Reviewerly/exHarmony huggingface dataset:
gCBO: A Cost-based Optimizer for Graph Databases

Query optimization is an especially challenging problem in graph databases due to its wide plan space and the difficulty in gathering statistics. In this demonstration, we propose a new cost-based query optimizer called gCBO for graph databases and implement it in a specific graph database (i.e., gStore). To tackle the aforementioned challenges, gCBO employs a hybrid plan enumerator based on dynamic programming, cost models that capture the characteristics of different types of joins, and a sampling-based cardinality estimation strategy that gathers the necessary statistics on-the-fly. What is more, to absorb the experience of users, we build an interactive component for gCBO, which allows users to receive the optimized execution plans with detailed information and generate their own plans for execution.
Extreme Systematic Reviews: A Large Literature Screening Dataset to Support Environmental Policymaking

The United States Environmental Protection Agency (EPA) periodically releases Integrated Science Assessments (ISAs) that synthesize the latest research on each of six air pollutants to inform environmental policymaking. To guarantee the best possible coverage of relevant literature, EPA scientists spend months manually screening hundreds of thousands of references to identify a small proportion to be cited in an ISA. The challenge of extreme scale and the pursuit of maximum recall calls for effective machine-assisted approaches to reducing the time and effort required by the screening process. This work introduces the ISA literature screening dataset and the associated research challenges to the information and knowledge management community. Our pilot experiments show that combining multiple approaches in tackling this challenge is both promising and necessary. The dataset is available at https://catalog.data.gov/dataset/isa-literature-screening-dataset-v-1.
CLOUD COMPUTING SECURITIES ISSUE

As the demand for cloud computing grows, so does the requirement for cloud computing security.The cloud platform's security was the focus of several researchers, but no one has yet proven it.Similar to the same focus, this work first examines a variety of methods, including encryption, authentication, and many others.Then, based on recent analysis, some of these methods were chosen to suggest a novel approach.This work is carried out in three distinct phases: a) Cryptography-based Frameworks; b) A Proposed Fragmentation Framework; and c) A Proposed Biometric-Based Framework.Five distinct datasets consisting of a mixture of various types of data files, including audio, video, and image, are created in order to conduct an evaluation of the effectiveness of each of these phases.This has data sizes ranging from 100 to 300 files, with a maximum of 289810KB.Based on various phase-specific parameters, the performance is analyzed for each phase.
The network society in the ‘new normal times’: Crisis digitalization and adaption of cultural organizations in the COVID-19

For the recent 3 years, there has been a growing research interest in adaptation to the new normality and crisis digitalization during pandemic. However, there are a little empirical researches in cultural organizations. This study is in line with some sorts of approaches of network society and autonomy. The aim of the research is to study how cultural institutions integrate into the network society through one of the critical elements in creating a network society – digitalization during COVID-19 pandemic. The study was conducted using a mixed methodology in 2021 in St. Petersburg. Three blocks of empirical data were collected and analyzed based on theaters, museums, libraries, and creative spaces: 67 questionnaires; 12 expert interviews; webometrics of social networks of 108 organizations. The key difficulties were the weakness of the technical base and the lack of competencies and specialists to produce content and attract an audience online. The key findings are three strategies of adaptation to the conditions of the new normality: deepening digitalization; hybrid and strategy of autonomy from digitalization. The strategy of deepening digitalization is typical mainly for libraries that are active online even before the pandemic. They found themselves in the most advantageous position by building and expanding the previously developed digital activity. The hybrid strategy was mainly characteristic of museums, which intensified the digitization of collections and introduced new formats, including broadcasts from previously closed repositories. The strategy of autonomy from digitalization is more inherent in theaters, for which the transition to online turned out to be the most difficult and in many cases impossible. Crisis digitalization has exposed the structural difficulties associated with (non-)willingness to transform the former autonomy into the new requirements of the network society.
A semi-supervised label distribution learning model with label correlations and data manifold exploration

Label distribution learning is a novel machine learning paradigm to deal with label ambiguity, which is the generalization of the traditional single-label learning and multi-label learning paradigms. Though label distribution learning has attracted a lot of attentions recently, data sets with label distributions rather than logical labels have always been scarce and most of the existing models put emphasis mainly on the supervised learning, neglecting the utilization of unlabeled samples. In this paper, we propose a semi-supervised Label Distribution Learning model with label Correlations and data Manifold exploration (sLDLCM). On one hand, sLDLCM is a semi-supervised extension of existing label distribution learning models, which can effectively make use of both labeled and unlabeled data for better capturing the underlying data properties; on the other hand, sLDLCM jointly estimates the label distributions of unlabeled samples and the other model variables. Besides, both label correlations and local data manifold are explored in sLDLCM. Extensive experiments are conducted on six real-world data sets including two facial expression, one movie rating, two bioinformatics and one visual sentiment analysis data sets. Comparative studies demonstrate that the proposed sLDLCM model achieves better performance the state-of-the-arts in terms of five evaluation metrics.
Is it Possible to Maintain Information Flow in Japanese into Thai Translation? A Study of Connecting Clauses

