Reviewerly/exHarmony huggingface dataset:
The effect of inside outside circle (IOC) model by using flashcards towards students' English vocabulary mastery

&#x0D; This research aims to determine the effect of Inside Outside Circle (IOC) model by using flashcards on students' English vocabulary mastery. This research was done on the 3rd-grade students of one elementary school in Magelang Regency, Central Java, Indonesia. This research used Pre-Experimental Design which type is One Group Pretest Posttest Design through one kind of treatment. The samples in this research were 3rd-grade students as many as 20 students. The data collection method was a multiple-choice test. The instrument test of this research was a validity test by experts (expert judgment), they are English lecturers of a private university Magelang and a 3rd-grade teacher of elementary school in Magelang Regency. The results of this research pointed out that there was a positive effect of the Inside Outside Circle (IOC) model by using flashcards on studentsâ€™ English vocabulary mastery. The effect was shown by pre-test and post-test results with means 49 and 80. The result of the Wilcoxon test showed that the significance of Z value was -3,725 with Asymp. Sig. (2-tailed) 0,000 &lt; 0,050.
Garbage object detection method based on improved Faster R-CNN

Garbage sorting plays an important and far-reaching role in environmental protection and resource regeneration. At present, garbage sorting is mainly manual assistance. Therefore, because of the characteristics of different object sizes in garbage images, this paper proposes a garbage object detection method based on improved Faster R-CNN. ResNet50 is the backbone network, and a Feature Pyramid Network structure is added to the model. Modified RPN structure parameters, and the original ROI pooling layer is changed to ROI align layer to achieve effective extraction of image features. The experiment shows that the improved Faster R-CNN method with ROI Align and FPN structure is effective in garbage object detection. ROI Align increased by 0.8 percentage points and FPN structure increased by 0.96 percentage points. Finally, the improved Faster R-CNN method achieves 92.81% accuracy on the garbage target detection dataset.
PERBEDAAN MINAT BELAJAR SISWA MENGGUNAKAN APLIKASI GOOGLE MEET DAN ZOOM MEETING PADA MATA PELAJARAN PAI

The outbreak of the COVID-19 virus in Indonesia has caused activities across all sectors to undergo changes and adjustments, including in the field of education. To reduce the spread of the virus, the government established distance learning policies as a solution to the limitations of the teaching and learning process. Student learning achievement is caused by one of the internal factors, namely Learning Interests. Based on the results of the survey, the Google Meet and Zoom Meeting application became the most popular distance learning media. This can be one of the factors that can affect students' learning interests. The study was created with the aim of distinguishing students' learning interests between using the Google Meet app and Zoom Meeting on PAI subjects.This research uses comparative quantitative methods (ex post facto) by spreading questionnaires online to students of SMAN 3 Babelan as a source of research data. A total of 54 students were objected to the study. For data processing techniques, the study used descriptive statistical techniques. From the results of this study, the results of the t count are smaller than the t table, so it can be concluded that there is no significant difference in student learning interest between using the Google Meet app and Zoom Meeting on PAI subjects. From the results of this study, the results of the t count are smaller than the t table, so it can be concluded that there is no significant difference in student learning interest between using the Google Meet app and Zoom Meeting on PAI subjects.
Geometric Range Searchable Encryption with Forward and Backward Security

Geometric range query is a general query algorithm over spatial data and applies to many real-world applications, such as location-based services. Considering the sensitivity of location information, how to guarantee the confidentiality of the location information while providing efficient query service becomes a big concern. Several cryptographic solutions are proposed to solve this problem, particularly those dynamic searchable encryption schemes with forward and backward privacy that provides strong security guarantees for encrypted spatial databases that support data deletion and addition. Despite the increasing efforts, recent studies show that existing solutions with these two securities either use less secure property-preserving encryption for efficiency and flexibility or intuitively build a binary tree for each dimension, which leads to poor scalability. This paper proposes a novel forward and backward secure geometric range searchable encryption scheme on encrypted spatial data. Specifically, we build a two-level index for first-step rough navigation and second-step precise testing to get accurate search results. Detailed theoretical analysis and experimental evaluation demonstrate that compared with related work, our scheme achieves strong security and sub-linear search efficiency while boosting the average update time by 70 times.
Research on Image Stegnography using based on Suduko matrix and LSB Value with multi-level encryption

In order to conceal data in such a manner that is invisible to a viewer, digital steganography makes use of the host signal. A complete reproduction of the actual picture is possible with discrete wavelet transform which translate integers to numbers. So, we put forth a method that incorporates the signal bit stream into the LSBs of the integer wavelets of a true picture. Additionally, the method performs a preprocessing phase on the cover image to correct saturation pixel components and retrieve the encrypted messages without losing it. Due to our growing dependence on digital media, novel techniques of safeguarding it from unauthorised use are urgently needed. Encryption is one of the methods that has been used in real-world applications for a very long time. The capability to exchange data between people in a way which prohibits someone else from accessing it is the fundamental service which cryptography provides. The presented model's high transparency, even with big message sizes, was demonstrated by experimental findings.
Analysis of Customer Expense using Market Basket Analysis Model with Hash-Based Algorithm and Association Rules

