Reviewerly/exHarmony huggingface dataset:
Innovative Pedagogical Technologies in Education System

The society in which we live is constantly evolving and changing. The modern world educational space is constantly being replenished with new content of knowledge, new qualifications. New spheres of relations are emerging, new specialties that form new disciplines. World higher education is undergoing reform. This led to the search for new forms and technologies of education. Harmonization of higher education in accordance with the requirements of the world space and standards, its development is carried out according to certain principles. This is, first of all, the priority introduction of innovative achievements in education and science. It is known that it is the innovative way of development of society that can ensure the formation of a generation of people who think and work in a new way. As a result, the main attention will be paid to the development of personality, cultural and communicative preparedness, the ability to independently acquire and develop knowledge, to form information and social skills. Considering this, the main purpose of the article is to study the main aspects of innovative pedagogical technologies in education system
Large-Scale Printed Chinese Character Recognition for ID Cards Using Deep Learning and Few Samples Transfer Learning

In the field of computer vision, large-scale image classification tasks are both important and highly challenging. With the ongoing advances in deep learning and optical character recognition (OCR) technologies, neural networks designed to perform large-scale classification play an essential role in facilitating OCR systems. In this study, we developed an automatic OCR system designed to identify up to 13,070 large-scale printed Chinese characters by using deep learning neural networks and fine-tuning techniques. The proposed framework comprises four components, including training dataset synthesis and background simulation, image preprocessing and data augmentation, the process of training the model, and transfer learning. The training data synthesis procedure is composed of a character font generation step and a background simulation process. Three background models are proposed to simulate the factors of the background noise patterns on ID cards. To expand the diversity of the synthesized training dataset, rotation and zooming data augmentation are applied. A massive dataset comprising more than 19.6 million images was thus created to accommodate the variations in the input images and improve the learning capacity of the CNN model. Subsequently, we modified the GoogLeNet neural architecture by replacing the fully connected layer with a global average pooling layer to avoid overfitting caused by a massive amount of training data. Consequently, the number of model parameters was reduced. Finally, we employed the transfer learning technique to further refine the CNN model using a small number of real data samples. Experimental results show that the overall recognition performance of the proposed approach is significantly better than that of prior methods and thus demonstrate the effectiveness of proposed framework, which exhibited a recognition accuracy as high as 99.39% on the constructed real ID card dataset.
A Survey of One Class E-Commerce Recommendation System Techniques

Although several recommendation algorithms are widely used by both commercial and non-commercial platforms, they face unique challenges such as sparse data sets and the absence of negative or “neutral” feedback. One-class algorithms attempt to overcome the data sparsity problem by using the implicit feedback inherent in user’s clicks and purchases, which are deduced from both positive and negative feedback. Existing literature uses several heuristic strategies to derive the negative samples needed for training, such as using random sampling or utilizing user-item interaction. However, these assumptions do not always reflect reality. In addition, with the explosive increase in the availability of big data for training recommendation systems, these methods might not adequately encapsulate the representations of the latent vectors. In this paper, we address the common issues of one-class recommendation and provide a survey on approaches that have been used to mitigate the existing challenges. To tackle the identified problems, we propose a neural network-based Bayesian Personalized Ranking (BPR) for item recommendation and personalized ranking from implicit feedback. BPR provides an optimization criterion derived from Bayesian analysis of a problem to develop an optimized model for such a problem. We conduct several experiments on two varieties of MovieLens datasets to illustrate the performance of the proposed approach. Our approach shows an impressive result in mitigating the issues of one-class recommendation when compared with the complexity of the state-of-the-art methods.
Wayback Machine: A tool to capture the evolutionary behavior of the bug reports and their triage process in open-source software systems

The issue tracking system (ITS) is a rich data source for data-driven decision-making. Different characteristics of bugs, such as severity, priority, and time to fix, provide a clear picture of an ITS. Nevertheless, such information may be misleading. For example, the exact time and the effort spent on a bug might be significantly different from the actual reporting time and the fixing time. Similarly, these values may be subjective, e.g., severity and priority values are assigned based on the intuition of a user or a developer rather than a structured and well-defined procedure. Hence, we explore the evolution of the bug dependency graph together with priority and severity levels to explore the actual triage process. Inspired by the idea of the "Wayback Machine" for the World Wide Web, we aim to reconstruct the historical decisions made in the ITS. Therefore, any bug prioritization or bug triage algorithms/scenarios can be applied in the same environment using our proposed ITS Wayback Machine. More importantly, we track the evolutionary metrics in the ITS when a custom triage/prioritization strategy is employed. We test the efficiency of the proposed algorithm using data extracted from three open-source projects. Our empirical study sheds light on the overlooked evolutionary metrics--e.g., overdue bugs and developers' loads--which are facilitated via our proposed past-event re-generator.
Unsupervised Remote Sensing Image Super-Resolution Guided by Visible Images

