Reviewerly/exHarmony huggingface dataset:
Towards a Multi-Agent Simulation of Cyber-attackers and Cyber-defenders Battles

As cyber-attacks show to be more and more complex and coordinated, cyber-defenders strategy through multi-agent approaches could be key to tackle against cyberattacks as close as entry points in a networked system. This paper presents a Markovian modeling and implementation through a simulator of fighting cyber-attacker agents and cyberdefender agents deployed on host network nodes. It aims to provide an experimental framework to implement realistically based coordinated cyber-attack scenarios while assessing cyberdefenders dynamic organizations. We abstracted network nodes by sets of properties including agents' ones. Actions applied by agents model how the network reacts depending in a given state and what properties are to change. Collective choice of the actions brings the whole environment closer or farther from respective cyber-attackers and cyber-defenders goals. Using the simulator, we implemented a realistically inspired scenario with several behavior implementation approaches for cyber defenders and cyber-attackers.
Clap-Breathe-Count: Using Ice-Breaking Ma-Te-Ma-Ti-Ka to Increase High School Students' Learning Motivation

Creating a conducive learning atmosphere is an important factor in getting full attention from students. Suppose the atmosphere in the classroom could be more conducive. In that case, it will cause students to feel bored, tired, not focused on the teacher, sleepy, and even talk to their classmates, especially when students appear to show anxiety about learning. Ice Breaking is an exciting learning media that prioritizes innovative, creative, and futuristic elements. This research aims to determine the use of the icebreaker method to increase student motivation in class XI high school mathematics subjects. The research design used was pre-experimental, namely a one-group design with pre-test and post-test (single-group pre-test and post-test design). The subjects of this research were 36 students using random quota data collection techniques. The data analysis used in this research is the product-time correlation test. The statistical technique used is Pearson product-moment correlation. The data collection tool used was the icebreaker questionnaire. The research results show that icebreaking learning significantly affects students' mathematics motivation.
Sentiment Analysis of Maxim Online Transportation App Reviews using Support Vector Machine (SVM) Algorithm

The continuous emergence of online transportation service platforms is one of the effects of the ever-increasing technological advancements. One such online transportation service application, Maxim, has recently been slowly gaining ground in the ride-hailing market in Indonesia. According to data collected by one media outlet in 2022, Maxim ranks third as the most preferred online transportation platform by the public, following Gojek and Grab. This suggests that there are factors causing users to lack interest in or hesitate to use the Maxim application. On the Google Play Store, user ratings (in numerical values) and written reviews serve as reasons for the potential users lack of interest. Analyzing ratings alone is less accurate and does not provide in-depth information and meaning regarding users experiences. To understand user opinions about Maxim's service and functionality, an analysis of user reviews is crucial. Therefore, this research conducts sentiment analysis on Maxim user reviews using the Support Vector Machine (SVM) algorithm to classify reviews quickly. The reviews are categorized into two classes: positive and negative sentiment. The classification process is carried out in three scenarios with different data training and testing ratios: 60:40, 70:30, and 80:20, using a Linear kernel and hyperparameter optimization with GridSearch. The best accuracy is achieved with a 70:30 ratio, which is 89.82%. Evaluation using the confusion matrix also yields a precision of 92.66%, recall of 94.09%, and an F1 score of 93.38%. The ROC-AUC curve evaluation results in an AUC value of 0.8505. The sentiment analysis results tend to lean towards positive sentiment, indicating a high level of user satisfaction with the Maxim application. Based on these sentiment results, developers can identify what aspects of the Maxim application need to be maintained and improved.
IMPLEMENTASI TEKNOLOGI INFORMASI RESOURCE UPAYA PENINGKATAN LAYANAN DAN MANAJEMEN DATA PERKUBURAN KEPADA MASYARAKAT

The Badan Pengelola Perkuburan Islam (BPPI) is an organization that manages cemeteries and provides burial service facilities to the community. BPPI in managing cemetery data experiences problems in data management and cemetery information service facilities are not updated and are slow. The facts found by the PKM team when visiting BPPI were the lack of data management knowledge and skills in using information technology to manage data and service facilities for the community. These findings prove that BPPI has not implemented information technology in managing cemetery data. Based on these findings, the implementing team has prepared a series of activities using structured training and mentoring methods through Community Based Empowerment (PBM) activities. PBM activities are expected to optimize the application of resource information technology and data management training with information technology as an effort to increase the knowledge and skills of BPPI administrators in managing cemetery data. By implementing hybrid-based resource information technology, administrators are helped in managing cemetery data management and the public can use hybrid-based applications to search and find information on cemetery locations online so that cemetery information services are provided more quickly and accurately. The results that have been achieved from PBM activities are an increase in knowledge by 81.93% and an increase in skills for BPPI administrators by 74.37%.
Identifying Counterfactual Queries with the R Package cfid

