Reviewerly/exHarmony huggingface dataset:
Deep Hierarchical Variational Autoencoders for World Models in Reinforcement Learning

With the increasing demand for sample-efficient and robust reinforcement learning agents, particularly in intricate domains like robotics, healthcare, and gaming, there is a strong need to minimize the computational overhead caused by the interactions between real and virtual agents. This necessitates highly accurate models to simulate virtual agents and limit the number of such interactions. To this effect, model-based reinforcement learning (MBRL) has been proven very effective in formulating an environment with superior decision-making and higher learning efficiency. A known approach in MBRL is World Models, which uses a generative engine called Variational Autoencoders (VAE). VAE utilizes a relatively simple architecture constrained in processing power for complex image inputs. Therefore, the image reconstruction error is high. Recent research in VAEs has shown poor reconstruction quality. This paper proposes a Nouveau VAE (NVAE) based World Models to address the abovementioned limitations. NVAE, which employs deep convolutions in its architecture, is employed as the visual sensory component of the World Models and is used to encode the environment dynamics into a latent representation. We show that NVAE-based World Models perform exceptionally well in the dream environment of car racing-v2 (OpenAI GYM env), improving the agent's performance by 45%. We then demonstrate that the NVAE-based World Models can be applied to robotic simulation environments like panda-gym, where the agent achieved a 95 % success rate in solving the reach task.
Addressing Critical Issues and Challenges for Dynamic Cybersecurity Management in Organisations and Local/Regional Networks: The CS-AWARE-NEXT Project

In our paper we present interactions between artificial intelligence (AI) and the field of cybersecurity. We present work of the Horizon Europe CS-AWARE-NEXT project that aims to provide improved cybersecurity management capabilities to organisations and local/ regional supply networks. Such organisations and networks operate in a highly dynamic cybersecurity environment, and must comply with European legislation such as the network and information security (NIS/NIS2) directive. Organisations increasingly understand that cybersecurity needs to be more dynamic and collaborative, building on a shared situational awareness of potential cybersecurity issues relevant to the organisations and networks in question. There is no doubt that there exists a cybersecurity shortfall in many organisations as the majority of their legacy systems have not been designed either to foster cybersecurity awareness, nor are they Artificial Intelligence / Machine Learning-ready, in terms of permitting models and algorithms to be deployed, or to interact with the application logic of the original systems. To remedy this, there is a need for experimenting with novel approaches to information systems engineering that can address needs emerging from the inclusion of dedicated Artificial Intelligence and Machine Learning modules and components to enable provisions for self-healing, self-protecting, and self-configuration.
Feature Selection via Independent Domination

Feature or variable selection is a fundamental problem in data analysis and statistical modeling. Classic methods resulting in dimensionality reduction are diverse and include things like statistical hypotheses testing for zero coefficients, ‘stepwise’ methods minimizing, e.g., AIC, the spectra of a data matrix or principal component analysis, regularization methods especially the Lasso, various heuristic and ‘shrinkage’ methods all of which result in a subset of the feature space used as a basis for statistical modeling. Combinatorial variable selection has also been used in a manner that aids in the selection of a good subset of the feature space. A graph, or the ‘data graph’, is based on the pairwise correlations of features and may be used to extract the most distinguishing features. Partly due to high computational cost, combinatorial variable selection methods have not been well studied. We consider a variable selection procedure via the Minimum Independent Dominating Set problem. We explore the use of some exact and heuristic methods that proved to be effective for feature extracting and ranking.
Bibliometric Analysis of Cooperative Leadership Using Vosviewer Application

This study aims to systematically analyze and demonstrate bibliometric analysis of data step by step using VOSViewer. A step-by-step analysis is provided to make first-time users access and utilize VOSViewers more easily. This study provides data analysis regarding Cooperative leadership so far by utilizing the mapping tool in VOSViewer. The method used is descriptive qualitative to describe the performance of bibliometric analysis by producing network visualizations of selected topics. From the search results, 25 relevant journals were found that were published in the last three years, which were then grouped into three categories according to the year of publication. After grouping journal data, researchers found that the number of articles published on the topic of cooperative leadership from 2019 to 2023 was 114 items. As a result, it is concluded that cooperative leadership is a field that can be combined with other fields to be studied. It's a way of showing lots of opportunities to discover new things to define a theme. Furthermore, this analysis is expected to be a source for researchers to conduct research and determine research themes.
TEACHERS’ PERCEPTIONS ON THE USE OF PEER- CORRECTION AND SELF-CORRECTION TECHNIQUE TO TEACH WRITING