Abstract This paper investigates the ordering of connecting subordinate and coordinate clauses in Japanese-Thai translation to examine whether it is possible to maintain information flow from the source text. Japanese preposed clauses were translated using both preposed and postposed clauses in the Thai translated text. Preposed clauses were used widely when they provided: 1) listed information; 2) grounded information for comprehending main clauses; 3) the framework within which main clauses are held; 4) temporal sequences; and 5) information linking prior context to a new discourse topic. Postposed clauses were used mainly to modify main clauses. In cases where clauses provided the information needed to comprehend subsequent clauses, professional translators attempted to place the clause sentence initially, despite a preference for sentence-final occurrence arising from Thai syntactic constraints. This demonstrated norms in Japanese-Thai translation in terms of awareness among professional translators of the significance of information flow in the source text.
The effect of narrative coherence and visual salience on children’s and adults’ gaze while watching video

Low-level visual features (e.g., motion, contrast) predict eye gaze during video viewing. The current study investigated the effect of narrative coherence on the extent to which low-level visual salience predicts eye gaze. Eye movements were recorded as 4-year-olds (n = 20) and adults (n = 20) watched a cohesive versus random sequence of video shots from a 4.5-min full vignette from Sesame Street. Overall, visual salience was a stronger predictor of gaze in adults than in children, especially when viewing a random shot sequence. The impact of narrative coherence on children's gaze was limited to the short period of time surrounding cuts to new video shots. The discussion considers potential direct effects of visual salience as well as incidental effects due to overlap between salient features and semantic content. The findings are also discussed in the context of developing video comprehension.
Using Social Media Categorical Reactions as a Gateway to Identify Hate Speech in COVID-19 News

The spread of COVID-19 news on social media provided a particularly prolific ground for emotional commotion, disinformation and hate speech, as uncertainty and fear grew by the day. In this paper, we examine the media coverage of the COVID-19 outburst in Portugal (March-May 2020), the subsequent emotional engagement of audiences and the entropy-based emotional controversy generated as a gateway to detect the presence of hate speech, using computer-assisted qualitative data analysis (CAQDAS) embedded in a cross-sectional descriptive methodology. Our results reveal that negative and volatile categorical emotions ("Angry", "Haha" and "Wow") serve as main engines for controversy, and that controversial news have the highest sharing ratio. Moreover, using a small sample of the most controversial news with the highest overall emotional engagement, we establish a relation between the entropy-based emotional controversy obtained from Facebook's click-based reactions and the presence of cultural and ethnic hate speech, plausibly confirming the click-based categorical emotions as a gateway to hatred comment pools. In doing so, we also reveal that negative emotions alone do not always indicate the presence of hate speech, which may sprout in seemingly humorous social media posts where irony proliferates, and negativity is not apparent. This work adds to the literature on social media categorical emotion detection and its implications for the detection of hate speech.
Value-Oriented Approach to Evaluating the Results of Educational Work in Educational Organizations of the Novosibirsk Region

The article is written on the basis of the analysis of the results of monitoring the indicators of the system of organization of education in the Novosibirsk region. The most important component of federal state educational standards is a focus on educational results. The question of the system for assessing personal results is one of the key ones in the education system and educational institution. Guided by the Federal Law “On Education in the Russian Federationˮ № 273–FL, each educational organization ensures the functioning of the internal system for assessing the quality of education, develops and implements its own system for assessing the achievement of the planned results of the development of the main educational program; determines the main directions and goals of evaluation activities, criteria, procedures, composition of assessment tools. The lack of assessment materials on personal results in the main educational program of an educational organization is a violation of the implementation of the GEF of basic general education and the Law “On Education of the Russian Federationˮ. In the requirements for achieving personal results, the standard and regulatory documents do not define evaluation and diagnostic tools. Therefore, the study of personal results, both according to scientists and practitioners, is of particular difficulty. This article presents a value-oriented approach to assessing the results of educational work in the educational organizations of the Novosibirsk region which can be used to achieve personal learning outcomes.
Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theory

