Reviewerly/exHarmony huggingface dataset:
The Guiding Power of Media on Public Opinion in the Internet Age -- Focus on the Construction of National Image

The change in media communication methods has led to a transformation in the audience and the subject and object of information. The diversity of information sources has made everyone become a producer and influencer, which has resulted in a multitude of voices and the multilateralism of public opinion on the Internet, posing a greater challenge to the media guidance power. The focus of this paper is on the impact of media guidance on public opinion, especially in how countries use public opinion to construct their national image. It begins with a literature review of previous researches on media changes, the relationship between media guidance power and public opinion, and the self-definition and other-definition of national images. Then, news articles from China and the United States are selected to analyze the performance of media in shaping their national images through content analysis and comparative analysis, and summarize the generality and feasibility of media guidance of public opinion. There is a problem of small sample size as quantitative analysis is unfeasible due to the timeliness and large number of news items. It is hoped that better solutions can be explored in future research.
The Current Situation and Countermeasures of Online Education Management in Chinese Universities in the "Post-epidemic" Era

With the advent of the post-epidemic era, educators and practitioners realize that online education is not only an emergency education approach to cope with special times, but also a driver of change in education and teaching as well as a necessary path to update the education model. Online teaching has been transformed from an emergency solution to normal behavior, which requires universities to actively establish an online teaching management system and mechanism that is compatible with the normalization of the epidemic, deeply explore the transformation of online teaching informatization and modernization, and advocate the construction of a teaching model that integrates online and offline to ensure the high-quality development of teaching activities in the post-epidemic era.
The Narration of the Relationship between Developments of Memes Culture and the Internet and Analysis of Memes Advantages in Diffuse

With the rise of the Internet and the establishment of online social platforms, there are more and more online information exchanges among netizens. As a product of the rise of the Internet, Memes culture has made an indelible contribution to virtual communication. This paper focuses on the development of memes culture and comprehensively describes the development of memes by studying and comparing domestic and overseas kinds of literature. In this article, the development of memes is summarized in three stages, with a summary of three eras of web development. It is divided into the first generation of memes whose main elements are characters, the second generation of memes represented by emojis that imitate faces and expressions, and the third generation of memes with narrative features. The emergence and development of the first, second, and third generations of memes do not correspond to the emergence and development of Web1.0, Web2.0 and Web3.0, but they are inseparable. At the same time, the article analyzes the reasons for the popularity of memes from five aspects: fast communication, narrative, low creation threshold, entertainment and identity. This paper also points out two problems in the development of Memes culture, namely, the weakening and degradation of language expression ability caused by the excessive use of memes, and the proliferation of vulgar online culture caused by the prevalence of memes
Pengembangan Media Pembelajaran Berbentuk Multimedia Interaktif pada Materi Bangun Ruang Siswa Kelas VI Sekolah Dasar

The progress and development of technology is increasingly rapid, making all compete to adapt to technology, one of which is the world of education and curriculum. For curriculum simplification, only the main material in learning activities needs to be conveyed, while for training can be independently or through application media. This research aims to create interactive multimedia-based learning media on building space material for grade VI elementary school students. This media can make it easier for students to understand the material to be conveyed and be able to adapt to technology, namely multimedia, this study uses research and development methods that are aligned with the curriculum used at school. Features in the multimedia include objectives and materials, as well as learning videos, and also evaluation. The product can be accessed using a smartphone.
Evolutionary computation to explain deep learning models for time series forecasting

Deep learning has become one of the most useful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, deep learning is known as a black box approach and most experts experience difficulties to explain and interpret deep learning results. In this context, explainable artificial intelligence (XAI) is emerging with the aim of providing black box models with sufficient interpretability so that models can be easily understood by humans. The use of an evolutionary-based association rules extraction algorithm to explain deep learning models for multi-step time series forecasting is addressed in this work. This evolutionary application is proposed to be used with the predictions obtained by long-short term memory (LSTM) deep learning network. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal, showing that almost 98% of the model can be explained.
Penerapan Metode Demonstrasi untuk Meningkatkan Keterampilan Berbicara Bahasa Indonesia Siswa Kelas V.B SD Negeri 7 Cakranegara