This study aims to determine the teacher's perception of the use of peer-correction and self-correction techniques to teach writing. This research method is a qualitative descriptive method. Research data were collected through interviews. Data were taken from two English teachers at SMAN 5 SELAYAR.The results of the study reveal that teachers believe that Peer-Correction and Self-Correction Techniques in Teaching Writing have a positive influence on students to gain knowledge, especially in writing lessons. In addition, Peer-Correction and Self-Correction techniques are suitable for use at every level of students. The researcher also found that the teacher had an important role in determining the type of material that was suitable for the learning objectives.
EFFECTIVENESS OF USING CAKE APPLICATION ON STUDENTS' PRONUNCIATION SKILLS AT SMP UNISMUH MAKASSAR

This study aims to find out the use of Cake Application in improving the students' pronunciation skills at the nine grade of SMP Unismuh Makassar.This research used quantitative research with a pre-experimental approach. The researcher gave pre-test to the students, and then the researcher gave the students’ treatment using Cake Application. After giving treatment, researcher gave post-test to the students. The population in this study were students of the class IX SMP Unismuh Makassar. The sample of this research was the students of the class IX.A, included 33 students.The result showed that the t-test value was higher than t-table value. The t-test value of pronunciation is greater than t-table (8.21&gt;2.03693). It was concluded that the null hypothesis (H0) was rejected and the alternative hypothesis (H1) was accepted. That means there were significant differences students' pronounciation skill before and after using the Cake Application. The research conducted shows that the use of Cake Application on students’ pronunciation skills was effective.
THE EFFECT OF CONTEXTUAL REDEFINITION STRATEGY (CRS) ON STUDENTS’ VOCABULARY IN READING COMPREHENSION AT THE 10th GRADE OF SMK NEGERI 2 SELAYAR

This study aims to find out whether Contextual Redefinition Strategy (CRS) was effective to enhance vocabulary at the 10th grade of SMK Negeri 2 Selayar. This research used quasi experimental design. The population of this research was the tenth-grade students of SMK Negeri 2 Selayar which consisted of 60 students. The sample of this research was 40 students taken by purposive sampling technique. The researcher used vocabulary test as the instrument of this research. Based on the research findings, the result showed that using Contextual Redefinition Strategy (CRS) was effective to enhance students’ vocabulary. It is proved by the means score of post–test between experimental group and control group. The means score in experimental group before and after treatment (47.15 became 89.50) and in control group before and after treatment (44.05 became 59.40) Moreover, the findings revealed that p-value of post-test score was 0.000 with the level significance 5% (0.05). It can be concluded that p-value (0.000) &lt; sig ɑ = (0.05, 5%). Then, the effect size gained was 2.8. Thus, it proved that Contextual Redefinition Strategy (CRS) was effective at a strong level on students’ vocabulary in reading comprehension.
Public Network Models: A Typology Based on a Systematic Literature Review

Objective: The article develops, based on a systematic literature review (SLR), a typology of public network models. It interprets the concept and specificity of a public network. Finally, it reviews the typology of organisational network models and indicates the directions for further research. Research Design &amp; Methods: The methodology of systematic literature review was used, supplemented by snowball sampling. To obtain data, the Scopus and Web of Science (WoS) scientific databases were analysed. The results were then synthesised to develop a typology for public networks. Typological analysis was used in addition to SLR and snowball sampling. Findings: As a result of a systematic review of the literature, a typology of public network models was reviewed and a typology proposed. The theoretical function of the typology is an indication of trends in the development of public network models, while its practical function is as a tool for further analysis. Implications / Recommendations: The analysis shows that public networks differ from other networks, such as business networks. The breakdown of public network models is multi-layered and may include various criteria. The typology developed for the paper covers four types of public networks: global public networks, networks of public organisations, local government networks, and partnerships. Contribution: The issues discussed in the paper fill the research and methodological gap in research on public networks. This gap is a stark lack of theory and conceptualisation in studying such networks. The paper defines the role of the network approach as an emerging paradigm in the management of the public sphere, and that is its main contribution.
DIGITAL DATA SECURITY WITH APPLICATION OF CRYPTOGRAPHY AND DATA COMPRESSION TECHNIQUES

The need for digital data security is to ensure that the data and information we have are confidential and can only be accessed by authorized users. And no one can change the information in it, thus ensuring complete accuracy. The functions of data security are confidentiality, authentication, integrity, and anti-repudiation. Compression techniques are used to protect digital data because they aim to make less storage space and allow us to transfer more data over the internet. This study aims to plan to prove the application of a combination of 2 (two) techniques, namely compression and cryptography to digital data with the aim of increasing the security of the data. This research has the result that the compression technique of the Huffman method is the most effective in compressing digital data into the smallest file size compared to other compression methods. It can compressed the data size by around 30% (thirty percent) to 40% (forty percent) compared to the original data size. And coupled with data security with cryptographic encryption techniques so that files remain safe when transferred over the network.
Use of the CodeIgniter Framework in Developing Online Registration and Pre/PostTest Education and Training Applications at BKA Aceh

