Reviewerly/exHarmony huggingface dataset:
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

We present an extremely simple Ultra-Resolution Style Transfer framework, termed URST, to flexibly process arbitrary high-resolution images (e.g., 10000x10000 pixels) style transfer for the first time. Most of the existing state-of-the-art methods would fall short due to massive memory cost and small stroke size when processing ultra-high resolution images. URST completely avoids the memory problem caused by ultra-high resolution images by (1) dividing the image into small patches and (2) performing patch-wise style transfer with a novel Thumbnail Instance Normalization (TIN). Specifically, TIN can extract thumbnail features' normalization statistics and apply them to small patches, ensuring the style consistency among different patches. Overall, the URST framework has three merits compared to prior arts. (1) We divide input image into small patches and adopt TIN, successfully transferring image style with arbitrary high-resolution. (2) Experiments show that our URST surpasses existing SOTA methods on ultra-high resolution images benefiting from the effectiveness of the proposed stroke perceptual loss in enlarging the stroke size. (3) Our URST can be easily plugged into most existing style transfer methods and directly improve their performance even without training. Code is available at https://git.io/URST.
Tri-level handwritten text segmentation techniques for Gujarati language

Objectives: To improve the efficiency of tri-level segmentation tasks for handwritten Gujarati text.Methods: Using hybrid methods for tri-level segmentation, we have used line, word and character segmentation from the image.This study presents a segmentation paradigm that works with touching characters, slop of the line written on the page, character overlapping, etc.It evaluated on the dataset of 500+ images created by us on different writing sentences by different people.We have used the Horizontal projection technique for line segmentation, Scale-space technique for word segmentation and the Vertical projection technique for character segmentation.Findings:The experimental results show that the proposed method is more efficient for handwritten Gujarati text with diacritics.We have obtained the accuracy for character level segmentation is 82%, word-level is 90% and for the line-level segmentation is 87%.Novelty: We have designed a methodology to segment Gujarati handwritten text with diacritics at all three levels including characters, words and lines.Applications: We have proposed tri-level segmentation which is pre-processing task that can be used in any character recognition systems i.e.OCR.
Suppression of background emission for efficient single-photon generation in micropillar cavities

We optimize the efficiency of a quantum-dot-based micropillar single-photon source by minimizing the spontaneous emission into unwanted background modes. We perform a numerical investigation of the background emission, where we identify a semi-periodic enhancement with the diameter. At these peaks of the background emission, the efficiency is reduced by ∼20%, and we show how this reduction can be avoided simply by choosing a diameter away from the peaks. Finally, we analyze the discrepancy between the Purcell factor estimated from experimental lifetime measurements and the true Purcell factor.
Adaptive Phase Correction for Phase Measuring Deflectometry Based on Light Field Modulation

The phase measuring deflectometry is a powerful measuring method for complex optical surfaces, which captures the reflected fringe images associated with a displaying screen and calculates the normal vectors of the surface under test (SUT) accordingly. The captured images are usually set conjugate to the SUT, which in turn makes the screen defocused. As a result, the blurring effect caused by the point spread function (PSF) of the off-axis catadioptric imaging system can bias the solved phases. In order to correct the phase errors, a light field is constructed based on the Fourier compressive sensing method to describe the light transmission between the screen and camera pixels. Fringe modulation is conducted to enhance the robustness against noise, and then, space-variant PSFs can be extracted from the light field. The true phases are obtained by solving a Wiener deconvolution problem, with the merit function adaptively regularized by adjusting the damping parameter. The proposed method can correct adaptively the phase errors induced by the complex aberrations. Compared to the reference measurements, the form accuracy can be improved by four times.
Spatiotemporal Mobility Based Trajectory Privacy-Preserving Algorithm in Location-Based Services

