Reviewerly/exHarmony huggingface dataset:
Cuckoo Search Algorithm: Review and its Application

Optimization techniques play a major role in real-world problems, As many cases in which decisions are made are based on random research. But choosing the optimization technology is a big challenge for the user. The cuckoo search algorithm is one of the modern optimization techniques that can replace many of the traditional techniques used, The cuckoo bird use a Levi's flight strategy based on the egg laying method to extract a solution to the problem. The presented work will provide a review of the original cuckoo search algorithm with mentioning some types of developed and hybrid cuckoo search algorithm as well as some of their applications.
Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions

Qualitative research provides methodological guidelines for observing and studying communities and cultures on online social media platforms. However, such methods demand considerable manual effort from researchers and can be overly focused and narrowed to certain online groups. This work proposes a complete solution to accelerate the qualitative analysis of problematic online speech, focusing on opinions emerging from online communities by leveraging machine learning algorithms. First, we employ qualitative methods of deep observation for understanding problematic online speech. This initial qualitative study constructs an ontology of problematic speech, which contains social media postings annotated with their underlying opinions. The qualitative study dynamically constructs the set of opinions, simultaneous with labeling the postings. Next, we use keywords to collect a large dataset from three online social media platforms (Facebook, Twitter, and Youtube). Finally, we introduce an iterative data exploration procedure to augment the dataset. It alternates between a data sampler --- which balances exploration and exploitation of unlabeled data --- the automatic labeling of the sampled data, the manual inspection by the qualitative mapping team, and, finally, the retraining of the automatic opinion classifiers. We present both qualitative and quantitative results. First, we show that our human-in-the-loop method successfully augments the initial qualitatively labeled and narrowly focused dataset and constructs a more encompassing dataset. Next, we present detailed case studies of the dynamics of problematic speech in a far-right Facebook group, exemplifying its mutation from conservative to extreme. Finally, we examine the dynamics of opinion emergence and co-occurrence, and we hint at some pathways through which extreme opinions creep into the mainstream online discourse.
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization

Given thousands of equally accurate machine learning (ML) models, how can users choose among them? A recent ML technique enables domain experts and data scientists to generate a complete Rashomon set for sparse decision trees-a huge set of almost-optimal inter-pretable ML models. To help ML practitioners identify models with desirable properties from this Rashomon set, we develop Tim-bertrek, the first interactive visualization system that summarizes thousands of sparse decision trees at scale. Two usage scenarios high-light how Timbertrek can empower users to easily explore, compare, and curate models that align with their domain knowledge and values. Our open-source tool runs directly in users' computational notebooks and web browsers, lowering the barrier to creating more responsible ML models. Timbertrek is available at the following public demo link: https: //poloclub. github. io/timbertrek.
GlideNet: Global, Local and Intrinsic based Dense Embedding NETwork for Multi-category Attributes Prediction

Attaching attributes (such as color, shape, state, action) to object categories is an important computer vision problem. Attribute prediction has seen exciting recent progress and is often formulated as a multi-label classification problem. Yet significant challenges remain in: 1) predicting a large number of attributes over multiple object categories, 2) modeling category-dependence of attributes, 3) methodically capturing both global and local scene context, and 4) robustly predicting attributes of objects with low pixel-count. To address these issues, we propose a novel multi-category attribute prediction deep architecture named GlideNet, which contains three distinct feature extractors. A global feature extractor recognizes what objects are present in a scene, whereas a local one focuses on the area surrounding the object of interest. Meanwhile, an intrinsic feature extractor uses an extension of standard convolution dubbed Informed Convolution to retrieve features of objects with low pixel-count utilizing its binary mask. GlideNet then uses gating mechanisms with binary masks and its self-learned category embedding to combine the dense embeddings. Collectively, the Global-Local-Intrinsic blocks comprehend the scene's global context while attending to the characteristics of the local object of interest. The architecture adapts the feature composition based on the category via category embedding. Finally, using the combined features, an interpreter predicts the attributes, and the length of the output is determined by the category, thereby removing unnecessary attributes. GlideNet can achieve compelling results on two recent and challenging datasets - VAW and CAR -for large-scale attribute prediction. For instance, it obtains more than 5% gain over state of the art in the mean recall (mR) metric. GlideNet's advantages are especially apparent when predicting attributes of objects with low pixel counts as well as attributes that demand global context understanding. Finally, we show that GlideNet excels in training starved real-world scenarios.
Analisis Gerakan Literasi Sekolah di Sekolah Dasar

