Reviewerly/exHarmony huggingface dataset:
Augmented reality in 9th grade history: Student opinions on the usefulness and effectiveness of the material

Research problem/aim; The aim of this study is to determine the opinions of the 9th grade students who use teaching materials designed with augmented reality technology on the usefulness and effectiveness of the material. For this purpose, 3 activities were designed using 9th grade topics and a total of 27 AR materials were applied to the students in these activities. Method; In this study, "developmental research" and "case study" were used. As data collection tools, "AR application course observation form", "student evaluation form" and "3 AR activities" were designed by the researchers. Findings; 30 codes were formed and the answers given by the students to the questions and grouped as positive and negative by combining them under the theme of effectiveness, usefulness and suggestions which is the main question of the research. Conclusions; According to the results of the research; In the "usefulness " theme, the most repeated positive code is "functionality / interactive ", while the negative code is "unrealistic", In the theme of "effectiveness", the most repeated positive code was "providing 3D detection", while the negative code was "not contributing". In the theme of suggestions, the code "animation and graphics should be developed" was repeated the most.
An efficient certificateless signature scheme on lattice

The certificateless cryptography solves the certificate management and key escrow problems in the Internet of Things. However, these schemes are no longer safe in the quantum environment. Lattice cryptography has become the main method to solve quantum security. At present, only a few lattice-based certificateless signature schemes have been proposed and the use of the trapdoor function limits the efficiency of the lattice cryptography, and the security of these schemes can only reach existential unforgeability. We construct a lattice-based certificateless signature scheme based on Lyubashevsky's signature scheme. The security of our scheme can reach strong unforgeability and the computational efficiency of our scheme is lower than the previous schemes.
Lightweight object tracking algorithm based on sparse attention

In recent years, with the widespread application of Transformer in computer vision, Transformer-based object tracking algorithms have made some progress in terms of accuracy. However, the large computation, high resource consumption, and slow inference speed of these algorithms severely limit their practical applications. Specifically, these algorithms struggle to meet the real-time tracking demands of resource-limited scenarios such as mobile devices and drones. Therefore, this paper proposes a pure Siamese-based, lightweight object tracking algorithm based on sparse attention. The proposed algorithm significantly improves tracking speed without significantly sacrificing tracking accuracy, making it suitable for practical resource-limited scenarios while still achieving good tracking performance. The proposed algorithm achieves a success rate of 76.3% and a normalized precision of 82.1% on the TrackingNet dataset, at the same time, it achieves a high inference speed of more than 100FPS, which exceeds the mainstream algorithm.
Time-Stage Model of Failure

In the past few decades, failure has attracted more attention as people realize that experiencing and learning from failure is an essential part of achieving success. In light of this, I propose a time-stage model of failure, which describes and predicts the processes and responses at three stages of failure: In the first stage, individuals experience immediate emotional pain and a sense of threat to their fundamental needs upon detecting failure; in the second stage, individuals reflect on the significance of their failure and develop different motives to recover from the threatened needs. In the third stage, prolonged exposure to failure drains individuals’ resources and leads them to believe that they are destined for continuous failure. This can result in compliance, avoidance, and a decrease in self-efficacy. Finally, we call for more research to explore the factors and psychological mechanisms that affect the individuals’ response to failure, so that more strategies can be developed to help individuals recover from short-term failures and minimize the negative consequences of long-term failure.
“Lakshya” A Novel Virtual Laboratory based on Django framework to conduct experiments in the area of Machine Learning, Signal processing and Communication

Abstract Practical laboratories are essential for advancing students' professional aspirations because they allow them to learn by doing. The paper proposes creating a virtual lab(V-lab) to improve students' learning and observational skills and prepare them for the workplace. Excellent instructions on how to carry out experiments with legitimate client inputs are provided by V-lab. Students can perform experiments in the fields of machine learning, signal processing, and digital communication by visiting to the website https://lakshya.vcetputtur.ac.in. The intelligent web-server designed had been used frequently by third year Electronics and Communication students of VCET, Puttur to conduct Digital Signal Processing Laboratory and Advanced Communication Laboratory experiments. Final year undergraduate students pursuing projects in the area of ML found this website helpful, as it provided information about Classification report, Confusion matrix and Accuracy in one step. A Comparative study of various ML algorithms (Available in V-Lab) applied on Iris flower and Lung cancer dataset is provided in results and discussion section to justify the use of V-Lab in ML area. Results shows RBF based SVM classifier produced best accuracy when applied on iris flower dataset. Logistic regression classifier produced superior results compared to other ML algorithms when applied on lung cancer symptoms dataset. The website does not require sign-up credentials, free to use.
Speech Acts in Teaching Learning Activities at Tourism Vocational High Schools, Denpasar