The objectives to be achieved through this research activity are: To find out that the demonstration method can improve speaking skills in learning Indonesian for class V.B students at SD Negeri 7 Cakranegara for the 2021/2022 academic year. The subjects of this study were 25 class VI.A students at SD Negeri 7 Cakranegara, consisting of 14 female students and 11 male students. The stages of this research include: (1) planning, (2) implementing actions, (3) observing, and (4) reflecting in each cycle. The result of the research is that the demonstration method can improve the speaking skills of class V.B students at SD Negeri 7 Cakranegara. This is supported by the increase in the average learning outcomes from pre-action by 56.00% to 76.00% in cycle I, increasing to 100 in cycle II at the end of learning to speak. The achievement of the average score in cycle II shows that students have met the minimum completeness criteria of 75. Research to improve students' speaking skills using the demonstration method has yielded results in the form of changes in students who were initially hesitant when practicing to be more daring in practice. The mistakes made by students have also decreased. Students also become more active in learning to speak. The activeness of students in learning activities is marked by an increase in student activity in asking, giving opinions and responding to the example of the presenter as well as increasing student confidence when advancing to practice in front of the class. Students have been able to practice speaking well and meet the criteria as skilled at speaking.
The aplication of the fractal analysis in oncopathology

Fractal analysis is an objective approach that in oncopathology is one of the important fields of application. In this study is present fractal methodologies at histological level that have been successfully applied to characterize pathological features and able to perform differential diagnosis and prognosis in oncopathology. The basic principles and prospects of fractal geometry in pathology are promising. In particular, fractal analysis is emerging as a powerful tool to perform differential diagnosis and prognosis of the patients in cancer and other malignancies as well to improve the effectiveness and safety of patient care. All fractal objects have Fractal Dimension FDs, commonly calculated with box counting. Morphometry, the measure of shapes of the structures, can be added to every imaging technique in order to obtain objective indexes. In this field, fractal analysis has been applied to histopathology, cytopathology, and electron microscopy with great success. Performing fractal analysis of tissue samples, it’s possible to make differential diagnosis between the early stage of tumors and flogosis, or among the different types of Basal Cell Carcinoma, and different grade of Invasive Bladder Carcinoma, well as to investigate the subtle alterations of the nuclear patterns in human breast tumors, or to evaluate other tumors.
THE CORRELATION BETWEEN STUDENTS HABIT IN READING ENGLISH AND VOCABULARY ACHIEVEMENT IN SMA UNISMUH MAKASSAR

This study aims to determine the correlation between students' reading habits in English and vocabulary achievement at the second grade (IPA) of SMA Unismuh Makassar. In this study, using the correlation description method, which consists of two instruments in collecting data, namely questionnaires and tests. This research was conducted in a class at SMA Unismuh Makassar where the sample chosen by the researcher was class two science consisting of 27 students in one class. The results in this study indicate that variable X to variable Y has a correlation with the degree of the correlation, namely a weak correlation in the form of a negative correlation. The negative correlation is the higher the variable X, the lower the Y variable and vice versa. The data analysis calculation of r is the lower r-table (-0.287 &lt;0.3809). This means that there is no significant correlation between students' reading habits in English and vocabulary achievement. In other words, hypothesis H0 is accepted and hypothesis H1 is rejected.
Unveiling Fakes: A Blockchain-Backed Approach for Product Authentication

Abstract: In recent years, the proliferation of counterfeit products has become a pervasive issue, posing significant challenges to consumers, businesses, and governments worldwide. Traditional methods of detecting fake products often fall short due to their reliance on centralized databases, which can be tampered with or manipulated. However, blockchain technology, with its decentralized and transparent nature, offers a promising solution for detecting fake products. This paper presents a novel approach for fake product detection using blockchain technology, leveraging its inherent properties of immutability, transparency, and distributed consensus. The proposed system utilizes a blockchain-based registry that securely records product information, such as serial numbers, manufacturing details, and supply chain data, at each stage of the product's lifecycle. Through smart contracts and cryptographic techniques, the system ensures that product information is authentic and cannot be tampered with. Moreover, the system allows consumers, businesses, and other stakeholders to easily verify the authenticity of a product by accessing the blockchain registry, thereby empowering them to make informed purchasing decisions. The paper also discusses the potential benefits and challenges of implementing blockchain-based fake product detection, including increased consumer trust, improved supply chain visibility, and enhanced brand protection, as well as considerations such as privacy, scalability, and interoperability. Overall, this paper contributes to the emerging field of blockchain applications in supply chain management and counterfeit detection, providing insights and recommendations for future research and development in this area.
Dynamic Resource Allocation in Cloud Environments