The purpose of this study was to determine the implementation of the School Literacy Movement, the obstacles to implementing the School Literacy Movement and also efforts to overcome the obstacles to the implementation of the School Literacy Movement in SD Negeri 01 Banjarejo, Karanganyar District, Pekalongan Regency. This study uses a qualitative method with a descriptive approach. The data collection procedure in this study used interviews, questionnaires, observation, and documentation techniques. The results showed that the implementation of the School Literacy Movement at SD Negeri 01 Banjarejo had been going well. Three stages of school literacy, namely the habituation stage, the development stage, and the learning stage have been fulfilled, one of which is reading 15 minutes before learning. The obstacles to implementing the School Literacy Movement include time constraints because face-to-face learning is limited and students' enthusiasm for reading and visiting the library is still lacking. Efforts to overcome obstacles in implementing the School Literacy Movement are by implementing reading strategies, giving awards, and providing motivation and facilities for students. Based on the results of filling out student questionnaires, on average students can read fluently, interest in reading is quite high, reading activities 15 minutes before class run regularly, students are more interested in reading picture books, and students are able to find learning resources independently.
Ransomware – A Growing Threat for Law Firms

In recent years, ransomware virus software has become an immediate threat to corporate and individual data files. Hundreds of millions of dollars are lost every year to this malware, and new versions appear frequently, making it possible to bypass antivirus programs. This article aims not only to draw attention to the ransomware phenomenon and provide an action plan for law firms in the event of a cyber-attack, but also to address the Kaspersky antivirus debate in light of recent events in Ukraine.
DR.VIC: Decomposition and Reasoning for Video Individual Counting

Pedestrian counting is a fundamental tool for under-standing pedestrian patterns and crowd flow analysis. Existing works (e.g., image-level pedestrian counting, cross-line crowd counting et al.) either only focus on the image-level counting or are constrained to the manual annotation of lines. In this work, we propose to conduct the pedes-trian counting from a new perspective - Video Individual Counting (VIC), which counts the total number of individual pedestrians in the given video (a person is only counted once). Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame. Then, an end-to-end Decomposition and Reasoning Network (DRNet) is designed to predict the initial pedestrian count with the density estimation method and reason the new pedestrian's count of each frame with the differentiable optimal transport. Extensive experiments are conducted on two datasets with congested pedestrians and diverse scenes, demonstrating the effectiveness of our method over baselines with great superiority in counting the individual pedestrians. Code: https://github.com/taohan10200/DRNet.
Developing an App for Improving Access to COVID-19 Information in Underserved Communities

The deployment of mobile health (mHealth) apps can transform healthcare in rural and remote communities worldwide.Rural communities in Zimbabwe have limited access to information that affects their health, economic and social being due to structural and social barriers related to the inaccessibility of traditional media.mhealth apps are a valuable tool to monitor disease outbreaks and provide preventative information to the public.Lack of access to COVID-19 information results in high fatalities and public panic, and it is critical to publish reliable and timely information.The study's objective was to demonstrate the utility of a mHealth app prototype developed to enhance access to COVID-19 information in rural and remote communities in Zimbabwe.The prototype provides COVID-19 information such as statistics, preventative measures, self-diagnostics, social distancing information, and general hygiene to rural communities with limited access to official information channels on the pandemic.A design science research methodology was used to design, build and evaluate the COVID-19 mHealth app and fulfil the study's objectives.Thirty potential users participated in the evaluation of the prototype.The evaluation results show that potential users perceived that the prototype was useful, engaging, easy to learn, well designed, and provided relevant information.A strong correlation was observed between the design, engagement, functionality, and learnability.More widespread usability and more representative tests should be conducted to ascertain the efficacy and usability of the app.The study contributes literature on usability studies in developing countries.As more mHealth apps are being developed and deployed, more usability tests will be required to ensure that they are fit for purpose.The paper provides a baseline for developing related health information apps.Policymakers, health practitioners, technologists, and scholars can further investigate the deployment of digital technologies to improve healthcare and control the transmission and spread of COVID-19.
WebART: Web-Based Augmented Reality Learning Resources Authoring Tool and Its User Experience Study Among Teachers