In the framework of structural causal models, counterfactual queries describe events that concern multiple alternative states of the system under study. Counterfactual queries often take the form of "what if" type questions such as "would an applicant have been hired if they had over 10 years of experience, when in reality they only had 5 years of experience?" Such questions and counterfactual inference in general are crucial, for example when addressing the problem of fairness in decision-making. Because counterfactual events contain contradictory states of the world, it is impossible to conduct a randomized experiment to address them without making several restrictive assumptions. However, it is sometimes possible to identify such queries from observational and experimental data by representing the system under study as a causal model, and the available data as symbolic probability distributions. @shpitser2007 constructed two algorithms, called ID\* and IDC\*, for identifying counterfactual queries and conditional counterfactual queries, respectively. These two algorithms are analogous to the ID and IDC algorithms by @shpitser2006id [@shpitser2006idc] for identification of interventional distributions, which were implemented in R by @tikka2017 in the causaleffect package. We present the R package [cfid](https://CRAN.R-project.org/package=cfid) that implements the ID\* and IDC\* algorithms. Identification of counterfactual queries and the features of cfid are demonstrated via examples.
OPTIMALISASI DESAIN DIDAKTIS MATERI PEMAHAMAN KONSEP PERBANDINGAN NILAI MELALUI IDENTIFIKASI DAN PENANGANAN LEARNING OBSTACLE

This research aims to develop an effective didactic design to address students' learning obstacles in the concept of value ratios. A qualitative research method was employed, specifically utilizing Didactical Design Research. Data were collected through tests, interviews, observations, and documentation from 8th-grade students at MTs PUI Kancana who had studied the subject matter. The didactic design was based on the analysis of learning obstacles and trajectories, metapedadidactics, and retrospectives. Following the implementation of the instructional design in 7th-grade class A at MTs PUI Kancana, which had not received prior instruction on value comparison, a decrease in students' learning obstacles was observed. Although not all learning obstacles were eliminated, the didactic design effectively reduced the barriers encountered by the students. This research holds significant implications for instructional development. By employing the didactic design for value ratios, it provides an alternative approach to enhance students' understanding and mastery of value comparison concepts. Consequently, this study offers an accurate and comprehensive overview of the didactic design approach to address learning obstacles in the context of value comparison. The findings highlight the importance of employing effective didactic designs to improve learning outcomes in the domain of value ratios.
Towards an Open Science definition as a political and legal framework: on the sharing and dissemination of research outputs

It is widely recognised nowadays that there is no single, accepted, unified definition of Open Science, which motivates our proposal of an Open Science definition as a political and legal framework where research outputs are shared and disseminated in order to be rendered visible, accessible, reusable is developed, standing over the concepts enhanced by the Budapest Open Science Initiative (BOAI), and by the Free/Open Source Software (FOSS) and Open data movements. We elaborate this proposal through a detailed analysis of some selected EC policies and laws as well as of the function of research evaluation practices. The legal aspects considered in our examination include, in particular, the study of the role of licenses in the context of the dissemination of research outputs.
Managing Classroom Activities for Autistic Students in Improving English Vocabularies by Using Total Physical Response

English is one of the subjects studied in special education schools, and until now, are still many problems faced in the process of teaching this foreign language. Teachers' understanding of classroom management and appropriate learning strategies is a solution to overcome the problems that occur in learning English for children with autism. The application of the Total Physical Response (TPR) method is one of the effective methods used to learn English. The purpose of this study is to explore more information about classroom management activities and the application of the Total Physical Response (TPR) method at Satria Galdin Junior High School. The research approach used by the author is a qualitative method with a descriptive research design. Sources of data collection using interviews and observations. To analyze the data, the author uses descriptive techniques to present data based on the results of observations and interviews. The participants were selected using a purposive sampling technique. The participants of this study were students in the 2022/2023 school year, especially class VII of Satria Galdin Junior High School, as many as 20 people. The results of this study can conclude that good classroom management supported by the application of the Total Physical Response (TPR) method in teaching English vocabulary is proven to be effective for student's mastery of English vocabulary and helps students maximize and explore their abilities. The effectiveness is seen in autistic students at Satria Galdin Junior High School.
An Innovative Ground Truth Dataset for Automated Validation of Arabic Handwritten Character Segmentation Algorithms