Remote sensing images are widely used in many applications. However, due to being limited by the sensors, it is difficult to obtain high-resolution (HR) images from remote sensing images. In this paper, we propose a novel unsupervised cross-domain super-resolution method devoted to reconstructing a low-resolution (LR) remote sensing image guided by an unpaired HR visible natural image. Therefore, an unsupervised visible image-guided remote sensing image super-resolution network (UVRSR) is built. The network is divided into two learnable branches: a visible image-guided branch (VIG) and a remote sensing image-guided branch (RIG). As HR visible images can provide rich textures and sufficient high-frequency information, the purpose of VIG is to treat them as targets and make full use of their advantages in reconstruction. Specially, we first use a CycleGAN to drag the LR visible natural images to the remote sensing domain; then, we apply an SR network to upscale these simulated remote sensing domain LR images. However, the domain gap between SR remote sensing images and HR visible targets is massive. To enforce domain consistency, we propose a novel domain-ruled discriminator in the reconstruction. Furthermore, inspired by the zero-shot super-resolution network (ZSSR) to explore the internal information of remote sensing images, we add a remote sensing domain inner study to train the SR network in RIG. Sufficient experimental works show UVRSR can achieve superior results with state-of-the-art unpaired and remote sensing SR methods on several challenging remote sensing image datasets.
Learning geodesic-aware local features from RGB-D images

Most of the existing handcrafted and learning-based local descriptors are still at best approximately invariant to affine image transformations, often disregarding deformable surfaces. In this paper, we take one step further by proposing a new approach to compute descriptors from RGB-D images (where RGB refers to the pixel color brightness and D stands for depth information) that are invariant to isometric non-rigid deformations, as well as to scale changes and rotation. Our proposed description strategies are grounded on the key idea of learning feature representations on undistorted local image patches using surface geodesics. We design two complementary local descriptors strategies to compute geodesic-aware features efficiently: one efficient binary descriptor based on handcrafted binary tests (named GeoBit), and one learning-based descriptor (GeoPatch) with convolutional neural networks (CNNs) to compute features. In different experiments using real and publicly available RGB-D data benchmarks, they consistently outperforms state-of-the-art handcrafted and learning-based image and RGB-D descriptors in matching scores, as well as in object retrieval and non-rigid surface tracking experiments, with comparable processing times. We also provide to the community a new dataset with accurate matching annotations of RGB-D images of different objects (shirts, cloths, paintings, bags), subjected to strong non-rigid deformations, for evaluation benchmark of deformable surface correspondence algorithms.
Improving speaker de-identification with functional data analysis of f0 trajectories

Due to a constantly increasing amount of speech data that is stored in different types of databases, voice privacy has become a major concern. To respond to such concern, speech researchers have developed various methods for speaker de-identification. The state-of-the-art solutions utilize deep learning solutions which can be effective but might be unavailable or impractical to apply for, for example, under-resourced languages. Formant modification is a simpler, yet effective method for speaker de-identification which requires no training data. Still, remaining intonational patterns in formant-anonymized speech may contain speaker-dependent cues. This study introduces a novel speaker de-identification method, which, in addition to simple formant shifts, manipulates f0 trajectories based on functional data analysis. The proposed speaker de-identification method will conceal plausibly identifying pitch characteristics in a phonetically controllable manner and improve formant-based speaker de-identification up to 25%.
From ‘Wow’ to ‘Why’: Guidelines for Creating the Opening of a Data Video with Cinematic Styles

Data videos are an increasingly popular storytelling form. The opening of a data video critically influences its success as the opening either attracts the audience to continue watching or bores them to abandon watching. However, little is known about how to create an attractive opening. We draw inspiration from the openings of famous films to facilitate designing data video openings. First, by analyzing over 200 films from several sources, we derived six primary cinematic opening styles adaptable to data videos. Then, we consulted eight experts from the film industry to formulate 28 guidelines. To validate the usability and effectiveness of the guidelines, we asked participants to create data video openings with and without the guidelines, which were then evaluated by experts and the general public. Results showed that the openings designed with the guidelines were perceived to be more attractive, and the guidelines were praised for clarity and inspiration.
Selection and Optimization of Hyperparameters in Warm-Started Quantum Optimization for the MaxCut Problem