Augmented reality (AR) has been incorporated into the educational context for years. Numerous studies have demonstrated the effectiveness of AR in different disciplines, learning styles, and learning environments. However, few studies have focused on the problem of generating AR applications in authentic educational contexts. In this study, an AR learning resource authoring tool called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WebART</i> was designed and developed to help teachers with no prior programming experience create and share AR learning resources. This study collected feedback and opinions on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WebART</i> from 51 K-12 teachers through questionnaires and interviews. Multiple linear regression was used to measure the relationships between teachers’ intentions to use technology and the user experience of the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WebART</i> tool. The questionnaire results revealed that multiple variables in teachers’ intentions are predictive of user experience. Interview results showed that teachers had a positive attitude toward the AR learning resources authoring tools inspiring novel ways of teaching and learning.
On the Infrastructure Providers That Support Misinformation Websites

In this paper, we analyze the service providers that power 440 misinformation and hate sites, including hosting platforms, domain registrars, CDN providers, DDoS protection companies, advertising networks, donation processors, and e-mail providers. We find that several providers are disproportionately responsible for serving misinformation websites, most prominently Cloudflare. We further show that misinformation sites disproportionately rely on several popular ad networks and payment processors, including RevContent and Google DoubleClick. When misinformation websites are deplatformed by hosting providers, DDoS protection services, and registrars, sites nearly always resurface through alternative providers. However, anecdotally, we find that sites struggle to remain online when mainstream monetization channels are severed. We conclude with insights for infrastructure providers and researchers working to stem the spread of misinformation and hate content.
Organizational Culture as a mediator between Employee Dissatisfaction and Turnover Intention

The purpose of the study is to examine whether and how organizational culture are associated with job dissatisfaction and turnover intention among firm employees in Kurdistan region of Iraq. Ninety-two workers participated in the study after being selected at random. The 100 employees from various companies were selected at random based on the total student body size at each school to receive the survey. In order to get the survey responses ready for statistical analysis, they were cleaned, coded, and put into an Excel matrix. The findings revealed that organizational culture has a significant mediation role between employee dissatisfaction and turnover intention. The test results returned both direct and indirect effects for all the relationships, which indicated only partial mediation in all the tested relationships. The results provided organisations’ insight into the possible consequences of firms in Kurdistan region of Iraq on employees and the organisations’ culture.
A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees

Classification is essential to the applications in the field of data mining, artificial intelligence, and fault detection. There exists a strong need in developing accurate, suitable, and efficient classification methods and algorithms with broad applicability. Random forest is a general algorithm that is often used for classification under complex conditions. Although it has been widely adopted, its combination with diverse fuzzy theory is still worth exploring. In this paper, we propose the intuitionistic fuzzy random forest (IFRF), a new random forest ensemble of intuitionistic fuzzy decision trees (IFDT). Such trees in forest use intuitionistic fuzzy information gain to select features and consider hesitation in information transmission. The proposed method enjoys the power of the randomness from bootstrapped sampling and feature selection, the flexibility of fuzzy logic and fuzzy sets, and the robustness of multiple classifier systems. Extensive experiments demonstrate that the IFRF has competitative and superior performance compared to other state-of-the-art fuzzy and ensemble algorithms. IFDT is more suitable for ensemble learning with outstanding classification accuracy. This study is the first to propose a random forest ensemble based on the intuitionistic fuzzy theory.
Research on the Language Characteristics of Agricultural English and Its Translation Strategies

In recent years, with the continuous advancement of economic globalization, transnational agricultural cooperation has been on rise at a gradual manner, and English plays an important role in it. Based on the increasingly frequent transnational agricultural cooperation and the increasing demand for agricultural English translation, this paper analyzes the linguistic characteristics of agricultural English and the difficulties in agricultural English translation, and puts forward some strategies for agricultural English translation in order to provide reference for agricultural English translation and serve the transnational agricultural cooperation.
Sentiment Analysis of Facebook Posts through Special Reactions: The Case of Learning from Home in Indonesia Amid COVID-19