Character segmentation is one of the most critical phases of Arabic handwriting recognition systems. The validation of Arabic Handwritten Character Segmentation (AHCS) algorithms requires Ground Truthed Datasets (GTD) that provide information on how characters should be segmented. Existing Arabic handwritten datasets provide Ground Truth Files (GTFs) which describe many entries, however, up to date, there is a lack of character-level information. Very few attempts have been made in the literature to establish character-level GTFs. Existing attempts provide characters’ boundaries or unique Segmentation Points (SP). The concept of constant SPs does not go in line with Arabic script features which suppose the presence of many possible SPs between two successive characters. Consequently, existing GTDs cannot allow reliable validation for all varieties of AHCS algorithms. In this paper, we propose a new ground-truthing concept that captures Segmentation Areas (SA) instead of SPs. This concept could respond to all varieties of AHCS algorithms and provides information about overlapping/touching characters and vertical ligatures. Our proposed GTD consists of 1400 GTF describing character-level information for a subset of 1400 images from the IFN/ENIT database. The proposed dataset is publicly available at (https://www.kaggle.com/datasets/mohsineelkhayati/character-level-ground-truth-dataset-for-ifnenit)
Joint Lossless Compression and Encryption for Medical Images

In order to achieve the secure, efficient storage and transmission of medical images, we propose a joint lossless compression and encryption (JLCE) scheme. First, according to the intra-block correlation degree, the original medical image is divided into two non-overlapping regions with strong correlation and weak correlation. Then, a linear prediction method is performed on the strong correlation region to generate prediction errors; while the integer discrete Tchebichef transform (iDTT) with the properties of energy compaction and perfect image reconstruction is exploited to compact the energy of the weak correlation region and produce the transformed coefficients. Finally, a secure arithmetic encoding algorithm is presented to encode the prediction errors and transformed coefficients and output the encrypted and compressed bitstream. Without secret keys, the outputted encoded result can be decoded by our scheme, but the decoded result doesn’t disclose the content of the original medical image. Experimental results show that, the proposed scheme has satisfactory format compatibility and security and also achieves better performances of compression ratio and computational efficiency compared with some state-of-the-art schemes.
MIC: An Effective Defense Against Word-Level Textual Backdoor Attacks

Backdoor attacks, which manipulate model output, have garnered significant attention from researchers. However, some existing word-level backdoor attack methods in NLP models are difficult to defend effectively due to their concealment and diversity. These covert attacks use two words that appear similar to the naked eye but will be mapped to different word vectors by the NLP model as a way of bypassing existing defenses. To address this issue, we propose incorporating triple metric learning into the standard training phase of NLP models to defend against existing word-level backdoor attacks. Specifically, metric learning is used to minimize the distance between vectors of similar words while maximizing the distance between them and vectors of other words. Additionally, given that metric learning may reduce a model's sensitivity to semantic changes caused by subtle perturbations, we added contrastive learning after the model's standard training. Experimental results demonstrate that our method performs well against the two most stealthy existing word-level backdoor attacks.
Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways

Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems and the larger number of videos captured. Thus, an automatic surveillance system that identifies the anomalies has become indispensable for computer vision (CV) researcher workers. The recent advancements in deep learning (DL) algorithms have attracted wide attention for CV processes such as object detection and object classification based on supervised learning that requires labels. The current research study designs the bioinspired Garra rufa optimization-assisted deep learning model for object classification (BGRODL-OC) technique on pedestrian walkways. The objective of the BGRODL-OC technique is to recognize the presence of pedestrians and objects in the surveillance video. To achieve this goal, the BGRODL-OC technique primarily applies the GhostNet feature extractors to produce a set of feature vectors. In addition to this, the BGRODL-OC technique makes use of the GRO algorithm for hyperparameter tuning process. Finally, the object classification is performed via the attention-based long short-term memory (ALSTM) network. A wide range of experimental analysis was conducted to validate the superior performance of the BGRODL-OC technique. The experimental values established the superior performance of the BGRODL-OC algorithm over other existing approaches.
Attention-Assisted Feature Comparison and Feature Enhancement for Class-Agnostic Counting

In this study, we address the class-agnostic counting (CAC) challenge, aiming to count instances in a query image, using just a few exemplars. Recent research has shifted towards few-shot counting (FSC), which involves counting previously unseen object classes. We present ACECount, an FSC framework that combines attention mechanisms and convolutional neural networks (CNNs). ACECount identifies query image–exemplar similarities, using cross-attention mechanisms, enhances feature representations with a feature attention module, and employs a multi-scale regression head, to handle scale variations in CAC. ACECount’s experiments on the FSC-147 dataset exhibited the expected performance. ACECount achieved a reduction of 0.3 in the mean absolute error (MAE) on the validation set and a reduction of 0.26 on the test set of FSC-147, compared to previous methods. Notably, ACECount also demonstrated convincing performance in class-specific counting (CSC) tasks. Evaluation on crowd and vehicle counting datasets revealed that ACECount surpasses FSC algorithms like GMN, FamNet, SAFECount, LOCA, and SPDCN, in terms of performance. These results highlight the robust dataset generalization capabilities of our proposed algorithm.
Implementasi Media Pembelajaran Robokids Berbasis STEAM Untuk Meningkatkan Minat Belajar Dan Prestasi Belajar Siswa Sekolah Dasar

