Reviewerly/exHarmony huggingface dataset:
Busy Book Learning Media Development to Improve The Reading Ability of Children Aged 5-6 Years at Bungong Kupula Kindergarten Pasie King

This study aims to improve the reading ability of children aged 5-6 years in Bungong Kupula Kindergarten .This study was designed using a 4-D model ( four D model) consisting of 4 main stages, namely: Define , Design , Develop , and Disseminate .The research was conducted during the 2020-2021 academic year pandemic .The data collection techniques for this study were 1) a questionnaire validation sheet from a team of experts, 2) a questionnaire response sheet for students and teachers, 3) an observation sheet for the reading ability test of children aged 5-6 years.The results showed that the busy book media was very feasible and effective in improving reading learning which had implications for the average score of the child's initial test, which was 34.54 and on the final test it increased to 68.18 with an increase in the score of 33.64 .
Pengaruh Pemanfaatan Media Youtube Terhadap Keterampilan Menulis Cerpen

This article aims to describe the effect of using Youtube media on the ability of fifth graders at SDN Gerem 1 Cilegon City in writing short stories. The method used in this research is the True Experimental Design method using the Pretest-Posttest Control Group Design research design. The results showed that the use of Youtube media had a significant effect on students' ability to write short stories. The results of the experimental class pretest before getting treatment using Youtube media an average value of 59.5. The results of the short story writing skills test after getting treatment using Youtube media the average value of 80.16.
Dropout-Based Ensemble Dual Discriminator for Cross-Domain Sentiment Classification

The main task of Cross-Domain Sentiment Classification is to train a well-performing classification model by using labeled source domain data, and then transfer the model to unlabeled target domain data, thereby solving the expensive labor consumption and domain shift caused by a large number of labels resulting performance degradation. Most mainstream adversarial domain adaptation methods are based on a single discriminator, which ignores the uneven distribution of labels between domains and multiple modalities of data and tends to cause negative transfer and poor generalization performance. We propose a Dropout-based ensemble dual discriminator for Cross-Domain Sentiment Classification. We functionally decouple the single discriminator by using two forms of text data, and replace it with a positive sentiment discriminator and a negative sentiment discriminator. A dynamic set of discriminators will be obtained by random deactivation of the discriminator network neurons, then the feature extractor has to extract richer and more realistic domain-invariant features to fool the discriminator and mitigate the mode collapse phenomenon. To solve the problem of class imbalance in a large number of unlabeled data samples, we use mutual information maximization to train sentiment classifiers to ensure that label predictions are distributed in a reasonably balanced state. We conduct full experiments on the Amazon and Airlines datasets. Experiments showed that our proposed model achieves state-of-the-art cross-domain sentiment classification performance.
A reconfigurable design of satellite in-orbit encryption algorithms based on FPGA

Encryption algorithm is the basis for securing satellites, achieving reliable user access and secure data transmission. However, since the electromagnetic information generated by satellite encryption operation has certain regularities, the risk of leaking plaintext is high when the side channel is attacked, especially using a single algorithm. To solve this problem, this paper analyzes encryption algorithms with different types, summarizes four coarse-grained operators, and proposes a coarse-grained reconfigurable encryption module (CGREM) for satellites based on FPGA. Based on the coarse-grained reconfigurable architecture (CGRA), we implement a reconfigurable configuration subsystem and a computing array on a 0.15 &mu;m CMOS FPGA. The result of the performance test show that our module can reconstruct encryption algorithms in 1.76 us. Compared with the previous method, our design is three orders of magnitude faster and more efficient. Our method can obtain the resource consumption rate of LUT and FF less than 60%, which means it can achieve a derating effect on satellite components.
Research and Application of Fault Early Warning and Location Device for Low-voltage Distribution Network Based on Edge Computing

Using traditional automation technology for fault early warning and location of low-voltage distribution network has some disadvantages, such as complex technology implementation, low early warning accuracy, long fault location time and so on. Therefore, firstly, this paper studied the implementation principle of fault early warning and location in low-voltage distribution network. Secondly, making full use of the edge computing to play the role of data analysis on the terminal side, a fault early warning and location device for low-voltage distribution network was designed. Besides, the overall process of realizing early warning and location was formulated. Finally, through the field application test, the accuracy of early warning and the rapidity of location of the device were verified. The results showed that the average early warning accuracy of the device was 92.11%, the fault location time was less than 10s, there was no false alarm and missing alarm, and has high practical application value.
PREPARATION OF INFILTRATION WELL DEVELOPMENT PLANNING IN KADIRI UNIVERSITY AREA