In contrast to several other countries, Indonesian sentiment analysis research is primarily focused on the text-based analysis of Twitter. Given that Twitter users in Indonesia account for less than a seventh of those on Facebook, sentiment analysis on the latter may have a greater impact than on the former. This research sought to close that gap in the literature by pioneering the use of Facebook special reactions as an alternative to text-based sentiment analysis on social media posts about Indonesian social issues. The topic of learning from home in the midst of the COVID-19 pandemic was chosen because it is both timely and relatable to almost everyone in the country. Through CrowdTangle, a total of 39,657 Facebook posts containing the key phrase "belajar dari rumah" were gathered, but only 9,310 of them received special reactions and thus remained to be analyzed quantitatively. The results indicated that with the exception of 'love,' all special reactions are somewhat correlated, suggesting that they can be used to indicate the negative valence of a Facebook post. Further analysis revealed a significant increase in the proportion of posts with a negative valence during the second year of the COVID-19 pandemic. The textual analysis of the posts revealed that those with a negative valence primarily discuss internet access and other IT infrastructure issues that presumably impede learning from home activities for some. The main contribution of this study is to demonstrate how to analyze special reactions on Facebook for sentiment analysis purposes, particularly in the context of Indonesia. Additionally, it lays out how Facebook's special reactions have the potential to be used in conjunction with text-based sentiment analysis to provide a complete picture of the social issue being investigated.
Pattern Database Heuristics for Fully Observable Nondeterministic Planning

When planning in an uncertain environment, one is often interested in finding a contingent plan that prescribes appropriate actions for all possible states that may be encountered during the execution of the plan. We consider the problem of finding strong cyclic plans for fully observable nondeterministic (FOND) planning problems. The algorithm we choose is LAO*, an informed explicit state search algorithm. We investigate the use of pattern database (PDB) heuristics to guide LAO* towards goal states. To obtain a fully domain-independent planning system, we use an automatic pattern selection procedure that performs local search in the space of pattern collections. The evaluation of our system on the FOND benchmarks of the Uncertainty Part of the International Planning Competition 2008 shows that our approach is competitive with symbolic regression search in terms of problem coverage, speed, and plan quality.
Digital competence of higher education applicants: new opportunities and challenges for future education

As a result of the spread of the Covid-19 pandemic and the introduction of distance learning, the issue of digital competence, which both modern educators and students should possess, has become especially relevant. Digital competence is important in the education system, because having it, students can adequately respond to modern global challenges and transformations. The study aims to analyze the state of digital competence of higher education applicants through the prism of modern challenges and opportunities. The work is based on the use of general theoretical and empirical research methods. Analysis, synthesis, abstraction, and concretization were important among theoretical methods. Based on the predictive method of research the main ways of digital competence formation and specifics of future use were characterized. At the same time, to determine the level of digital competence, a questionnaire and survey of students based on digital literacy diagnostic techniques were conducted. The results considered the problem of theoretical justification of the digital competence phenomenon, offered their own understanding of this term, determined the structure of formation of basic competencies in higher education applicants, based on the use of modern techniques, characterized the state of digital literacy of students. The results also indicate that mostly the respondents of the experiment had an average level of digital competence. Despite this, many respondents were not fully acquainted with the manipulative possibilities of information resources. The practical value of the work lies in the development of practical recommendations that can be applied in further work to improve the level of mastery of digital competence.
GeoRefine: Self-supervised Online Depth Refinement for Accurate Dense Mapping

We present a robust and accurate depth refinement system, named GeoRefine, for geometrically-consistent dense mapping from monocular sequences. GeoRefine consists of three modules: a hybrid SLAM module using learning-based priors, an online depth refinement module leveraging self-supervision, and a global mapping module via TSDF fusion. The proposed system is online by design and achieves great robustness and accuracy via: (i) a robustified hybrid SLAM that incorporates learning-based optical flow and/or depth; (ii) self-supervised losses that leverage SLAM outputs and enforce long-term geometric consistency; (iii) careful system design that avoids degenerate cases in online depth refinement. We extensively evaluate GeoRefine on multiple public datasets and reach as low as $$5\%$$ absolute relative depth errors.
Image Encryption via Complementary Embedding Algorithm and New Spatiotemporal Chaotic System

Although image encryption is developing rapidly, it lacks the pertinence of specific scenarios. This paper proposes a complementary embedding encryption strategy. The strategy firstly identifies the airport area, then replaces the optimal similar area and embeds the airport image into a random position through the complementary embedding algorithm. In the encryption stage, to generate a better key stream, we propose an improved sine cross coupled mapping lattice (ISCCML). Comprehensive performance analysis shows that ISCCML has a larger parameter space and better chaotic cryptographic properties. Furthermore, a fractal disordered matrix (FDM) with iterative and out-of-order properties is presented for the simultaneous scrambling diffusion of images. In particular, the encryption algorithm has generalization and is also suitable for ordinary image encryption. Our results indicate that the proposed scheme can avoid repeated encryption on the premise of ensuring the security of important information; at the same time, the security analysis shows that our algorithm has security, practicality and scalability.