Reviewerly/exHarmony huggingface dataset:
Semi-fragile watermarking with recovery capabilities for HGI compression method

The article proposes a new semi-fragile watermarking system with the ability of tamper localization and recovery after distortions, adapted for the HGI image compression method. The system uses a hierarchical image structure when embedding and replaces the stage of post-interpolation residuals quantization with a special quantizer based on quantization index modulation. As a result, the protected image becomes resistant to HGI compression with an adjustable quality parameter. The proposed watermarking system allows an image to be restored after distortions with an acceptable quality. In this case, the authentication part and the recovery part operate at different hierarchical levels. The developed watermark system, compatible with the HGI compression method, may be used to protect remote sensing images and medical images from malicious distortion.
Pengaruh Media E-Learning Berbasis LMS Moodle dan Motivasi Belajar terhadap Hasil Belajar Mahasiswa di Masa Pandemi Covid-19

Online learning in E-learning Media in Covid-19 conditions involving lecturers as educators and students as educators has an important role in improving student learning outcomes. The purpose of this study was to find out the influence of E-learning based on LMS Moodle and Motivation to Learn partially and simultaneously on student learning outcomes. This type of research is a mix method that integrates quantitative and qualitative research with sequential explanatory forms. The data collection techniques used are questionnaires or questionnaires and interviews. The instruments used are questionnaire sheets and interviews that are used to collect the data needed. The sample from this study was 75 accounting students. The data analysis used is inferential analysis with Classical Assumption Test (Normality Test and Linearity Test) and Multiple Regression Analysis with partial t test and simultaneous F test. The results showed that there was an influence of LMS Moodle-based E-learning Media, Partial and simultaneous Learning Motivation on student learning outcomes which was indicated by a significant value smaller than 0.05 and the large influence of both variables on dependent variables of 60.6%.
Remote sensing image registration based on the improved attention neighborhood consistency network

Image registration plays an important role in surface detection, aerial image classification, and satellite image fusion. However, the existing image registration frameworks pay less attention to local feature information. Additionally, such frameworks have insufficient extraction of important channel and spatial information and ambiguous matching. To address these issues, we propose an end-to-end remote sensing image registration framework based on the improved attention neighborhood consistency network. Our network improves the robustness of the rotation distortion region by employing an asymmetric convolution block in the feature extraction stage, which is augmented by two one-dimensional convolution kernels with square convolution kernels. Moreover, we use a 1 × 1 convolution to adjust the residual network channel to retain global information, realize feature reuse, and obtain multiscale image information. Next, the attention mechanism is improved by utilizing skip connections to resolve the problems of high resolution and image heterogeneity issues in remote sensing images. This allows the framework to quickly focus on important regional features and improve the network’s ability to distinguish and localize image features. In the feature matching stage, a bidirectional cross-correlation algorithm is used to establish the correspondence between the two pictures and match the corresponding points are matched. At the same time, the multilayer perceptron (MLP) structure is introduced into the neighborhood consensus network to eliminate the noise generated by mismatching. Further, it establishes a strong matching locality, prompts information aggregation at different positions and different channels, reduces registration deviation, and enhances matching robustness. The experimental results show that, overall, the algorithm outperforms comparable algorithms, extracting more accurate feature points and efficiently reducing the mismatch rate. The algorithm put forward effectively functions in a variety of situations, exhibiting high efficiency and high accuracy.
Pengembangan Perangkat Pembelajaran Elektronik Berbasis STEAM untuk Meningkatkan Minat dan Hasil Belajar

This study is a development research that aims to produce STEAM-based electronic learning device on colloid material to increase student’s interest and learning achievement that are valid, practical and effective. The development model uses the ADDIE development model which consists of 5 stages : (1) the analysis phase includes curriculum analysis, student analysis, concept analysis, formulation of learning objectives, and task analysis; (2) the design, which includes the stage of selecting the format and initial design of the RPP, e-module and E-LKPD using the Canva application; (3) the device development stage by integrating STEAM aspects and following the Project Based Learning (PjBL) syntax; (4)the deployment stage is the testing phase of learning tools that have been validated by experts and have been revised by developers; (5) the evaluation phase includes the evaluation of learning devices after going through the trial phase. Analysis of the results of the validation of RPP, e-Modul and e-LKPD based on the STEAM PjBL model, namely 3.53; 3.80; and 3.77 very valid categories. Practical based on the practicality test by looking at the implementation of STEAM-based learning the PjBL model has an average value of M = 1.87 which means that it is fully implemented; the responses of students and teachers were very positive with a percentage of 90.91% and 85.25%. Learning Devices also meet the effective criteria because the class completeness reaches 84.85% and the n-gain results in increasing interest in learning are in the medium criteria.
A Searchable Encryption Scheme Over Facial Image