Today’s quantum computers are limited in their capabilities, e.g., the size of executable quantum circuits. The Quantum Approximate Optimization Algorithm (QAOA) addresses these limitations and is, therefore, a promising candidate for achieving a near-term quantum advantage. Warm-starting can further improve QAOA by utilizing classically pre-computed approximations to achieve better solutions at a small circuit depth. However, warm-starting requirements often depend on the quantum algorithm and problem at hand. Warm-started QAOA (WS-QAOA) requires developers to understand how to select approach-specific hyperparameter values that tune the embedding of classically pre-computed approximations. In this paper, we address the problem of hyperparameter selection in WS-QAOA for the maximum cut problem using the classical Goemans–Williamson algorithm for pre-computations. The contributions of this work are as follows: We implement and run a set of experiments to determine how different hyperparameter settings influence the solution quality. In particular, we (i) analyze how the regularization parameter that tunes the bias of the warm-started quantum algorithm towards the pre-computed solution can be selected and optimized, (ii) compare three distinct optimization strategies, and (iii) evaluate five objective functions for the classical optimization, two of which we introduce specifically for our scenario. The experimental results provide insights on efficient selection of the regularization parameter, optimization strategy, and objective function and, thus, support developers in setting up one of the central algorithms of contemporary and near-term quantum computing.
A novel risk management model in the Scrum and extreme programming hybrid methodology

&lt;span&gt;Risk management in software development has always been one of the necessities of software project management. The logical nature of software projects and products has caused several challenges and risks in these projects. On the other hand, with the emergence of Agile methodologies, especially Scrum, and extreme programming (XP) methodologies, in recent years, this issue has become more serious. This is mainly because emphasizing limited documentation in these methodologies has caused these methods to pay little attention to some aspects of project management, particularly risk management. Concentrating on this challenge, the current study has proposed a risk management model in the hybrid methodology, combining Scrum and XP. Using this model in a case study shows this model's success in achieving risk management purposes. The results of this study indicate an appropriate reduction in the number of reworks, change requests, identified risks, and occurred risks. Moreover, the number of eliminated risks and team productivity have increased.&lt;/span&gt;
A Systematic Approach Towards Efficient Private Matrix Multiplication

We consider the problems of Private and Secure Matrix Multiplication (PSMM) and Fully Private Matrix Multiplication (FPMM), for which matrices privately selected by a master node are multiplied at distributed worker nodes without revealing the indices of the selected matrices, even when a certain number of workers collude with each other. We propose a novel systematic approach to solve PSMM and FPMM with colluding workers, which leverages solutions to a related Secure Matrix Multiplication (SMM) problem where the data (rather than the indices) of the multiplied matrices are kept private from colluding workers. Specifically, given an SMM strategy based on polynomial codes or Lagrange codes, one can exploit the special structure inspired by the matrix encoding function to design private coded queries for PSMM/FPMM, such that the algebraic structure of the computation result at each worker resembles that of the underlying SMM strategy. Adopting this systematic approach provides novel insights in private query designs for private matrix multiplication, substantially simplifying the processes of designing PSMM and FPMM strategies. Furthermore, the PSMM and FPMM strategies constructed following the proposed approach outperform the state-of-the-art strategies in one or more performance metrics including recovery threshold (minimal number of workers the master needs to wait for before correctly recovering the multiplication result), communication cost, and computation complexity, demonstrating a more flexible tradeoff in optimizing system efficiency.
GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models

Deep learning (DL) techniques involving finetuning large numbers of model parameters have delivered impressive performance on the task of discriminating between language produced by cognitively healthy individuals, and those with Alzheimer's disease (AD).However, questions remain about their ability to generalize beyond the small reference sets that are publicly available for research.As an alternative to fitting model parameters directly, we propose a novel method by which a Transformer DL model (GPT-2) pre-trained on general English text is paired with an artificially degraded version of itself (GPT-D), to compute the ratio between these two models' perplexities on language from cognitively healthy and impaired individuals.This technique approaches state-ofthe-art performance on text data from a widely used "Cookie Theft" picture description task, and unlike established alternatives also generalizes well to spontaneous conversations.Furthermore, GPT-D generates text with characteristics known to be associated with AD, demonstrating the induction of dementia-related linguistic anomalies.Our study is a step toward better understanding of the relationships between the inner workings of generative neural language models, the language that they produce, and the deleterious effects of dementia on human speech and language characteristics.
MSCTD: A Multimodal Sentiment Chat Translation Dataset