The classification analysis of imbalanced data remains a challenging task since the base classifier usually focuses on the majority class and ignores the minority class. This paper proposes a reliability-based imbalanced data classification approach (RIC) with Dempster-Shafer theory to address this issue. First, based on the minority class, multiple under-sampling for the majority one are implemented to obtain the corresponding balanced training sets, which results in multiple globally optimal trained classifiers. Then, the neighbors are employed to evaluate the local reliability of different classifiers in classifying each test sample, making each global optimal classifier focus on the sample locally. Finally, the revised classification results based on various local reliability are fused by the Dempster-Shafer (DS) fusion rule. Doing so, the test sample can be directly classified if more than one classifier has high local reliability. Otherwise, the neighbors belonging to different classes are employed again as the additional knowledge to revise the fusion result. The effectiveness has been verified on synthetic and several real imbalanced datasets by comparison with other related approaches.
Al-Qur'an Learning with the Yanbu'a Model for the Quality of Pronunciation of Hijaiyah Letters at Islamic Boarding Schools

This study aims to determine the learning of the Koran with the yanbu'a model for the quality of the pronunciation of hijaiyah letters at the Ar-Roudhoh Krian Sidoarjo Islamic Boarding School and to find out the supporting and inhibiting factors in its application. The research method used is descriptive qualitative which produces data in the form of written words from the results of interviews, observations and documentation. The results showed that: (1) The application of the Yanbu'a model used 3 classical models, sorogan, and tadarus. Although the learning of the Qur'an was carried out well, phonological errors were found in the pronunciation of the hijaiyah letters, namely consonant errors and vowel errors. (2) The supporting factors are in practice using many variations and supported by khataman activities and giving awards to students who have achieved the memorization target. While the inhibiting factor is the application of existing regulations during the learning process of the Al-Qur'an pronunciation of the hijaiyah letters that are not enforced to students, especially the inadequate facilities and infrastructure so that the yanbu'a model for the quality of the pronunciation of the hijaiyah letters is less than optimal.
Stripe-Based Futurism-like Image—A New Type of Art and Its Application to Covert Communication

The generation of art images using computers has been studied recently. A new type of art image called a stripe-based Futurism-like image, and its application to covert communication, are proposed. Firstly, a source image is segmented into regions. The region corners are extracted by chain code analysis, and polygon approximation is conducted accordingly to simplify the region shapes. Then, each region is partitioned into stripes according to the region direction and the stripes are filled with the region’s average color, or the color white alternatively, resulting in an image with the abstraction and dynamism of Futurism. Furthermore, a data hiding method is proposed to embed a secret message into the created art image by filling the stripes with the aforementioned colors in a message-dependent fashion, with the message bits being adjusted beforehand by a 2-to-3 mapping scheme to avoid filling a region’s stripes with consecutive identical colors. Furthermore, hidden data security is enhanced by randomizing both the message-bit embedding order and the region processing sequence via the use of a secret key. By processing a database of 100 images, good experimental results of art image creation and secure message hiding have been obtained, showing the feasibility of the proposed methods for covert communication.
On the Universally Optimal Activation Function for a Class of Residual Neural Networks

While non-linear activation functions play vital roles in artificial neural networks, it is generally unclear how the non-linearity can improve the quality of function approximations. In this paper, we present a theoretical framework to rigorously analyze the performance gain of using non-linear activation functions for a class of residual neural networks (ResNets). In particular, we show that when the input features for the ResNet are uniformly chosen and orthogonal to each other, using non-linear activation functions to generate the ResNet output averagely outperforms using linear activation functions, and the performance gain can be explicitly computed. Moreover, we show that when the activation functions are chosen as polynomials with the degree much less than the dimension of the input features, the optimal activation functions can be precisely expressed in the form of Hermite polynomials. This demonstrates the role of Hermite polynomials in function approximations of ResNets.
Face-to-Face VS. Emergency Remote Teaching During the Covid-19 Lockdown. A Comparative Analysis of Ecuadorian Undergraduate Polytechnic Students’ Performance

The COVID-19 outbreak has changed education, mainly by shifting face-to-face classes to online sessions. Online courses are not new as many educational institutions offer distance or hybrid learning courses. However, emergency remote teaching (ERT), into which education has shifted, attains differences. This study aims to identify student performance differences depending on teaching modality (face-to-face and ERT). Studies considering the COVID-19 pandemic and information originating from Ecuador are scarce, making this study relevant. This quantitative research used a two-way ANOVA to analyze skills results from exams given in the academic year 2019 (face-to-face) and 2020 (ERT). Results were not statistically significant at p &lt; .05, which concurs with the existing literature.
A machine learning method to variable classification in OpenMP