Along with the development in Indonesia, resulting in the reduction of vacant land used as water infiltrated into the ground. This causes frequent flooding such as at Kadiri University, Kediri City there are often flood puddles when rainfall is high. To overcome this disaster, it is necessary to create infiltration wells in the area because the infiltration wells are so effective, and practical. This study aims to overcome or overcome puddles at Kadiri University. The metodologi penelitian is carried out in several stages, namely the primary and secondary data collection stage, the second processing of parametric testdistributiondata, the third calculation of water discharge and smirnov kolmogrov test, thefourth determination of the dimensions of infiltration wells. The final result of the analysis obtained sage of infiltration in the form of a circle with a diameter of 1.4 meters depth of 3 meters a number of 153 pieces. Dengan the existence of infiltration wells later is expected to reduce puddles in the campus area.
A Novel Lightweight Architecture of Deep Convolutional Neural Networks

Deep convolutional neural networks have achieved much success in many computer vision tasks. However, a network has millions of parameters which limit its inference speed and usage for some situations with limited storage space. Low-rank based methods and pruning methods are verified effective to compress the number of parameters and accelerate inference speed of deep convolutional neural networks. As the price, the performance of the networks decreases. To overcome this problem, in this paper, we design a novel low-rank and sparse architecture of convolutional neural networks. Besides accelerating inference speed and reducing parameters, our approach achieves better performance than baseline networks.
Investigation of multi-agent interaction processes in socio-demographic systems

The article provides a conceptual description of the structural-demographic theory and examples of its mathematical implementation. The method of multi-agent modeling is described. As a test of the structural-demographic theory, a multi-agent system of demographic-structural interactions, including the state, is pro-posed. The proposed method opens up a wide range of studies of the influence of culture, ideology, and information wars on internal and geopolitical dynamics.
A comparative study of the directional characteristics of two gamelan gongs

While previous research on the structural modes of gamelan gongs has revealed important insight into its unique acoustics, little attention has been dedicated to study the directionality of their acoustic radiation. This study compares high-angular resolution spherical directivity measurements of two Balinese gamelan gongs of different sizes. The directional characteristics are usually closely connected with measured structural modes at both low and high frequencies. However, the directivities of the large and small gong for the same structural mode shape are not always consistent, particularly at higher frequencies. Thus, changes in modal frequencies due to the scaling of the gongs do not always indicate similar acoustical radiation patterns.
Blockchain-Aided Hierarchical Attribute-Based Encryption for Data Sharing

Ciphertext-policy attribute-based encryption (CP-ABE) is widely used in access control to achieve secure data sharing over different cloud platforms. However, most of the existing CP-ABE data sharing schemes perform one-time encryption on the shared data, which cannot satisfy the need for data sharing in the public cloud with complex users. In order to realize multi-user data sharing on blockchain and achieve hierarchical decryption of privacy data and shared data, we propose a blockchain-aided hierarchical and searchable attribute-based encryption scheme, named BC-HSABE. In BC-HSABE, we adopt a symmetric encryption algorithm to encrypt data in a hierarchical manner, and use attribute-based encryption technology to encrypt two hierarchical symmetric keys of the encrypted data file, and upload the ciphertext to the cloud server. In addition, data users can decrypt data ciphertext at different levels according to their hierarchical authority. Meanwhile, searching for keyword trapdoor through blockchain ensures the security of keyword ciphertext. The security analysis and experimental evaluations verify the feasibility and effectiveness of BC-HSABE.
VQEG Column: VQEG Meeting Jun. 2021 (virtual/online)

Welcome to the fifth column on the ACM SIGMM Records from the Video Quality Experts Group (VQEG). The last VQEG plenary meeting took place online from 7 to 11 June 2021. As the previous meeting celebrated in December 2020, it was organized online (this time by Kingston University) with multiple sessions spread over five days, allowing remote participation of people from 22 different countries of America, Asia, and Europe. More than 100 participants registered to the meeting and they could attend the 40 presentations and several discussions that took place in all working groups. This column provides an overview of the recently completed VQEG plenary meeting, while all the information, minutes and files (including the presented slides) from the meeting are available online in the VQEG meeting website.
Neural Network-Based Stereo Vision Outlier Removal

Stereo vision systems rely on accurate feature matching to provide valid stereo reconstruction and pose estimation. This accuracy is achieved through outlier removal techniques, such as RANSAC. However, images also contain semantic information, which can be extracted using neural networks. This paper proposes an additional outlier removal method, where the images are semantically segmented using a neural network, before the features identified are assigned semantic identifiers using a probabilistic data association technique, and matches are evaluated based on this added semantic information. This blending of feature-based techniques with dense semantic maps allows for more information to be tied to each feature, not just its position in the image. This opens paths to applications like class-based clustering. The approach proposed is compared to a traditional outlier removal system by comparing the produced disparity values to known ground truth measurements, and assessed for accuracy and execution speed. It is shown how the addition of semantic segmentation does improve the accuracy of disparity measurements in stereo images, with a loss in processing speed. However, this loss can be mitigated by utilising more specialised hardware.
Estimasi Potensi Cadangan Air Tanah Dengan Metode Persamaan Darcy di Desa Pilomonu, Gorontalo