The title of this research is "Speech Acts in Teaching Learning Activities at Tourism Vocational High Schools, Denpasar".This study is aimed at finding and describing teachers' speech acts at Denpasar Tourism Vocational School.English Learning in Vocational High Schools to develop student's communication skills in conveying opinion, developing knowledge, technology, and also culture.There are four English language skills (listening, speaking, reading, and writing) and among those, it is necessary to master speaking as the skill is considered to have the utmost importance in language learning.Therefore, by learning to speak English, students obtain valuable life skills it may prove useful in their future and assist them to make contributions to their communities and country.The theories utilized in this study were the theory by Yule (1996) on the types of speech acts and the theory by Searle (1969) used to analyze the functions of speech acts.The data were analyzed qualitatively.The method used in this research was observation method.The techniques used to collect the data were divided into: note taking and video recording.To analyze the data, the teacher's utterances when teaching in class were recorded and then transcribed.Afterward, the transcripts were analyzed in detail to find out the types and functions of their speech acts and they were classified by the related theories.In this research, 2 types of speech acts were found: direct speech acts and indirect speech acts.There are also 5 functions of speech acts, namely: stating, commanding, thanking, promising, and forcing.Three meanings of speech acts were found, namely: llocutionary speech acts, illocutionary speech acts, and perlocutionary speech acts.
Super-resolution Model-based Versatile Video Coding for Light Field Video

In this paper, we propose a novel light field (LF) super-resolution model for a downsampling-based LF video coding framework by introducing a feature selection and shuffling mechanism based on our super-resolution model. It is shown to achieve a good performance on the downsampling-based LF video coding framework benefiting from its ability for learning of the most useful features and effective removal of the artifact caused by the VVC encoding and downsampling process. The proposed scheme is shown to achieve BD-rate savings of about 39% and 28% on AI-Main and RA-Main conditions for LF video coding against the anchor of VVC.
Hierarchical clustering algorithm for dendrogram construction and cluster counting

The article provides a comprehensive overview of hierarchical clustering and dendrogram construction, with a focus on the methods used for determining the optimal number of clusters.The article discusses the theoretical foundations of hierarchical clustering and the process of constructing dendrograms, and goes on to describe several popular methods for determining the number of clusters.The article focused on both divisive and agglomerative clustering methods and the dendrogram, the advantages and disadvantages of each method, and how dendrograms are used to visualize the results of hierarchical clustering.It also provides comparison of hierarchical clustering with non-hierarchical clustering, particularly the K-means algorithm, and discusses their respective advantages and disadvantages.One of the key advantages of hierarchical clustering is that it does not require the user to specify the number of clusters in advance, as is the case with non-hierarchical clustering.Instead, a dendrogram can be used to determine the appropriate number of clusters.The article concludes by noting the usefulness of hierarchical clustering for a range of applications, particularly in exploratory data analysis.The article also covers the main methods to identify which objects and clusters are most similar.Additionally, the article provides an overview of the K-means clustering method and compares it to hierarchical clustering.
Decision Making System for Visitor Satisfaction of Labuhanbatu Library Using K-Means Method

The K-Means research technique is used in this study. By using Satisfaction Variables and visits to college libraries, this research was conducted at the Labuhanbatu library. Researchers used a sample of 35 visitors for this study, which were tested using the Rapidminer data processing application. Where the results of this study showed the results of 18 people were satisfied and 17 people were neutral. Where it is stated that most visitors are satisfied with library services.
Retweet Predictions Regarding COVID-19 Vaccination Tweets through The Method of Multi Level Stacking

The rapid development of technology from day to day indirectly influences increasing social media use. This can be seen from spreading information that is very easily found on social media, one of which is Twitter. It is one of the most popular platforms for expressing people’s feelings by tweeting and interacting with other users at the same time. Various opinions about the COVID-19 vaccination began to be discussed on the Twitter platform. Moreover, most people take advantage of the feature available on Twitter, namely retweets. Users do retweet because there are many influencing factors. It can be caused by a reason that they have the same opinions and thoughts as the tweet owner, and so on. A retweet feature is also a form of information diffusion on the Twitter platform. The diffusion of information on Twitter has several factors, such as the most influential users, using hashtags or URLs, and others. In this conclusion, retweet predictions have been carried out regarding COVID-19 vaccination tweets using the features user-based and time-based through the Multi-Level Stacking classification method. This method indicates the best results when oversampling with an F1-Score of 96.23%.
Fake News (Hoaxes) Detection on Twitter Social Media Content through Convolutional Neural Network (CNN) Method