Recent years have seen the wide application of Location-Based Services (LBSs) in our daily life. Although users can enjoy many conveniences from the LBSs, they may lose their trajectory privacy when their location data are collected. Therefore, it is urgent to protect the user’s trajectory privacy while providing high quality services. Trajectory k-anonymity is one of the most important technologies to protect the user’s trajectory privacy. However, the user’s attributes are rarely considered when constructing the k-anonymity set. It results in that the user’s trajectories are especially vulnerable. To solve the problem, in this paper, a Spatiotemporal Mobility (SM) measurement is defined for calculating the relationship between the user’s attributes and the anonymity set. Furthermore, a trajectory graph is designed to model the relationship between trajectories. Based on the user’s attributes and the trajectory graph, the SM based trajectory privacy-preserving algorithm (MTPPA) is proposed. The optimal k-anonymity set is obtained by the simulated annealing algorithm. The experimental results show that the privacy disclosure probability of the anonymity set obtained by MTPPA is about 40% lower than those obtained by the existing algorithms while the same quality of services can be provided.
A New Interpretable Unsupervised Anomaly Detection Method Based on Residual Explanation

Despite the superior performance in modeling complex patterns to address challenging problems, the black-box nature of Deep Learning (DL) methods impose limitations to their application in real-world critical domains. The lack of a smooth manner for enabling human reasoning about the black-box decisions hinder any preventive action to unexpected events, in which may lead to catastrophic consequences. To tackle the unclearness from black-box models, interpretability became a fundamental requirement in DL-based systems, leveraging trust and knowledge by providing ways to understand the model’s behavior. Although a current hot topic, further advances are still needed to overcome the existing limitations of the current interpretability methods in unsupervised DL-based models for Anomaly Detection (AD). Autoencoders (AE) are the core of unsupervised DL-based for AD applications, achieving best-in-class performance. However, due to their hybrid aspect to obtain the results (by requiring additional calculations out of network), only agnostic interpretable methods can be applied to AE-based AD. These agnostic methods are computationally expensive to process a large number of parameters. In this paper, we present the RXP (Residual eXPlainer), a new interpretability method to deal with the limitations for AE-based AD in large-scale systems. It stands out for its implementation simplicity, low computational cost and deterministic behavior, in which explanations are obtained through the deviation analysis of reconstructed input features. In an experiment using data from a real heavy-haul railway line, the proposed method achieved superior performance compared to SHAP, demonstrating its potential to support decision making in large scale critical systems.
ESA☆: A generic framework for semi-supervised inductive learning

Semi-supervised learning is crucial in many applications where accessing class labels is unaffordable or costly. The most promising approaches are graph-based but they are transductive and they do not provide a generalized model working on inductive scenarios. To address this problem, we propose a generic framework, ESA☆, for inductive semi-supervised learning based on three components: an ensemble of semi-supervised autoencoders providing a new data representation that leverages the knowledge supplied by the reduced amount of available labels; a graph-based step that helps augmenting the training set with pseudo-labeled instances and, finally, a classifier trained with labeled and pseudo-labeled instances. Additionally, we also introduce two variants of our framework adopting different graph-based pseudo-labeling strategies: the first, ESALP, is based on a confidence-aware label propagation algorithm, while the second, ESAGAT, on a graph convolutional attention network. The experimental results show that our framework outperforms state-of-the-art inductive semi-supervised methods.
Probabilistic Spatial Distribution Prior Based Attentional Keypoints Matching Network

Keypoints matching is a pivotal component for many image-relevant applications such as image stitching, visual simultaneous localization and mapping (SLAM), and so on. Both handcrafted-based and recently emerged deep learning-based keypoints matching methods merely rely on keypoints and local features, while losing sight of other available sensors such as inertial measurement unit (IMU) in the above applications. In this paper, we demonstrate that the motion estimation from IMU integration can be used to exploit the spatial distribution prior of keypoints between images. To this end, a probabilistic perspective of attention formulation is proposed to integrate the spatial distribution prior into the attentional graph neural network naturally. With the assistance of spatial distribution prior, the effort of the network for modeling the hidden features can be reduced. Furthermore, we present a projection loss for the proposed keypoints matching network, which gives a smooth edge between matching and un-matching keypoints. Image matching experiments on visual SLAM datasets indicate the effectiveness and efficiency of the presented method.
Deep generative models in DataSHIELD