This research aims to know the prediction of potential for groundwater reserves. The research method applies surface geological mapping and subsurface data collection with the 200-span Schlumberger configuration geoelectric method. The stratigraphy of the research area is sorted from old to young, the first is the granodiorite unit, the sandstone unit, and alluvial units are deposited inconsistently. The geological structure working in the research area has a dominant northwest-southeast direction. The direction of. groundwater flow follows the topography of the research area, whicb is flowing from the north to the south, where the elevation of the norlh js higher than the eJevation of the south. The aquifer characteristics in the research area can be divided into two based on the mterpretation of geoelectric inversion, such as shallow aquifer with material in the form of sand and deep aquifer with sand and gravel material. Prediction of the potential for groundwater reserves is obtained from the interpretation of geoelectric data and data from the calculation of the groundwater level in the research area by observing the. conditions around the research area So, it is obtained a prediction of potential for shallow groundwater reserves is 360.444 m3/day or equivalent to 360,444 liters/day, and the prediction of poteptial deep groundwater reserves is 66,520.476 m3/day or equivalent to 66:,520,476 liters/day.
Students’ Self Perception Towards Digital Reading: Findings from Indonesian

Digital Reading is one of the options in the context of online learning to stop the Covid-19 virus from spreading since the Covid-19 pandemic has forced schools and colleges to close. The purpose of this study is to describe how students perceptions about learning English using digital reading when the Covid-19 pandemic is occurring. This study employed a descriptive quantitative methodology. 40 of 6th semester students from English Education Study Program, Guide and Counseling Study Program and Early Childhood Teacher Training Study Program of Universitas Muhammadiyah Palopo, Indonesia were participants. To get data, a questionnaire about student perceptions of the practicality of digital reading was issued as a Google form. According to the study's findings, the majority of students are enthusiastic about the use of digital reading. it is thought to be more helpful and effective in supporting learning activity.
Artist beyond style

The thematic series of articles “Artist beyond style” by the authors Artamonova M.M., Arutyunyan J.I., Kareva A.A., Kisteneva S.V., Moiseeva S.V., Pavlova N.V., Usacheva S.V. and Chuiko L.V. is dedicated to the study of various aspects of national artistic culture from Enlightenment to Modernity outside of style affiliation.
Sparse Support Vector Machine with Fisher-Regularizer for Data Reduction

The goal of data reduction is to remove noise features or samples from the original data and hence improve the performance of algorithms. Although Fisher-regularized support vector machine (FisherSVM) has good separability, FisherSVM lacks sparsity to reduce noise data. To alleviate this issue, we propose a novel sparse support vector machine with Fisher-regularizer (SF-SVM). The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{1}$</tex> -norm of model coefficients and the Fisher-regularizer term used in SF-SVM can induce a sparse model and improve the generalization performance, respectively. Besides, the proposed SF-SVM includes two versions: linear SF-SVM and kernel SF-SVM, which can perform feature and sample reduction with favorable sparsity, respectively. Experimental results on seven UCI datasets show that the proposed SF-SVM has a satisfactory data reduction ability and good classification performance.
Will the Robot’s Dominance of the Stock Market Disrupt the Market Order?

Economic activities are the most basic activities of mankind and are of great significance. At one end of economic activity is man and nature, which reflects the restriction of natural laws on human will; the other end is individual and society, which reflects the restriction of collective existence on individual will. As a contemporary form of economic activity, the market has therefore become a space where individuals purposeful actions are intertwined with unintended consequences under the dual constraints of nature and society.
Lilac: Parallelizing Atomic Cross-Chain Swaps

Hashed Timelock Contract (HTLC) is a widely-used protocol for cross-chain asset swaps. However, it relies on serial asset-locking to guarantee atomicity, which causes high latency and poor fairness. Aiming at the drawbacks of HTLC, we propose Lilac, a cross-chain asset swap protocol that supports parallel asset-locking. Lilac replaces the unique asset-unlocking credential in HTLC with multiple sub-credentials generated by all participating users, and the sequence of sub-credentials is used as the complete asset-unlocking credential. Users obtain the complete credential only when all assets have been locked, and the credential construction process is independent of the order in which assets are locked, so atomicity can be guaranteed when users lock their assets in parallel. Experiments show when a swap involves 2 to 4 blockchains, Lilac reduces the swap latency by 36.75% to 62.20%. Moreover, Lilac reduces the waiting time gap between different users so the fairness of a swap is improved.
Ethical IoT: A Sustainable Way Forward

