysis, we also take into account how detector dark counts may affect the performance of the automaton.