The background of this research is the lack of interest in learning and student learning achievement, the lack of use of STEAM-based learning media in learning so that learning is less interesting so that it affects student learning interest. This study aims to describe how interest in learning and learning achievement of elementary school students is influenced by STEAM-based robokid learning media. One group pretest-posttest design is used in this type of quantitative descriptive research. In this study, questionnaires, interviews, and observations were used to collect data. In this research using data analysis techniques in the form of Paired samples t-test and N-gain score test. In the paired samples t-test, the results of data analysis of learning interest and learning achievement show sig. (2-tailed) 0.000 &lt;0.05. Interest in learning gets an average N-Gain Score of 0.7014 on the N-Gain Score test, while learning achievement gets an average N-Gain Score of 0.4029. It was concluded that STEAM-based robokid learning media can improve the learning achievement of elementary and high school students. So that robokids learning media can be used by teachers in learning.
Analisis Kesalahan Ejaan Bahasa Indonesia pada Berita di Portal Berita Online Tribun-Timur.com (Tribun Sidrap)

The aim of this research is to describe Indonesian spelling errors in news on the online news portal Tribun-Timur.com. This research uses a descriptive qualitative approach. This research focuses on spelling errors, namely the use of letters, writing words, and the use of punctuation marks. The data in the research are words or sentences that contain spelling errors. The data source in this research is news on the online news portal Tribun-Timur.com (Tribun Sidrap) January 2023 edition. The data collection techniques used in this research are reading techniques, note-taking techniques and documentation techniques. The data analysis techniques used in this research are data collection, data reduction, data presentation, and drawing conclusions.From the research results, it was found that there were 1 errors in using capital letters, 9 errors in using italics, 4 errors in writing basic words, 2 errors in writing affix words (prefixes), 4 errors in writing combinations of words, 2 errors in writing combinations of words, 2 errors in writing prepositions. , 3 errors in writing abbreviations and acronyms, 2 errors in writing numbers and numbers, and 12 errors in using commas.
A Method for Point Cloud Accuracy Analysis Based on Intensity Information

Three-dimensional laser scanning has emerged as a prevalent measurement method in numerous high-precision applications, and the precision of the obtained data is closely related to the intensity information. Comprehending the association between intensity and point cloud accuracy facilitates scanner performance assessment, optimization of data acquisition strategies, and evaluation of point cloud precision, thereby ensuring data reliability for high-precision applications. In this study, we investigated the correlation between point cloud accuracy and two distinct types of intensity information. In addition, we presented methods for assessing point cloud accuracy using these two forms of intensity information, along with their applicable scopes. By examining the percentage intensity, we analyzed the reflectance properties of the scanned object’s surface employing the Lambertian model. Our findings indicate that the Lambertian circle fitting radius is inversely correlated with the scanner’s ranging error at a constant scanning distance. Experimental outcomes substantiate that modifying the surface characteristics of the object enables the attainment of higher-precision point cloud data. By constructing a model associating the raw reflectance intensity with ranging errors, we developed a single-point error ellipsoid model to assess the accuracy of individual points within the point cloud. The experiments revealed that the ranging error model based on the raw intensity is solely applicable to point cloud data unaffected by specular reflectance properties. Moreover, the devised single-point error ellipsoid model accurately evaluates the measurement error of individual points. Both analytical methods can be utilized to evaluate the performance of the scanner as well as the accuracy of the acquired point cloud data, providing reliable data support for various high-precision applications.
Pengembangan Buku Cerita Bergambar Untuk Siswa Kelas 2 SD Tunas Dharma Karawang

This development research was sparked because the material provided needed to be tailored to the needs of students and class II students. Students and students need more explanation about clean and healthy environmental material. The purpose of this study is to produce a picture book with clean and healthy environmental material for grade II students to increase reading interest, reading comprehension, and awareness and concern for a clean and healthy environment during the Covid 19 pandemic. The research method was carried out by modifying the development method of Borg. and Gall. Product validation is done through storybook experts and design experts. the validation results have an average value of 3.88 which means good. The final product is an electronic storybook in PDF format. Based on the results of the validation, the researcher concluded that the illustrated storybook with healthy and clean environmental material was worthy of being a source of reading material and media in learning for grade II students at SD Tunas Dharma.