This study analyses the use of the Codeigniter Framework in developing an online training registration and pre/posttest application at the Aceh Civil Service Agency (BKA). The purpose of this research is to build an information system that can provide general information about training, as well as facilitate registration and pre/posttest online and real-time. This research uses the method of needs analysis and system design analysis. The results of this study indicate that the system developed can provide easy access to prospective participants to find out the training schedule. Admin can access data related to training schedules, participants, and committees, while the committee can manage pre/post test questions and exam results. The system allows participants to access modules and pre/posttests after registering. Exam result data can be used as a reference in decision-making related to job promotions that require training qualifications. In conclusion, the use of the Codeigniter Framework is effective in developing online registration and pre/posttest training applications at BKA Aceh.
COMPARISON OF DATA MINING ALGORITHM FOR CLUSTERING PATIENT DATA HUMAN INFECTIOUS DISEASES

Tuberculosis is known as an infectious disease whose transmission through air intermediaries is caused by the germ Mycobacterium Tuberculosis. This disease has become a case that has almost spread throughout the pelalawan Regency with the number continuing to increase every year so that it is possible to be able to group the areas where this disease spreads. Grouping of tuberculosis data distribution areas using data mining methods in the form of clustering with the data used coming from the Pelalawan Regency Health Office from 2020 to 2022. The data obtained earlier will then be processed using k-medoids, k-means, and x-means algorithms. The beginning of this research was by processing data from each year using these three algorithms. Determination of the most optimal algorithm using DBI or known as the Davies Bouldin Index. The results of the processing of existing indicators are grouped into three sections, namely areas with a high, medium, and low number of cases. From the results of the study, the optimal algorithm in 2020 data is the k-medoids algorithms with a DBI value of 0,553 and in 2021 data, the most optimal algorithm is the k-means and x-means algorithm with similar DBI values of 0,582. Furthermore, the data in 2022 the most optimal algorithms are the k-means and x-means algorithms because they have the same DBI value, which is 0,510.
HUMAN FACE RECOGNITION ON IMAGE VIDEO CONFERENCE APPLICATION USING SIAMESE NETWORK WITH SKIP CONNECTION SMALLER VGG MODEL

Attendance recording is needed to find out someone's attendance at a meeting or meeting. These meetings are sometimes conducted online through the video conferencing application. Recording attendance at online meetings is using an online form that is distributed via chat. There are several problems such as chats piling up and meeting participants arriving late so they cannot access the form link. Therefore, facial recognition can be used to record attendance using screenshots as an attendance record with the aim of helping to facilitate attendance recording through video conferencing applications using computer vision technology. This study proposes a method of using the Siamese network with the Smaller VGG skip connection model to improve human face recognition in video conferencing application images. Has validation accuracy results in the training phase of 98%, precision of 98%, and recall of 98%. For the similarity phase where the model is applied to the Siamese network, the accuracy is 95%, the precision is 53%, and the recall is 78%. Then the model is applied to the pipeline system with the YOLO-face model to classify the results of face detection from Yolo with the faces in the database so that the model does not need to be retrained if there are new faces, it only needs to add facial images to the database to be compared with the query image..
DESIGN OF APPLICATION INFORMATION SECURITY SELF-ASSESSMENT USING VBA AND MSXML2.XMLHTTP CASE STUDY: DISKOMINFO KABUPATEN KAMPAR

Information security includes the issues that may threaten accountability, reliability, trustworthiness, privacy, authenticity, and trustworthiness of information in an agency. Data and information are very risky things in Information Security, and therefore it is important to do information security governance. The process of evaluating information security using the Index KAMI and ISO 27001: 2013 will be carried out in this study by recording data using Microsoft Excel which has been provided by the National Cyber and Crypto Agency (BSSN). To make it easier to conduct information security assessments and simplify the Excel display, Visual Basic for Applications (VBA) will be utilized as a medium for adding ISO 27001: 2013, then it will be connected using MSXML2.XMLHTTP. The results of the self-assessment carried out show that the Communication, Information, and Signaling Service of Kampar Regency has a level of completeness in implementing the ISO 27001 standard at the "Inadequate" level with a score of 151 only reaching level I+. Meanwhile, the results of the ISO 27001: 2013 Annex control evaluation show that there are still clauses that have not been fulfilled. Therefore, the Communication, Informatics, and Coding Office of Kampar Regency urgently needs improvement in order to fulfill the clauses of ISO 27001: 2013.
APPLICATION OF NAÏVE BAYES CLASSIFIER ALGORITHM IN DETERMINING THE LEVEL OF CUSTOMER SATISFACTION WITH RUMBAI POST OFFICE SERVICES