The use of social media is very influential for the community. Users can easily post various activities in the form of text, photos, and videos in social media. Information on social media contains fake news and hoaxes that will have an impact on society. One of the most social media used is Twitter. This study aims to detect fake news found on the Tweets using the Convolutional Neural Network (CNN) method by comparing the weighting features used of the Term Frequency Inverse Document Frequency (TF-IDF) and the Term Frequency-Relevance Frequency (TF-RF). The highest accuracy was obtained in the Term Frequency-Relevance Frequency (TF-RF) weighting feature with an accuracy of 84.11%, while in the Term Frequency Inverse Document Frequency (TF-IDF) weighting feature with an accuracy of 80.29%.
Lagash Archaeological Survey and Recording System (LASRS) - Rectangular Survey Grid Creation with QGIS v1

Tools available in the open-source GIS program QGIS can be used to create a rectangular grid for archaeological surveys within irregularly-shaped boundaries. This protocol describes the steps necessary for generating the grid, trimming it to the desired survey area, and uniquely labeling the nodes generated.
Website-Based Development of Learning Management System using V-Development Method Model (Case Study: UNRI, Informatics Engineering Study Program)

Developers often use a software development model, namely the waterfall or classical models. Waterfall uses a systematic approach, so it tends to be archaic and will cause significant problems. The problems appeared when the process at the previous stage experiences happened, so the process could not go to the next stage. In addition, the tests were carried out for the solution to the problem. There was a need for a v-model development model. This model is the waterfall model development, then described in the form of v, and there was testing in each phase. This V-Model is an excellent model because it has good illustrations and clarity. The Website Learning Management System is built using the PHP programming language. Based on the results of the testing unit, the software used whitebox, and all units could run 100% well. Meanwhile, integration testing and testing systems using black boxes were 100% running well. Acceptance testing using ISO 25010 consisted of eight characteristics: functionality, efficiency, compatibility, usability, reliability, maintainability, security, and portability. These aspects could run well.&#x0D;
Lagash Archaeological Survey and Recording System (LASRS) - Creating Collection Forms with ArcGIS Field Maps v1

By carefully creating a mobile GIS data collection routine for ArcGIS Field Maps, in-field collection of archaeological survey data can be sped up significantly. This protocol outlines how we created such a routine for the Lagash Archaeological Project.
Sentence embedding approach using LSTM auto-encoder for discussion threads summarization

Online discussion forums are repositories of valuable information where users interact and articulate their ideas and opinions, and share experiences about numerous topics. These online discussion forums are internet-based online communities where users can ask for help and find the solution to a problem. A new user of online discussion forums becomes exhausted from reading the significant number of irrelevant replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) model as a sentence embedding tool, which is poor at capturing the overall meaning of the sentence and is unable to grasp word dependency. To overcome these limitations, we introduce the LSTM Auto-encoder as a sentence embedding technique to improve the performance of DTS. The empirical result in the context of the proposed approach?s average precision, recall, and F-measure with respect to ROGUE-1 and ROUGE-2 of two standard experimental datasets demonstrates the effectiveness and efficiency of the proposed approach and outperforms the state-of-the-art CBOWmodel in sentence embedding tasks and boost the performance of the automated DTS model.
Summarization of Massive RDF Graphs Using Identifier Classification

The size of massive knowledge graphs (KGs) and the lack of prior information regarding the schemas, ontologies and vocabularies they use frequently makes them hard to understand and visualize. Graph summarization techniques can help by abstracting details of the original graph to produce a reduced summary that can more easily be explored. Identifiers often carry latent information which could be used for classification of the entities they represent. Particularly, IRI namespaces can be used to classify RDF resources. Namespaces, used in some RDF serialization formats as a shortening mechanism for resource IRIs, have no role in the semantics of RDF. Nevertheless, there is often a hidden meaning behind the decision of grouping resources under a common prefix and assigning an alias to it. We improved on previous work on a namespace-based approach to KG summarization that classifies resources using their namespaces. Producing the summary graph is fast, light on computing resources and requires no previous domain knowledge. The summary graph can be used to analyze the namespace inter-dependencies of the original graph. We also present chilon, a tool for calculating namespace-based KG summaries. Namespaces are gathered from explicit declarations in the graph serialization, community contributions or resource IRI prefix analysis. We applied chilon to publicly available KGs, used it to generate interactive visualizations of the summaries, and discuss the results obtained.
Multi-Feature and Multi-Channel GCNs for Aspect Based Sentiment Analysis