The Market Basket Analysis is a method to analyze consumer spending patterns. The method use data that is retrieved from the transaction database, then processed to obtain information. Normally, the data transaction is obtained from supermarket or any retail business. The transaction database does not serve management any information because it only contains raw data. Therefore, market basket analysis is conducted to analyze consumer's patterns. In this study, the algorithm used is a hash-based algorithm. The algorithm reduces the number of candidate item-sets at the beginning. The results of this study found 2 frequent items that is being bought together, namely bread and butter with support value of 96. Then eggs, bread and butter with support value of 97. Based on the results of the implementation of hash-based algorithm and association rules, we can conclude that managers or any stakeholder is suggested to use promotion on the products above. Those items that are being frequently bought together need to be placed in the same shelf or near each other to accommodate consumer for convenient purposes.
INNOVATION IN EDUCATION

The innovation process is a complex activity of creating, developing, using and disseminating modern or modernized innovations (theories, methods, technologies, etc.). This is the process of transforming scientific knowledge into innovation, the process of constantly transforming an idea into a product, technology or service. The goal of the innovative processes is the development of the school as a pedagogical system and special social organization, the achievement of qualitatively new higher educational results and the increase of the competitiveness of educational institutions. This process is motivated, intentional, conscious, aiming to transfer the education system to a new qualitative state, in a way of development. This article addresses the issue of innovation in education, which is a superior, primary product of pedagogical creativity, validated by its originality and confirmed relevance at the social level, at the scale of the educational system and process. The article carries out an analytical review of the field of educational innovation. The situation of innovation in the education system is evaluated, how much it means reform of the system and how much innovation, but also how innovation can be stimulated in the education system. Innovation in education encourages teachers and learners to explore, research and apply all tools to discover something new. Innovation is an essential component for many economic, social and technical fields, and education is no exception to this.
Discursive-manipulative strategies in scam emails and SMS: The Nigerian perspective

Abstract Cyber scam, a subculture in Nigeria, especially among youths, has been under-investigated from the linguistic perspective. This study thus explores discursive-manipulative strategies in scam emails and SMS/messages in Nigeria, drawing samples from a corpus of over 200 emails and 50 SMS documented between 2018 and 2022. With insights from Brown and Levinson’s face, Mey’s pragmatic act and McCronack’s information manipulation theories, it is observed that discursive manipulative strategies such as positive and negative false alarm, self-denigration, formulaic, and evocation of theistic and religious context project the Nigerian realities in scam emails and SMS. Embedded in these manipulative strategies are face-saving and face-threatening acts strategically woven round the violation of the maxims of quality and quantity.
Training a Lightweight ViT Network for Image Retrieval

Recently, Vision Transformer (ViT) networks have achieved promising advancements on many computer vision tasks. However, a ViT network has a large number of parameters, which is not conducive to deploy of the models in the edge devices. In this paper, we focus on learning lightweight ViT models and formulate both the quantization and distillation strategies into the multi-exit framework to compress the size of the model as much as possible. Specifically, we use the quantized heterogeneous distillation strategy based on the relaxed contrastive loss that incorporates pairwise similarities in the convolutional teacher network into the training process of a student network based on ViT whose weights are quantized to a limited set of levels. Then, we deploy the quantized heterogeneous distillation strategy on the multi-exit framework to further compress the model and explore five types of early exits for the ViT-based models. This paper establishes the latest technical level on four popular image retrieval benchmarks, and achieves the leading Recall@1 results 78.5%, 84.5%, 90.3% and 95.2% respectively on CUB-200–2011, SOP, Cars196, In-Shop, while reducing the size of the model by 50%, ensuring that the ViT-based models run in a resource-constrained environment but without much performance loss.
TAFDet: A Task Awareness Focal Detector for Ship Detection in SAR Images

With the wide application of Synthetic Aperture Radar(SAR) radar in maritime surveillance, ship detection method has been developed rapidly. However, there is still a key problem that is the general parallel detection head network has task inconsistency between different tasks. In this work, we proposes a task awareness focal Detector (TAFDet) that alleviate the problem of task inconsistency, consisting of two sub-networks: task awareness attention(TAA-Subnet) and cross-task focal sub-network (CTF-Subnet). Firstly, we design TAA-Subnet that can use the standard backpropagation operation to transform the gradient signal into an attention map of different tasks. To select prominent attention features and obtain a task awareness mask, we proposed a mask generation module(MGM) in TAA-Subnet. Second, CTF-Subnet is designed to tune features in different tasks using task awareness mask, where a feature focal module (FFM) was proposed to enhance the features not paid attention to in the task. Experiments show that our method consistently improved state-of-the-art detectors on SSDD and HRSID datasets. Specifically, our TAFDet consistently surpasses the strong baseline by 0.4 $$\sim $$ 5.8 AP with different backbones on SSDD and HRSID datasets.
Multi-scale Coarse-to-Fine Network for Demoiréing