In recent years, facial image search has been widely used. Although offering considerable convenience, facial image search also poses a severe threat to people’s privacy. How to conduct facial image search while protecting privacy has become a challenge. We design a scheme of searchable encryption facial image. The scheme is separated into two phases: uploading phase and retrieval phase. At the phase of uploading, the facial image is divided into two parts: the obfuscated features of the facial image and the encrypted facial image. In the retrieval phase, the facial image features are extracted and the similarity is calculated with the obfuscated facial features in the cloud after being obfuscated to obtain the closest obfuscated facial features and the corresponding encrypted facial image, then the encrypted facial image is decrypted in order to acquire the original facial image. Specifically, Facenet512 is used to collect facial image features while the obfuscation function is designed to obfuscate those facial image features and the Advanced Encryption Standard (AES) algorithm is used to encrypt facial images. Theoretical analysis and experimental results indicate that the scheme possesses favorable performance and high security.
MATHEMATICAL MODELING OF THE MARKET OF THREE GOODS IN TERMS OF SUPPLY LAG

The mathematical model of the free market of three goods in the conditions of supply lag in the interval of discrete time with market parameters, demand vector and initial conditions is considered. The results of simulation modeling of the market of 3 goods with the values of parameters and initial conditions are obtained. Graphs of the optimal dynamics of prices of three competing goods, graphs of the dynamics of demand for the three competing goods at the optimal dynamics of prices are analyzed. Graphically presents the dynamics of additional supplies of three competing products, the order of which was made steps earlier in forecasting at time t optimal prices. It was found that during the initial time interval the balances of unsold goods accumulate, the market is overstocked, so at the end of the interval orders for goods are terminated. The dynamics of the optimal allowable total deliveries of goods to the market and the graphs of the dynamics of sales of goods are presented and analyzed. The dynamics of the maximum allowable profit of the seller is obtained.
Inner-imaging 3D attention module for residual network

Abstract We propose an Inner-Imaging three-dimensional (3D) attentional feature fusion module for a residual network, which is a simple yet effective approach for residual networks. In our attention module, we constructed a 3D soft attention feature map to refine the input feature. The map fuses the attentional features from different dimensions, including channel and spatial axes, to create a 3D attention map. Then, we implemented a feature fusion module to further fuse the attentional features. Lastly, the attention module outputs a 3D soft attention map that is applied to the residual branch. The attention module can also model the relationship between attentional features from different dimensions and achieve the interaction between attentional features. This function allows our attention module to acquire more attentional features. To demonstrate the effectiveness of our method, extensive experiments were conducted on several computer vision benchmark datasets, including ImageNet 2012 and Microsoft COCO (MS COCO) 2017 datasets. The experimental results show that our method performed better than the baseline methods in the tasks of image classification, object detection, and instance segmentation tasks.
Student Ability Cultivating of Electronic Technology Experimental Teaching Adapted To “New Engineering”

To meet the cultivating requirements of the high-quality engineering and technology talents for the development of “new engineering”, the electronic information major of the college vigorously carries out the reform of the electronic technology experimental teaching. The electronic technology experiment is an important basic practical course in the electronic information major, which plays an important role in the ability cultivation of the engineering practice talents. Combining with the current needs of China to vigorously develop the “new engineering” construction, this paper discusses the problems existing in the traditional electronic technology experimental courses and proposes to split the electronic technology related modules from the school-enterprise cooperation or currently popular practical topics for the experimental topic selection. This experimental topic requires students to use the virtual simulation software to simulate the experimental tasks and complete the assigned design tasks before the class, and then complete the physical circuit experiments in the offline practical courses, which can cultivate the students' design thinking, exercise the students' hands-on ability and consolidate the knowledge learned in theoretical teaching, thereby adapt to the development of the "new engineering".
A method based on SVD-PCA microblog hot event prediction

Aiming at the problem of no standard for real-time data hotspot classification, this paper proposes a method based on SVD-PCA microblog hotspot event prediction, based on the TF-IDF algorithm to calculate keyword weights, construct a text-keyword matrix, and use SVD to generate covariance matrix, and use PCA method to reduce dimensionality to alleviate sparsity, determine the evaluation criteria of hot topics based on the amount of reading and discussion, and use logistic regression to predict. Finally, through experiments on the news data set of a news website from July 2020 to July 2021, the prediction method proposed in this paper can effectively improve the accuracy of the prediction of hot events on microblog.
THE EFFECTIVENESS OF USING HUMOUR STORIES TO IMPROVE THE READING SKILL OF SEVENTH GRADERS