Abstract: The provisioning and use of computing resources has been completely transformed by cloud computing, which provides previously unheard-of levels of scalability, flexibility, and cost-effectiveness. In order to maximize resource utilizatio and guarantee peak performance in cloud settings, dynamic resource allocation is essential. In order to provide a thorough grasp of the most recent developments in this subject, this survey paper offers an in-depth review of dynamic resource allocation methods in cloud computing. The survey includes a wide range of dynamic resource allocation techniques, including as cost-optimization methods, resource provisioning and scheduling techniques, and load-balancing algorithms. Each method is examined in terms of its advantages, drawbacks, and applicability to particular cloud deployment scenarios. The research also investigates evaluation approaches and performance indicators for evaluating the efficacy of dynamic resource allocation systems. It emphasizes the need for continued research and innovation to address the evolving demands of cloud computing environments.
Classification by Frequent Association Rules

Over the last two decades, Associative Classifiers have shown competitive performance in the task of predicting class labels. Along with the performance in accuracy, associative classifiers produce human-readable predictive rules which is very helpful to understand the decision process of the classifiers. Associative classifiers from early days suffer from the limitation requiring proper threshold value setting which is dataset-specific. Recently some studies eliminated that limitation by producing statistically significant rules. Though recent models showed very competitive performance with state-of-the-art classifiers, their performance is still impacted if the feature vector of the training data is very large. An ensemble model can solve this issue by training each base learner with a subset of the feature vector. In this study, we propose an ensemble model Classification by Frequent Association Rules (CFAR) using associative classifiers as base learners. In our approach, instead of using a classical ensemble and a voting method, we rank the generated rules based on predominance among base learners and select a subset of the rules for predicting class labels. We use 10 datasets from the UCI repository to evaluate the performance of the proposed model. Our ensemble approach CFAR eliminates the limitation of high memory requirement and runtime of recent associative classifiers if training datasets have large feature vectors. Among the datasets we used, along with increasing accuracy in most cases, CFAR removes the noisy rules which enhances the interpretability of the model.
APPLICATION OF WEB-BASED APRIORI ALGORITHM FOR DRUG INVENTORY AT KHAIRI FARMA PHARMACY

Inventory has a very important role in increasing sales and service to consumers. The purpose of this study was to determine the information and sales patterns in the form of association rules at a certain period that can provide advice to the pharmacy in managing drug inventory. The algorithm used in this study is a priori to determine the results of sales patterns in the form of association rules. Association rules are obtained by implementing an apriori data mining algorithm to a website-based system using laravel and the resulting calculation results are in the form of Drug Association rules purchased simultaneously. With a minimum support value of 2, there are 214 items in 1 – the itemset that passes the minimum support and 9 association rules formed from all transactions of 519 data with a confidence value of more than 30%. From the resulting Association rules, there are Association rules with the highest confidence value of 66.67% in the form of ketotifen and cupanol pairs purchased simultaneously.
Semantic Segmentation: A Zoology of Deep Architectures

In this paper we review the evolution of deep architectures for semantic segmentation.The rst successful model was fully convolutional network (FCN) published in CVPR in 2015.Since then, the subject has become very popular and many methods have been published, mainly proposing improvements of FCN.We describe in detail the Pyramid Scene Parsing Network (PSPnet) and DeepLabV3, in addition to FCN, which provide a multi-scale description and increase the resolution of segmentation.In recent years, convolutional architectures have reached a bottleneck and have been surpassed by transformers from natural language processing (NLP), even though these models are generally larger and slower.We have chosen to discuss about the Segmentation Transformer (SETR), a rst architecture with a transformer backbone.We also discuss SegFormer, that includes a multi-scale interpretation and tricks to decrease the size and inference time of the network.The networks presented in the demo come from the MM-Segmentation library, an open source semantic segmentation toolbox based on PyTorch.We propose to compare these methods qualitatively on individual images, and not on global metrics on databases as is usually the case.We compare these architectures on images outside of their training set.We also invite the readers to make their own comparison and derive their own conclusions.
Design of a Spatially Anchoring Framework Using an Augmented Reality Head-Mounted Device and Fisheye Lens

&lt;p&gt;The development of improved Augmented Reality (AR) Head-Mounted Devices (HMDs) have led to many potential applications for augmented reality. In the case of surgery, an HMD could be used as an assistive tool to address some deficits that popular neuronavigation devices have, such as a disturbance to surgical workflow and surgeon comfort.&lt;/p&gt; &lt;p&gt;This thesis aims to bring an HMD-based overlay framework that can be used in the operating room. Through a combination of Android Studio, OpenCV, and OpenGL, a method to localize an HMD (ODG R9s) was created. This framework used a fisheye lens and a marker-based tracking method to localize the headset and renders a spatially-anchored 3D model in real time. A “Focus” mode was also developed in stabilizing the camera pose estimation and translational and rotational errors were quantified.&lt;/p&gt;
HSPM: A Better Model to Effectively Preventing Open-Source Projects from Dying