Demoiréing is a multi-level image restoration task involving texture and color restoration. However, the moiré pattern in documents is difficult to remove, and the blurred font also appears. In this paper, we propose a multi-scale coarse-to-fine neural network to solve this problem. First, a multi-scale dense network structure is adopted to capture the moiré pattern from different resolution features, in which a spatial attention module is utilized that has remarkable performance in capturing moiré pattern. Second, a lightweight deblurring network is taken to remove the blur in documents for further optimziation. Through ablation experiments, the effectiveness of our method is demonstrated. The network proposed in this paper takes the third place in the document demoiréing track of the 2022 Baidu.com AI Competition.
Perspectives of Leadership in the Modern School and Kindergarten – Results of Empirical Research

The article traces aspects of leadership in the modern school and kindergarten from different perspectives, formulated on the basis of analysis of theories and understandings of leadership, management styles, roles of the director of an educational institution. A study is presented, the main purpose of which is to improve the understanding of these qualities of the leader that make him a successful leader. The contribution to the district of effective leadership, as part of management functions and skills, is in outlining the characteristics of the leader that are important for creating a leadership community and outlining those that are not considered particularly important among principals, teachers, experts and teachers . It has been established that the created conditions for the professional development of pedagogical specialists who understand, share and support the views in the leadership vision for the development of the institution, the manifestation of a culture of generating innovative ideas, the ability to manage based on magnetism, attractiveness, charisma. are the indicators for successful implementation and development of leadership.
Hybrid K-Modes and Firefly Algorithm based Entropy Measure for Clustering Heterogeneous Categorical Timber Opinionated Data

When working with heterogeneous categorical data, clustering becomes more difficult because of the lack of inherent distance measurements defined between data objects. Further research is required to find practical clustering techniques for diverse datasets. This work describes a proposed entropy measure (K-Modes +FA +EM) for clustering diverse categorical opinionated data that combines the strengths of the k-modes and the firefly algorithm. A firefly algorithm is one of the optimum global methods. The approach improves k-modes capability in clustering. Entropy uses to measure the effect of information loss during clustering. Several parameter settings are tested and tweaked to determine the K-Modes+FA +EM's effectiveness. According to the findings, the K-Modes+FA+EM achieves a higher accuracy rate (59.70%) than the K - Modes and the K - Modes+ EM. It is interesting to note that K-Modes+EM improves accuracy by 26% compared to K-Modes+FA+EM. Recall, Precision, F-1 Measure, and Fowlkes-Mallows Score also improve. Future work will evaluate more machine-learning methods and categorical data.
THE STUDENTS’ PERCEPTION TOWARDS THE EFFECTIVENESS OF SPEAKING FOR INTERMEDIATE SUBJECT TO SUPPORT THEIR SPEAKING SKILL OF ENGLISH DEPARTMENT STUDENTS IN MAKASSAR MUHAMMADIYAH UNIVERSITY

This research is aimed to find out the Students’s Perception towards the Effectiveness of Speaking for Intermediate Subject to Support their Speaking Skill of English Department Students in Makassar Muhammadiyah University. This research was descriptive qualitative. The population were 20 students at the third semester. The instrument used observation and interview.As a result on the findings, the students gave the positive perception toward the implementation of speaking for intermediate subject to measure the effectiveness of speaking for intermediate subject to support their speaking skill. However, the students claimed that they have got many advantages during the implementation of speaking for intermediate subject. This research also found out that this strategy also needed to be improved by the lecturers in terms of giving instruction, observing the class and evaluating the implementation of speaking for intermediate subject.
Install and Upgrade

One of the first things you will want to do is to install SQL Server 2022 so you can explore all the goodness it has to offer. The great news for anyone familiar with SQL Server is that the installation experience for SQL Server 2022 is fairly identical to previous releases with a few minor changes.
EHNP Strikes Back: Analyzing SM2 Implementations