Multimodal machine translation and textual chat translation have received considerable attention in recent years.Although the conversation in its natural form is usually multimodal, there still lacks work on multimodal machine translation in conversations.In this work, we introduce a new task named Multimodal Chat Translation (MCT), aiming to generate more accurate translations with the help of the associated dialogue history and visual context.To this end, we firstly construct a Multimodal Sentiment Chat Translation Dataset (MSCTD) containing 142,871 English-Chinese utterance pairs in 14,762 bilingual dialogues and 30,370 English-German utterance pairs in 3,079 bilingual dialogues.Each utterance pair, corresponding to the visual context that reflects the current conversational scene, is annotated with a sentiment label.Then, we benchmark the task by establishing multiple baseline systems that incorporate multimodal and sentiment features for MCT.Preliminary experiments on four language directions (English↔Chinese and English↔German) verify the potential of contextual and multimodal information fusion and the positive impact of sentiment on the MCT task.Additionally, as a by-product of the MSCTD, it also provides two new benchmarks on multimodal dialogue sentiment analysis.Our work can facilitate research on both multimodal chat translation and multimodal dialogue sentiment analysis.
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization

A common method for extractive multidocument news summarization is to reformulate it as a single-document summarization problem by concatenating all documents as a single meta-document.However, this method neglects the relative importance of documents.We propose a simple approach to reorder the documents according to their relative importance before concatenating and summarizing them.The reordering makes the salient content easier to learn by the summarization model.Experiments show that our approach outperforms previous state-of-the-art methods with more complex architectures.
SMT-Based Planning Synthesis for Distributed System Reconfigurations

Abstract Large distributed systems with an emphasis on adaptability are now considered a necessity in many domains, yet reconfiguration of these systems is still largely carried out in an ad hoc fashion, a process that is both inefficient and error-prone. In this paper, we tackle the planification problem for the reconfiguration of distributed systems in the component-based reconfiguration model Concerto. Specifically, given some tasks to execute and a desired final state of the system, we show how to compute a reconfiguration plan that guarantees satisfaction of inter-component dependencies and is also optimized for parallel execution. Our technique relies on an SMT solver to compute the required dependencies between components and ultimately schedule the reconfiguration. We illustrate the use of this technique on a variety of synthetic examples as well as a real use case in the context of an OpenStack system.
VIS-iTrack: Visual Intention Through Gaze Tracking Using Low-Cost Webcam

Human intention is an internal, mental characterization for acquiring desired information.From interactive interfaces containing either textual or graphical information, intention to perceive desired information is subjective and strongly connected with eye gaze.In this work, we determine such intention by analyzing real-time eye gaze data with a low-cost regular webcam.We extracted unique features (e.g., Fixation Count, Eye Movement Ratio) from the eye gaze data of 31 participants to generate a dataset containing 124 samples of visual intention for perceiving textual or graphical information, labeled as either TEXT or IMAGE, having 48.39% and 51.61% distribution, respectively.Using this dataset, we analyzed 5 classifiers, including Support Vector Machine (SVM) (Accuracy: 92.19%).Using the trained SVM, we investigated the variation of visual intention among 30 participants, distributed in 3 age groups, and found out that young users were more leaned towards graphical contents whereas older adults felt more interested in textual ones.This finding suggests that real-time eye gaze data can be a potential source of identifying visual intention, analyzing which intention aware interactive interfaces can be designed and developed to facilitate human cognition.
Generation of highly pure single-photon state at telecommunication wavelength

Telecommunication wavelength with well-developed optical communication technologies and low losses in the waveguide are advantageous for quantum applications. However, an experimental generation of non-classical states called non-Gaussian states at the telecommunication wavelength is still underdeveloped. Here, we generate highly-pure-single-photon states, one of the most primitive non-Gaussian states, by using a heralding scheme with an optical parametric oscillator and a superconducting nano-strip photon detector. The Wigner negativity, the indicator of non-classicality, of the generated single photon state is -0.228 ± 0.004, corresponded to 85.1 ± 0.7% of single photon and the best record of the minimum value at all wavelengths. The quantum-optics-technology we establish can be easily applied to the generation of various types of quantum states, opening up the possibility of continuous-variable-quantum-information processing at the telecommunication wavelength.