Aspect Based Sentiment Analysis (ABSA) is a challenging task in natural language processing that involves identifying the sentiment polarity of different aspects in a given text. While graph convolutional network (GCN) has been shown to achieve state-of-the-art results for ABSA, existing methods typically rely on investigating a single type of feature in the sentence to construct the graph representation, which may not capture enough relevant information. In this paper, we propose an approach that leverages multiple channels to extract multiple features, including aspect relations, word dependency relation types, and semantic information, to enhance the performance of the model. We evaluate our approach on four benchmark datasets and demonstrate its validity, achieving state-of-the-art results. We also conduct extensive ablation studies to analyze the contribution of different components of our model. Our findings suggest that the combination of multiple channels and multiple features GCN is crucial for achieving the best performance in ABSA tasks. In conclusion, our proposed approach provides a promising solution to the ABSA problem and contributes to advancing the field by highlighting the importance of considering multiple features and leveraging the power of multiple channels GCN to improve performance.
PENERAPAN ALGORITMA A* PADA WEBGIS PENCARIAN RUTE TERPENDEK

Geographic information systems have been able to solve routing problems such as TSP (Travelling Salesman Problems) or finding the closest route. GIS has developed a lot into GIS applications, both in the form of desktop, web, and mobile. This research develops a webGIS application that can provide a solution for tourists who have difficulty reaching a furniture showroom in Jepara City. The method used is the A* algorithm. The algorithm can calculate existing nodes through heuristic calculations. Data from google maps are converted into a grid form, then calculations are carried out. This research produces an alternative shortest route, while the accuracy of Google Maps road data affects the displayed route search.
Ensemble of top3 prediction with image pixel interval method using deep learning

Computer vision (CV) has been successfully used in picture categorization applications in various fields, including medicine, production quality control, and transportation systems. CV models use an excessive number of photos to train potential models. Considering that image acquisition is typically expensive and time-consuming, in this study, we provide a multistep strategy to improve image categorization accuracy with less data. In the first stage, we constructed numerous datasets from a single dataset. Given that an image has pixels with values ranging from 0 to 255, the images were separated into pixel intervals based on the type of dataset. The pixel interval was split into two portions when the dataset was grayscale and five portions when it was composed of RGB images. Next, we trained the model using both the original and newly constructed datasets. Each image in the training process showed a non-identical prediction space, and we suggested using the topthree prediction probability ensemble technique. The top three predictions for the newly created images were combined with the corresponding probability for the original image. The results showed that learning patterns from each interval of pixels and ensembling the top three predictions significantly improve the performance and accuracy, and this strategy can be used with any model.
Sustainable Energy Consumption in the Industry 4.0 era

Worsening environmental degradation, depletion of fossil fuels reserves, and fuel price volatility have led to increased concerns about getting adequate energy to power the rapidly growing industrial sector and other uses.The cost of energy is a major contributor to the rising cost of production and a source of worry to both the producers and consumers alike.Investigation into devising sustainable, accessible, and affordable energy sources to power the manufacturing sector and other consumers has engaged the attention of researchers and other stakeholders in recent decades.The current research, therefore, investigates techniques for ensuring sustainable energy consumption in the Industry 4.0 era.Results show that effective implementation of energy saving, cost reduction, and green manufacturing measures will lower energy costs, benefit the environment and result in a high return on invested capital.The application of novel intelligent technologies like cyber-physical systems, industrial internet of things, artificial intelligence, big data, cloud computing, smart metering, and other components of Industry 4.0 will contribute to achieving sustainable energy utilization in the manufacturing sector.Going forward, more targeted investigations are required to cultivate realistic and implementable models for achieving affordable, environmentally benign, and sustainable energy for the manufacturing sector.
Employees Information System Misuse: Insights from National culture and Criminological Theories

IS resources are expected to promote competitiveness and profitability, however, employees' misuse causes considerable security threats.To that, global organizations are investing evermore to defend their cyberspaces, but they are not able to fully protect them.Cultural factors contributed to influence employees' behaviours, but studies are lacking in Africa.So, developing a conceptual research model that comprising cultural dimensions and criminological theories will probably explain the factors that help to design socio-culturally appropriate countermeasures in Ethiopia.