Abstract Background The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients. Methods The DataSHIELD software provides an infrastructure and a set of statistical methods for joint, privacy-preserving analyses of distributed data. The contained algorithms are reformulated to work with aggregated data from the participating sites instead of the individual data. If a desired algorithm is not implemented in DataSHIELD or cannot be reformulated in such a way, using artificial data is an alternative. Generating artificial data is possible using so-called generative models, which are able to capture the distribution of given data. Here, we employ deep Boltzmann machines (DBMs) as generative models. For the implementation, we use the package “BoltzmannMachines” from the Julia programming language and wrap it for use with DataSHIELD, which is based on R. Results We present a methodology together with a software implementation that builds on DataSHIELD to create artificial data that preserve complex patterns from distributed individual patient data. Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses. As an exemplary application, we conduct a distributed analysis with DBMs on a synthetic data set, which simulates genetic variant data. Patterns from the original data can be recovered in the artificial data using hierarchical clustering of the virtual patients, demonstrating the feasibility of the approach. Additionally, we compare DBMs, variational autoencoders, generative adversarial networks, and multivariate imputation as generative approaches by assessing the utility and disclosure of synthetic data generated from real genetic variant data in a distributed setting with data of a small sample size. Conclusions Our implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e.g., for pattern recognition with deep learning. This also demonstrates more generally how DataSHIELD can be flexibly extended with advanced algorithms from languages other than R.
Multi-Robot Sensor Fusion Target Tracking With Observation Constraints

In Mobile Robotics, visual tracking is an extremely important sub-problem.Some solutions found to reduce the problems arising from partial and total occlusion are the use of multiple robots.In this work, we propose a three-dimensional space target tracking based on a constrained multi-robot visual data fusion on the occurrence of partial and total occlusion.To validate our approach we first implemented a non-cooperative visual tracking where only the data from a single robot is used.Then, a cooperative visual tracking was tested, where the data from a team of robots is fused using a particle filter.To evaluate both approaches, a visual tracking environment with partial and total occlusions was created where the tracking was performed by a team of robots.The result of the experiment shows that the non-cooperative approach presented a lower computational cost than the cooperative approach but the inferred trajectory was impaired by the occlusions, a fact that did not occur in the cooperative approach due to the data fusion.
TAHAP DEFINISI DALAM FOUR-D MODEL PADA PENELITIAN RESEARCH &amp; DEVELOPMENT (R&amp;D) ALAT PERAGA EDUKASI ULAR TANGGA UNTUK MENINGKATKAN PENGETAHUAN SAINS DAN MATEMATIKA ANAK USIA 5-6 TAHUN

Educational play tools (APE) have become a necessity during AUD learning because they are full of educational materials that can be adapted to their growth. This development research aims to produce a product in the form of a ladder snake APE in improving the cognitive abilities of children aged 5-6 years in the fields of science and mathematics at RA Mafatihul Ulum, Serang City, Banten Province. This type of research involves using a number of methods and types of research and going through various stages of research sessions, so each session is carried out to achieve its respective objectives. The Four-D Model development model suggested by Sivasailam This model consists of 4 development stages, namely; Define (defining), Design (Design), Develop (development) and Disseminate (deployment). The definition stage offered in the 4D model consists of several stages of analysis, namely curriculum analysis, needs analysis, student analysis, concept analysis, task analysis and formulation of learning objectives
How preadolescents and adults remember and experience virtual reality: The role of avatar incarnation, emotion, and sense of presence

Incarnate bodies in the first-person view in virtual reality, especially with a head-mounted display, could impact cognitive processes relying on self-representation, such as memory, emotion, or sense of presence. The goal of this study is to investigate these effects by comparing adults' and preadolescents' behaviors. We manipulated incarnation by presenting the experience with or without an avatar (full-body avatar versus VR controllers), while manipulating emotion with presented stimuli. 40 adults and 32 preadolescents were both divided into two experimental groups (avatar versus no-avatar). Valence, arousal, and presence were self-evaluated, and a free recall task was performed. Physiological measurements were carried out to add objective support for our findings. We showed an emotional enhancement of memory in both age groups. Importantly, embodying an avatar permitted preadolescents to have the same memory performance than adults. Memory was predicted by valence and head movements in adults, but no predictors were found for preadolescents. Overall, preadolescents rated virtual environments as more arousing, more positive, and inducing more presence than adults. Surprisingly, avatar incarnation did not impact these measures, we propose that immersion at a sensorimotor level is a more crucial component than incarnation.
Density Gain-Rate Peaks for Spectral Clustering