OpenMP is a parallel computing framework that provides programmers with a set of directives and clauses to use when writing parallel applications. The most important task in adopting OpenMP is deciding the parallel pattern with associated clauses to employ in a sequential program that already exists. The shared-memory parallelization is complicated by parallel directives with different roles. Some tools have been developed to assist programmers in developing parallel programs using OpenMP. Many tools, however, have constraints on the size of program analysis, OpenMP scoping, and scalar and array reduction. Manually selecting clauses with the necessary data-sharing attributes is also prone to errors. In this study, we target the variable classification in directives to explore the loop-level parallelism. We set the variable classification problem as a type inference task based on a machine learning method, which understands the attributes of variables in certain contexts and relations. We propose an aligned corpus of tokens and types to predict variable attributes used inside the target loop. We support the reduction clause whenever it is applicable. Experimental results indicate that our method is very promising and favorably suited to dealing with real-world complex programs while showing high accuracy.
Transfer Learning-based GNSS Jamming Classification Scheme Using Overlapped SPWVD-PSD Images

In this paper, a transfer learning-based global navigation satellite system (GNSS) jamming classification scheme using overlapped images of the smoothed pseudo-Wigner-Ville distribution (SPWVD) and power spectral density (PSD) is proposed. Five types of jamming are considered: amplitude modulation jamming (continuous wave interference), linear chirp jamming, frequency modulation jamming, narrow-band noise jamming, and distance measurement equipment jamming (pulse jamming). The performance of the proposed scheme is shown to be the F1 score and the confusion matrix for pre-mentioned 5 jamming types with no jamming case. A representative conventional transfer learning-based GNSS jamming classification scheme uses a concatenated image comprising a spectrogram, in-phase and quadrature constellation, PSD, and histogram. However, in conventional schemes, the spectrogram
Design and Implementation of Sleep Aid System

Abstract: Mental stress is a major issue in today’s competitive environment. Insomnia, memory issues, depression, and other disorders can all be caused by a lack of sleep. Frustration, irritation, and a higher risk of heart disease are all symptoms of stress. Anxiety, inability to concentrate, and car accidents etc. The majority of individuals only sleep for 3-4 hours each day, which is insufficient. Enough. People should sleep for roughly 6-7 hours every day in reality. To alleviate this problem, many people utilize high doses of drugs. This is an issue that is extremely detrimental to the humanbody. There are several options available. Therapy options for insomnia is also there. Yoursleep cycle is regulated by aninternal “body clock” that controls when you feel weary and ready for bed or refreshed and awake which is known as cardiac rhythm. The circadian rhythm is a 24-hour cycle that this clock follows. You will grow progressively fatigued during the day after waking up from sleep. These emotions will peak in the evening, just before bedtime. Adenosine, anorganic substance generated in the brain, linked to sleep drive, also known as sleep-wake homeostasis. As you get extra weary throughout the day, your adenosine levels rise, and your body subsequently breaks down this chemical while yousleep. The circadian rhythm is also influenced by light. The hypothalamus is a particular area of nerve cellsin the brain,and the suprachiasmatic nucleus is a cluster of cells in the hypothalamus that processes signals when the eyes are exposed to natural or artificial light. The brain uses these signals to decide whether it is day or night. As natural light fades in the evening, the body produces melatonin, a drowsiness-inducing hormone. The hormone cortisol, which increases energy and alertness, is released when the sun rises in the morning. Most individuals require at least seven hours of sleep each night in order to operate well cognitively and behaviorally. An inadequate quantity of sleep might have catastrophic consequences. Sleep deprivation has been linked to attention lapses, impaired cognition, delayed responses, and mood swings in certain studies.
Joint strong edge and multi-stream adaptive fusion network for non-uniform image deblurring

• An efficient non-uniform motion deblurring network is proposed to introduce edge information to guide image deblurring task. • A new Multi-stream Adaptive Fusion Module (MAFM) is designed to focus on crucial information in different information flows. • A novel Dense Attention Feature Extraction Module (DAFEM) is developed to obtain significant recovery information. • Edge loss and perceptual loss are introduced to optimize the generator to improve the image restoration effect. Non-uniform motion deblurring has been a challenging problem in the field of computer vision. Currently, deep learning-based deblurring methods have made promising achievements. In this paper, we propose a new joint strong edge and multi-stream adaptive fusion network to achieve non-uniform motion deblurring. The edge map and the blurred map are jointly used as network inputs and Edge Extraction Network (EEN) guides the Deblurring Network (DN) for image recovery and to complement the important edge information. The Multi-stream Adaptive Fusion Module (MAFM) adaptively fuses the edge information and features from the encoder and decoder to reduce feature redundancy to avoid image artifacts. Furthermore, the Dense Attention Feature Extraction Module (DAFEM) is designed to focus on the severely blurred regions of blurry images to obtain important recovery information. In addition, an edge loss function is added to measure the difference of edge features between the generated and clear images to further recover the edges of the deblurred images. Experiments show that our method outperforms currently public methods in terms of PSNR, SSIM and VIF, and generates images with less blur and sharper edges.