The study aimed to find out the use of The Humour Stories to improve the students' reading skills. The subject of this study was the seventh grade of An Nuriyah Islamic Junior High School 22 Jagakarsa in the academic year of 2018/2019, consisting of 25 students. The method of this study was a quantitative method with the design of a pre-experimental study. The data from the pre-test and post-test were analyzed statistically. This study showed that the average score of the students in the pre-test was 64.8, while the average score of students in the post-test was 80.4. This research analysis obtained that t-observation (to) of 1.997. Meanwhile, the t-table (tt) is 1.708 with a significant 5% which means that toto is higher than tt. So, the alternative hypothesis (Ha) is accepted, and the null hypothesis (Ho) is rejected (1.997&gt;1.708). It can be concluded that the use of the humor stories of students of An Nuriyah Islamic Junior High School 22 Jagakarsa of 7th-grade students enhanced reading skills.
An efficient residual learning deep convolutional neural network for de-noising medical images

Image denoising is a pre-processing technique that is done in every image processing applications. It plays a significant role in the performance of any methods. The objective of this paper is to remove Gaussian noises at different noise levels in medical images. This paper proposed an efficient Deep Convolution Neural Network model for denoising medical images to remove Gaussian noise using Residual Learning. Convolutional Neural Networks are a class of deep neural networks that can be trained on large databases and have excellent performance on image denoising. Residual learning and batch normalisation are various techniques used to enhance the training process and denoising performance. The proposed RL-DCNN method is tested with 20 layers and evaluated using the performance metrics Peak Signal to Noise Ratio, Mean Square Error and Structural Similarity. It is compared with Denoising Convolutional Neural Network and Shrinkage Convolutional Neural Network models and proved to be better than the other methods.
Penetapan Alokasi Sawah Penerima Air Irigasi Berdasarkan Kondisi Hidroklimatologi di Daerah Irigasi Cihea

One of the main supporting factors for the agricultural sector in the Cihea Irrigation Area is the availability of sufficient water resources. However, uncertain climatic conditions due to climate change lead the changes in the rain distribution and affect the availability of water resources. The study aims to (1) assess the land water balance and drought index in Cihea Irrigation Area and (2) develop alternatives related to irrigation priority locations based on hydro climatological conditions. The water balance was analyzed using the Thornthwaite-Mather method (1957), while the irrigation priority locations were determined using the composite performance index method. Based on the annual water balance analysis, this irrigation area had a 568 mm/year water surplus. However, there was a water shortage from July to October, at 132 mm per month. From ten land systems,the highest deficit was in SL_9, while the lowest was in SL_1. These conditions triggered drought in some locations. The highest drought index occurred in August (56,89%), while the lowest occurred in July (3.98%). Based on hidroclimatological ,the prioritised locations for escalated productivity are paddy fields in CDK.16 Ka, CDK.17 Ki, PD.1 Ka, PD.1 Ki, PD.2 Ka, PD.2 Ki, CRJ.6 Ki, CRJ.7 Ki, CRJ.8 Ki, CRJ.9 Ki, CRJ.10 Ki, and CKR.1 Ki in the Ciranjang subdistrict. Meanwhile, to gain the equal irrigation distribution, the priority locations consist of paddy fields in CSK.1 Ki to CSK.5 ki, CSK.6 Ki, CSK.6 Ka, CSK.7 Ki to CSK.9 Ki, CSK.10 Ki, and CSK.10 Ka, situated in Bocongpicung District.&#x0D; &#x0D; Keywords: drought index, irrigation priority, water balance
The Influence of Computer Network Technology Using Digital Technology on the Quality of Physical Education in Colleges under Complex Scenes

Recently, college physical education has received a lot of attention. Traditional physical education and teaching are unable to meet the demand of students in today’s society and cannot attract students’ interest in sports. In this study, the challenges of college physical education classrooms are examined and the teaching objectives, contents, and teaching evaluation of the course are established to improve students’ deep learning (DL) and the effect and quality of college PE. The flipped classroom model based on DL is applied to PE teaching in colleges, and the influence of classroom teaching is explored. Moreover, a teaching experiment is conducted and the teaching effect before and after the experiment is compared. The results show that the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> </math> values of the three groups of students in the five items of 50 m running, sit-up/pull-up, 800 m/1000 m, sit and reach, and crossing direction change running are 0.003, 0.012, 0.024, 0.024, and 0.048, respectively. This indicates significant improvements in the physical quality of the three groups of students after the experiment. In addition, the three groups of students have significant differences in basketball technical and tactical application ability, DL ability, and autonomous learning ability after the experiment. This exploration integrates the concept of DL with the flipped classroom, providing a theoretical supplement for the design of flipped classrooms in college PE.
Organisation of Professional Cross-Cultural Interaction between Russian and Foreign Students in the Educational Space of the University