Clustering has been troubled by varying shapes of sample distributions, such as line and spiral shapes.Spectral clustering and density peak clustering are two feasible techniques to address this problem, and have attracted much attention from academic community.However, spectral clustering still cannot well handle some shapes of sample distributions in the space of extracted features, and density peak clustering encounters performance problems because it cannot mine the local structures of data and well deal with non-uniform distributions.In order to solve above problems, we propose the density gain-rate peak clustering (DGPC), a new type of density peak clustering method, and then embed it in spectral clustering for performance promotion.Firstly, in order to well handle non-uniform sample distributions, we propose density gain-rate for density peak clustering.Density gain-rate is based on the assumption that the density of a clustering center will be higher with the reduce of the radius.Even under non-uniform distributions, the cluster center in low density region will still have a significant density gain-rate thus can be detected.We combine density gain-rate in density peak clustering to construct DGPC method.Then in the framework of spectral clustering, we use our new density peak clustering to cluster the samples by their extracted features from a similarity graph of these samples, such as the neighbor-based similarity graph or the self-expressiveness similarity graph.Compared with the previous spectral clustering and density peak clustering, our method leads to better clustering performances on varying shapes of sample distributions.The experiment measures the performances of our clustering method and existing clustering methods by NMI and ACC on seven real-world datasets to illustrate the effectiveness of our method.
A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers

The energy demand of data centers is increasing globally with the increasing demand for computational resources to ensure the quality of services.It is important to quantify the required resources to comply with the computational workloads at the rack-level.In this paper, a novel reliability index called loss of workload probability is presented to quantify the rack-level computational resource adequacy.The index defines the right-sizing of the rack-level computational resources that comply with the computational workloads, and the desired reliability level of the data center investor.The outage probability of the power supply units and the workload duration curve of servers are analyzed to define the loss of workload probability.The workload duration curve of the rack, hence, the power consumption of the servers is modeled as a function of server workloads.The server workloads are taken from a publicly available data set published by Google.The power consumption models of the major components of the internal power supply system are also presented which shows the power loss of the power distribution unit is the highest compared to the other components in the internal power supply system.The proposed reliability index and the power loss analysis could be used for rack-level computational resources expansion planning and ensures energy-efficient operation of the data center.
A Study of Students’ Preferences in The Information Resources of The Digital Learning Environment

The digital learning environment comprises various resources - didactically transformed and untransformed information, and mediated communication. Students’ information behaviour combines both actions characteristic of the traditional educational process and specific for the digital environment, based on digital tools and user interactions. Students’ information behaviour in the digital environment is considered as an indicator of their engagement in various educational activities that contribute to the personalisation of learning. The results of a survey on students’ preferences of information resources in the digital environment show that learners use a variety of information sources, but they mainly apply the methods of work in the “traditional” learning paradigm. They insufficiently use the digital environment potential of collaboration, knowledge exchange, and knowledge extraction from authentic sources. Obtained data indicates problems in students’ information culture and shortcomings in the methodological support of students’ autonomous work. Based on the results, recommendations on creating conditions for developing students’ prospective strategies of interaction with digital resources are proposed. These recommendations include a gradual increase of the authentic digital learning resources, an account of students’ information preferences, and a particular attention to the management issues in the digital learning environment.
Using Smart Contracts to Establish Decentralized Accounting Contracts: An Example of Revenue Recognition

ABSTRACT This study explores how to use smart contract technology to implement accounting principles for public use. To illustrate its feasibility, this study develops a design model of decentralized accounting contracts (DACs) and uses revenue recognition to demonstrate how to apply the proposed model. Considering various scenarios of revenue recognition, this study adopts Solidity to program smart contracts for five use cases: (1) regular sale, (2) installment sale, (3) gift card sale, (4) a contracted sale with multiple performance obligations, and (5) a contracted sale with variable considerations over the contract price. The results show that smart contracts can be created to fully address complex revenue recognition scenarios according to the Generally Accepted Accounting Principles (GAAPs). In conclusion, we discuss the implications of the study for business organizations, regulatory agencies, and the accounting profession. JEL Classifications: M40.
NLP-based personal learning assistant for school education

Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slackbased UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chatbot’s NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future.