Technological advances make service and delivery of goods grow rapidly. Coupled with people's changing shopping habits by shopping online, shipping companies are increasingly needed. PT. POS Indonesia is the first expedition company in Indonesia. Currently PT. POS Indonesia has opened many POS Office branches in every region in Indonesia, one of which is the Rumbai POS Office located in Pekanbaru City. To continue to maintain the company while competing with other expeditions, the Rumbai POS Office must continue to maintain its customers by improving the quality of service. Survey analysis can be done to determine the extent of customer satisfaction with the services provided. To find out the level of customer satisfaction, you can use the classification method. Naive Bayes is a popular and effective machine learning algorithm for classification problems. The study used datasets sourced from the results of questionnaire distribution to customers of the Rumbai POS Office. The questionnaire used 14 indicators derived from the Community Satisfaction Index set by the Ministry of Agriculturein 2004. The classification resulted in a Satisfied class of 16 data with a percentage of 84.2% and a Dissatisfied class of 3 data with a percentage of 15.8%, it can be concluded that the service at the Rumbai POS Office is good. From the classification results, it is proven that the Naïve Bayes algorithm is able to predict well the level of customer satisfaction with an accuracy value of 94.74%, precision of 100%, and recall of 94.12%. The results of this research can later be used as information for the Rumbai POS Office to be able to improve service quality.
Development of Video Games With WebGL Format That Allows You to Play Video Games Without Need for a Device With High Specifications

The rapid development of the gaming industry has brought new challenges in terms of hardware limitations faced by some players. Nowadays, many video games require devices with high specifications to provide an optimal gaming experience. However, this left several players with devices that were unable to run these video games. This research focuses on experimenting with video games in WebGL format that can run well on devices with low specifications, thereby allowing wider access to a quality gaming experience. In this research, the author tested video game performance on various devices with different specifications in WebGL format. The test results show that this approach succeeded in achieving the author's goal, namely that the video game still can be played on devices with low specifications.
Design and Evaluation of Mobile Application Interface for Expert System of Drug Selection with Local Wisdom using Goal-Directed Design and Heuristic Evaluation Approach

User interfaces play an essential role in computerized systems, especially expert systems, by facilitating communication between the user and the system. In expert systems designed to replicate experts' decision-making skills, the user interface is critical for receiving information from users and enabling effective interaction with the system. Through this interface, users can enter data and then find a solution to a problem. This research aims to create an interface design for an expert system on medicines with local wisdom. This interface design is needed to create an environment in the form of a system to preserve information about medicines with local wisdom. The approach used in designing the interface is a goal-directed design approach, and a heuristic evaluation approach is used to evaluate the design results. The research results are a high-fidelity mockup with good evaluation ratings, where none of the severity ratings obtained from 25 respondents got a score of more than 1. However, there are two aspects with a score close to 1, namely aspect nine with 0,97 severity ratings score and aspect ten with 0,98. Both focus on the absence of a contact menu that users can contact if they have problems using the application they have created. However, this can be minimized by adding information to the instructions menu section, which is already in the initial design.
Comparison Analysis of K-Means and DBSCAN Algorithms for Improving Budget Absorption Efficiency in EIS

This study aims to analyze the comparison between the K-Means and DBSCAN clustering algorithms in budget absorption within the Executive Information System (EIS). Realized budget achievement data from regional devices serve as the primary dataset for analysis. Before conducting experiments, the data undergo a preprocessing stage to eliminate outliers and apply normalization processes, ensuring the data is ready for further analysis. Subsequently, both algorithms, K-Means and DBSCAN, are applied to the budget achievement data to generate clusters corresponding to their respective characteristics. The research anticipates that the results will unveil significant findings in comparing the performance of K-Means and DBSCAN algorithms in budget absorption within the EIS context, especially within the scope of this study. Therefore, this analysis is expected to provide valuable insights for stakeholders aiming to enhance the efficiency and effectiveness of budget management through the optimal utilization of clustering algorithms within the EIS.
A Single-ring-oscillator based True-random-numbergenerator with 3-edges Collapse

This paper presents an all-digital ring oscillator (RO) based true random number generator (TRNG) that harvests entropy as a jitter. The proposed TRNG constructs upon the collapsing RObased structure and exhibits high immunity against external power attacks. This approach senses the 3-edge collapse point using a single RO without a reference RO, reducing both area and energy consumption. A prototype chip fabricated using 65 nm technology operates between 1.08 V and 1.44 V, occupying an area of 702 µ㎡. The randomness of the TRNG passed the NIST randomness test up to 2 LSB when the external power attack was below 300 mVSUBPP/SUB. This work demonstrates a throughput of 7.1 Mbits/s at 1.2 V and provides an efficiency of 40.93 Mbit/mJ at 1.08 V.