The purpose of the study is to provide a scientific substantiation of the pedagogical conditions for the organisation of professional cross-cultural interaction between Russian and foreign students in the educational space of the university. The paper shows the need to introduce the following pedagogical conditions into the educational space of the university: the inclusion of Russian and foreign students in joint extracurricular activities, the content of which is relevant to their future profession in a multicultural world; the joint exploration of the professional and cultural space of the city by Russian and foreign students; the use of an extensive system of partnerships with external subjects of professional activities. The study is novel in that it creates theoretical prerequisites for the transformation of the structure of the university’s educational space into a new designed state based on the introduction of new pedagogical conditions regarding the interaction of subjects of the educational process representing different cultures. As a result, the paper has provided insight into the processual features characterising the organisation of educational activities involving Russian and foreign students, has determined the structure and limits of the introduction of pedagogical conditions for the organisation of professional cross-cultural interaction between Russian and foreign students, has identified the dynamics of professional cross-cultural interaction on the basis of the cognitive, motivational, emotional and behavioural criteria developed by the authors.
SSV360: A Dataset on Subjetive Quality Assessment of 360° Videos for Standing and Seated Viewing on an HMD

In this paper, we introduce the SSV360 dataset containing psychophysical and psychophysiological data gathered in a pilot study on the effect of standing and seated viewing of 360 • videos on subjective quality assessment.The stimuli were shown to the participants on a head-mounted display for both viewing conditions.The ground truth contained in the SSV360 dataset may support, e.g., benchmarking of immersive media processing algorithms and developing of objective perceptual quality metrics.
Research on the Application of Blockchain Technology in Education and Teaching in Higher Vocational Colleges

With the progress and development of science and technology, blockchain technology has shown great influence in many fields. Blockchain technology has been widely used in various fields of society, and the integrated development of higher vocational education and blockchain technology has become a general trend. In view of the current dilemma of education and teaching innovation in higher vocational colleges, this paper creatively puts forward the application scheme of blockchain technology in education and teaching in higher vocational colleges, including the block chain model of higher vocational college campus, the block chain credit certification system of higher vocational college campus and the chain model in the credit system evaluation system of higher vocational colleges, The application scheme can build an individual learning and information big data system and create an intelligent education and teaching platform in higher vocational colleges, so that students and teachers can become the main users and beneficiaries of teaching resources and information, realize the best utilization and management of resources in the field of education and teaching in higher vocational colleges, and safely store information. This is of great significance to further improve the quality of talent training in Higher Vocational Colleges and improve the modernization level of vocational education.
Analysis of the Environment Factors and Power Quality of LV Distribution Networks for Residential Solar Power System in Yangon, Myanmar

The optimization of photovoltaic power system should be designed based on measuring the environmental factors and analyze the condition of electric power distribution in the region where it is to be installed. Using big data analysis on the environmental factors can solve the problems that could potentially affect power output. In this study, the environmental factors and LV grid quality in Yangon, Myanmar are measured and examined to establish whether a photovoltaic power system can be used. The environmental factors were collected specifically with the specially designed monitoring system which are gathering the environmental data every minute for one year. The monitoring system simultaneously measured environmental factors including horizontal/inclined solar irradiation, ambient temperature, and wind speed. The temperature was the highest from March to May in the Yangon region. During this period, the average temperature was 34°C and temperature at noon was 40°C or higher. Inclined solar irradiation reached its highest at &gt; 6 kW·m-2/day in March and decreased to &lt; 4 kW·m-2/day from July to October, when the rainy season began. In terms of LV grid quality, the voltage was determined to be unstable as it was 105 Vac at its minimum and 214 Vac at its maximum.
Towards Fine-grained Causal Reasoning and QA

Understanding causality is key to the success of NLP applications, especially in high-stakes domains. Causality comes in various perspectives such as enable and prevent that, despite their importance, have been largely ignored in the literature. This paper introduces a novel fine-grained causal reasoning dataset and presents a series of novel predictive tasks in NLP, such as causality detection, event causality extraction, and Causal QA. Our dataset contains human annotations of 25K cause-effect event pairs and 24K question-answering pairs within multi-sentence samples, where each can have multiple causal relationships. Through extensive experiments and analysis, we show that the complex relations in our dataset bring unique challenges to state-of-the-art methods across all three tasks and highlight potential research opportunities, especially in developing "causal-thinking" methods.