With the rapid development of Open-Source (OS), more and more software projects are maintained and developed in the form of OS. These Open-Source projects depend on and influence each other, gradually forming a huge OS project network, namely an Open-Source Software ECOsystem (OSSECO). Unfortunately, not all OS projects in the open-source ecosystem can be healthy and stable in the long term, and more projects will go from active to inactive and gradually die. In a tightly connected ecosystem, the death of one project can potentially cause the collapse of the entire ecosystem network. How can we effectively prevent such situations from happening? In this paper, we first identify the basic project characteristics that affect the survival of OS projects at both project and ecosystem levels through the proportional hazards model. Then, we utilize graph convolutional networks based on the ecosystem network to extract the ecosystem environment characteristics of OS projects. Finally, we fuse basic project characteristics and environmental project characteristics and construct a Hybrid Structured Prediction Model (HSPM) to predict the OS project survival state. The experimental results show that HSPM significantly improved compared to the traditional prediction model. Our work can substantially assist OS project managers in maintaining their projects’ health. It can also provide an essential reference for developers when choosing the right open-source project for their production activities.
FREDA: Flexible Relation Extraction Data Annotation

To effectively train accurate Relation Extraction models, sufficient and properly labeled data is required. Adequately labeled data is difficult to obtain and annotating such data is a tricky undertaking. Previous works have shown that either accuracy has to be sacrificed or the task is extremely time-consuming, if done accurately. We are proposing an approach in order to produce high-quality datasets for the task of Relation Extraction quickly. Neural models, trained to do Relation Extraction on the created datasets, achieve very good results and generalize well to other datasets. In our study, we were able to annotate 10,022 sentences for 19 relations in a reasonable amount of time, and trained a commonly used baseline model for each relation.
Deduplication vs Privacy Tradeoffs in Cloud Storage

Users often encrypt files they store on cloud storage services to ensure data privacy. Unfortunately, without additional mechanisms, encrypting files prevents the use of server-side deduplication as two identical files will be different when encrypted. Encrypted deduplication techniques combines file encryption and data deduplication. This combination usually requires some form of direct or indirect coordination between the different clients.
Extracting function relation from less than ideal digital curve

There are a lot of difficulties in the process of extracting function relationship from hard copy curve. Considering factors such as distortion, chromatic aberration, noise pollution and etc., a dense set of calibrated points near the real curve from the digitized image can be extracted out. Based on the fact that the function relationship corresponding to the curve can be expressed in the form of segmented Hermite polynomial function, a least squares model with outlier elimination strategy is presented, which can be solved to obtain the function parameters corresponding to the curve by iterative method. The simulation analysis shows that the difference between the obtained fitting function and the original function is positively related to the effective resolution of the digital image, and the algorithm provided in this paper is of high practical value.
CORPUS DRIVEN ANALYSIS OF MOTIVATIONAL SPEECHES WITH ANTCONC

Corpus linguistics is observed as a predominant approach to the investigation of a large amount of data. According to Biber et al. [2], corpus linguistics is based on the four cornerstones such as utilizing computer software, electronic databases, empirical method, and quantitative and qualitative analytical techniques for the interpretation of findings.
Paparan Gas Hidrogen Sulfida (H2S) dan Aktivitas Pemulung Terhadap Risiko Kesehatan Lingkungan di TPA Tahun 2022

Scavengers have the potential to be exposed to gases from the waste decomposition process at the final disposal area due to the activities of scavengers in the final disposal environment. Exposure to these gases can cause health problems for scavengers, one of which is due to Hydrogen Sulfide (H2S) gas. The purpose of the study was to determine the exposure of Hydrogen Sulfide (H2S) gas to scavengers at the Air Dingin final disposal area of Padang in 2022. The research method used Environmental Health Risk Analysis. The results of the three points measured were above the odor level limit value (0.02 ppm or 0.0278 mg/m3). The average realtime intake value is 1.55 x 10-3 mg/kg/day and the lifetime intake is 3.11 x 10-3 mg/kg/day. Realtime and lifetime RQ values, obtained RQ&gt;1. This means that exposure to Hydrogen Sulfide (H2S) is not safe for scavengers. For this reason, it is necessary to carry out risk control efforts. For scavengers to use PPE, especially masks, and increase body resistance. For the Department of Environment of Padang, recommended to be able to monitor air quality related to polluting gases in the air around the final disposal area on a regular basis.