The Internet of Things (IoT) is rapidly developing, primarily driven by businesses that see opportunities for profit through new business and business models. Other key players include public administrations and non-profit institutions that see IoT as an opportunity to address societal challenges in effective ways that were not reasonably available, before. Good public use of IoT can pay off rapidly, and lead to the ability of serving citizens and businesses better, at lower costs, in more inclusive ways. The first applications in the public domain show promising results – and it is still early days.
Design of intelligent mining system for learning needs based on intelligent mining technology

The development of network technology has brought great convenience to academic education and realized the interaction of learning. Students can find the learning materials they need through the online learning platform, which will lead to online learning behavior. Based on intelligent mining technology and knowledge model intelligent matching algorithm, this paper designs a student demand mining system, and then takes students in an electrical major class as the research object, and uses this system to mine the learning behavior and skill needs of the students in this class during online learning. Among the online learning behaviors generated by electrical majors, browsing behaviors and retrieval behaviors account for a large proportion, and students have a greater demand for the improvement of single-chip programming skills and CAD drawing skills.
Online signature verification based on attention mechanism depth-wise separable convolution residual network

How to adaptively learn important signature features and use a lightweight model to achieve high-precision signature verification is still a challenge in the field of online signature verification. In response to this challenge, we proposed an attention mechanism depth-wise separable convolution residual network(A-DWSRNet) for online signature verification. First of all, the weight of signature features is adaptively learned through the convolutional attention module to improve the representation learning ability of the network. Next, the depth-wise separable convolution and depth convolution modules are introduced to improve the standard residual structure to construct a depth-wise separable residual unit, which reduces the overall parameter amount of the model and alleviates the loss of feature information of the multi-step residual structure. Finally, the proposed method has achieved 2.88% equal error rate (EER) and 4.17% EER in MCYT100 database and SVC2004-task2 database, respectively. The results show that the proposed method can effectively improve the accuracy of the Online signature verification system.
Synergistic partners: How HRM and leadership climate shape employees’ ambidextrous behavior

Recent advancements in ambidexterity research suggest that HRM plays a vital role in shaping contextual ambidexterity. Building on social information processing theory, the present study extends this literature by putting the spotlight on two fundamental, yet often implicit assumptions of contextual ambidexterity. First, we examine how HRM practices that are specifically designed to promote ambidextrous cues (i.e., ambidexterity-oriented HRM [A-HRM] practices) are related to contextual ambidexterity. Second, we address the premise that such HRM practices actually affect all employees in an organization. In addition, we introduce transformational leadership (TFL) climate as a critical boundary condition that helps to translate paradoxical signals of A-HRM practices to the employees. We tested these relationships in a multi-source sample of 16,740 employees from 94 organizations and found support for the proposed interaction effect of A-HRM and TFL climate on employees’ ambidextrous behavior and, in turn, organizational performance (i.e., indirect effects). Our study contributes to a better understanding of creating contextual ambidexterity and the important role of leaders in the implementation of A-HRM practices.
Panel Segmentation: A Python Package for Automated Solar Array Metadata Extraction using Satellite Imagery

The NREL Python Panel-Segmentation package is a toolkit that automates the process of extracting accurate and valuable metadata related to solar array installations, using publicly available Google Maps satellite imagery. Our previously published work includes automated azimuth estimation for individual solar installations in satellite images. Our continued research focuses on automated detection and classification of solar installation mounting configuration (tracking, fixed-tilt) and type (rooftop, ground, carport). Specifically, a Faster-RCNN Resnet-50 feature pyramid network (FPN) model was trained and validated on over 770 manually labeled satellite images. This model was used to perform object detection on satellite imagery, locating and classifying individual solar installations' mounting configuration and type. Preliminary model results showed a combined mean average precision score (mAP) score across classes of 49.87% using an Intersection over Union (IoU) threshold of 0.5. We intend to release the complete image data set with labels on the NREL DuraMAT DataHub, to encourage further research in this area. Additionally, a pipeline for automated metadata extraction, including detection of mounting configuration and type as well as azimuth estimation, will be released via the NREL Panel-Segmentation package for public use.
PHILOSOPHY OF EDUCATION AS THE CONCEPTUAL BASIS OF MODERN RUSSIAN EDUCATION

Entering of contemporary Russian society and state info global transformation process (fundamentally) radically changed nature and directions of public development. The process of becoming juridical state in Russia, took place in the period, witch was called transitional. Such period, means transformation in all spheres of society, and first of all in juridical sphere. Reforming of educational system of modern Russia was also involved info global process.