The SM2 digital signature algorithm is part of the Chinese standard public key cryptography suite designed on elliptic curves and has been included in various Chinese commercial applications. Due to the structure of the algorithm and quality of coding, some implementations are vulnerable to potential side-channel attacks and leak information about the double-and-add chains. Popular SM2 libraries such as GmSSL, TASSL and old versions of OpenSSL still use sliding-window (recommended by standard) or wNAF to conduct scalar multiplication of points, which is vulnerable to side-channel attacks like Flush+Reload: key recovery is then an instance of the Extended Hidden Number Problem (EHNP). The EHNP can be reduced to the Shortest Vector Problem (SVP) and solved with lattice algorithms. In this paper, we propose an extended key-recovery attack with leaked double-and-add chains from signature schemes such as SM2 and ECDSA. The side-channel leakage is possible in libraries which use wNAF or sliding-window multiplication. Our approach translates side information of different implementations to an EHNP instance, then propose novel strategies to reduce the EHNP to SVP in a lattice of smaller dimension than previous method, and introduce our algorithms to solve the problem. To evaluate the probability, we provide new estimations for the norm of the target vector, and formulate a tradeoff function. Finally, we show the new record of attacking SM2 with provided information. We are able to recover the secret key with only three signatures, while previous attacks required more than six signatures. We also attack the SM2 traces with improved probability and efficiency. Our new algorithm does not rely on any specific digital signature scheme, thus can be used to attack other signature algorithms.
Instagram Filtering Hashtags using the hits Algorithm and Crowd Tagging

Instagram is a great place to look for descriptive tags for photographs and other types of information. Inaccordance with the learning by example paradigm, the tags–image pairs can be utilised to train automated image annotation (AIA) systems. In earlier research, we found that, on average. Approximately 22% of Instagram hashtags are related to the image's visual content,accompany, in the sense that they are descriptive hashtags, whereas there are many irrelevant hashtags, in the sense that they are not descriptive hashtags.Stop using hashtags on completely different photographs merely to get more clicks and likes.Enhancement of searchability We provide a revolutionary methodology in this study that is based on the collective intelligence principles that aid in the discovery of those hashtags. We demonstrate this in particular that the use of a modified version of the widely used hyper link induced topic search. In the context of crowd tagging, the (HITS) algorithm provides an effective and consistent method for locating pairs of Instagram photographs and hashtags, resulting in representative and noise-free results. Content-based image retrieval training sets We used crowdsourcing as a proof of concept platform Figure-eight to enable for the collection of collective intelligence in the form of tag selection.For Instagram hashtags, this is known as (crowdtagging). Figure-crowdtagging eight's data is utilised to create bipartite networks in which the first kind of node relates to the annotators and the second type of node corresponds to the annotations input the hashtags they've chosen. The HITS algorithm is used to rank the annotators in the first place,in terms of their efficiency in the crowdtagging activity, and then to find the appropriate hashtags for each situation image.
Blockchain Applications

In the previous chapter, we reviewed the latest technical progress in blockchains. We discussed works whose goals are to improve the security and performance of the blockchain systems. Another parallel line of work is to find new applications for blockchains that are beyond cryptocurrencies. Such efforts involve developing smart contracts for existing and new use cases.
Local linear embedding algorithms based on probability distributions

In response to the problem that the local linear embedding algorithm (LLE) only uses the concept of nearest neighbour to binarize the assignment of sample relations (there are only two fixed value cases of 0 and 1) and cannot judge or distinguish the degree of difference between any two points on the manifold, and the problem that the LLE algorithm does not make full use of the discriminative information of the data, this paper attempts to combine the discriminative information of the data and proposes a kernel based on probability distribution density estimation local linear embedding algorithm (KDELLE) in combination with discriminative information of the data. The algorithm obtains a natural one by estimating the kernel density of the sample on the manifold, which can be used to represent the affiliation score of a point on the manifold to a certain class of points, and adds discriminative information to obtain a weight matrix based on the probability distribution to further represent the distribution of points on the manifold. Through experimental comparative analysis on the bearing dataset, the method not only has a good clustering effect but also has good feature extraction results.
The Geometry Enhanced Deep Implicit Function Based 3D Reconstruction for Objects in a Real-Scene Image

For the 3D reconstruction of objects in a real scene, the state-of-the-art scheme is to detect and identify the target by a classic deep neural network and reconstruct the 3D object with deep implicit function (DIF) based methods. This scheme can be computationally and memory efficient, representing high-resolution geometry of arbitrary topology for reconstructing the 3D objects in a scene. However, geometry constraints are lacking in these procedures, which may lead to fatal mistaken identification or structural errors in the reconstruction results. In this paper, we propose to enhance the geometry constraint of the DIF-based 3D reconstruction. A geometry retainer module (GRM) ensures the detected target always retains the correct 2D geometry. The chamfer distance (CD) is introduced as a constraint on the 3D geometry for the DIF-based method. Correspondingly, a strategy to extract a point cloud from the signed distance field (SDF) is proposed to complete this 3D geometry constraint. Abundant experiments show that our method improves the quality of 3D reconstruction greatly.