Reviewerly/exHarmony huggingface dataset:
Transforming LMS into KMS in Indonesia Educational Institution Case Study in Telkom University Open Library

Library is an institution that collects printed and recorded knowledge, manages it in a particular way to meet the intellectual needs of its users through various ways of knowledge interaction. In 2021, Indonesia provided 4605 active educational institutions, ranging from universities to community academies. All these institutions are obliged to provide library to create a better learning environment, by providing source of references for each course delivered by the institution. This obligation is encouraged by the government by considering library support within the accreditation system. In this accreditation system, a library should allocate books as a source of reference to each course. This establishes a paradigm that library is where we store books and where a member of the institution may borrow, learn, and return those aforementioned books. Today, educational institution deals with not only books but also thesis, dissertation, technical report, training/workshop report, research paper, etc. Authors believe it will be prudent to leave all these documents of knowledge to librarians, by changing the library's paradigm from managing books to managing knowledge. This study proposes a model of Knowledge Management System as a transformation from Library Management System. This study also explains about expected opportunities and benefits after the transformation.&#x0D;
Antlion Optimizer Algorithm Modification for Initial Centroid Determination in K-means Algorithm

Clustering is a grouping of data used in data mining processing. K-means is one of the popular clustering algorithms, is easy to use, and is fast in clustering data. The K-means method groups the data based on k distances and randomly determines the initial centroid as a reference for processing. Careless selection of centroids can result in poor clustering processes and local optima. One of the improvements in determining the initial centroid on the k-means method is to use the optimization method to determine the initial centroid. The modified Antlion Optimizer (ALO) method is used to improve poor clustering in the initial centroid determination and as an alternative to determining the initial centroid in the k-means method for better clustering results. The results of the research on the use of the proposed method for determining the initial centroid provide an increase in clustering compared to the usual k-means and k-means++ methods. This is evidenced by the evaluation of the sum of intragroup distance (SICD) with UCI datasets, namely iris, wine, glass, ecoli, and cancer, in each method, the best SICD value was obtained in the proposed method. Then measuring the best SICD value for each method and dataset is measured by providing a ranking proving that the proposed method on the iris, wine, and cancer datasets gets the first rank, and on the ecoli and glass datasets the proposed method and the k-means++ method both get the first rank. From the average ranking value, the proposed method is ranked first, which provides evidence that the proposed method can improve the clustering results and can be an alternative method for determining the initial center of a cluster using the k-means method.&#x0D;
Systematic Mapping Study: Research Opportunities on Capacity Planning

The central idea of the research is to improve the efficiency and sustainability of data centers by implementing accurate capacity planning, which will also improve their performance and availability. Various literature reviews have been conducted to understand the current status of capacity planning implementation in different domains and perspectives. However, a more organized and systematic approach is required to map research and implementation results in the relevant areas of capacity planning that have the potential for further development. The present study aims to fill this gap by conducting a systematic mapping study that combines both quantitative and qualitative methodologies. The quantitative approach involved the collection of literature and the classification of topics using the Latent Dirichlet Allocation (LDA) method. On the contrary, the qualitative approach used content analysis to identify future research directions based on keyword trends and topics. The PRISMA framework was followed to guide the search for relevant studies in electronic research literature databases. The mapping results revealed 15 topics, with topics 8, 10, 11, and 15 showing significant potential for further research and exhibiting increasing trends. The identified topics encompass capacity planning, energy and resource management, computing and technology, data analysis and statistics, engineering, and industry, all crucial for businesses and industries to operate efficiently and sustainably. This study provides a comprehensive overview of the state of capacity planning implementation and highlights areas that require further investigation.&#x0D;
Folk Games Image Captioning using Object Attention

The result of a deep learning-based image captioning system with encoder-decoder framework relies heavily on the image feature extraction technique and the caption-based model. The accuracy of the model is heavily influenced by the proposed attention mechanism. The inability to distinguish between the output of the attention model and the input expectation of the decoder can cause the decoder to give incorrect results. In this paper, we proposed an object-attention mechanism using object detection. Object detection outputs a bounding box and an object category label, which is then used as an image input into VGG16 for feature extraction and into a caption-based LSTM model. The experimental results showed that the system with object attention performed better than the system without object attention. BLEU-1, BLEU-2, BLEU-3, BLEU-4, and CIDER scores for the image captioning system with object attention improved 12.48%, 17.39%, 24.06%, 36.37%, and 43.50% respectively compared to the system without object attention.&#x0D;
Utilization of Mobile Network Infrastructure to Prevent Financial Mobile Application Account Takeover

The Covid-19 pandemic has kept almost everyone at home and forced them to perform online activities using their mobile gadgets. Penetration of the Internet and mobile use is increased as lockdowns or restrictions on meeting face to face are getting used to. This has become a new market for cyber criminals to carry out their actions, such as spreading Social Engineering, sending Phishing, doing Account Takeover, and ending in theft of money in Financial Mobile Applications. Application protection with OTP SMS and Magic Link SMS still has vulnerabilities, with several examples of cases that have occurred. For this reason, this problem was raised to find a solution using the Mobile Network Infrastructure. The method used is to compare the congruence between the phone numbers registered in the application and the phone numbers used. Every time a user signs in or signs up, the Financial Mobile Application will perform Mobile Network Verification to cellular operators via API. Verification is carried out by utilizing the header enrichment in the background of the application process that was installed on the user's smartphone or tablet to the Mobile Network Verification Server. The Financial Mobile Applications can then determine whether the user is using a valid or invalid telephone number. Therefore, the target account cannot be taken over because the cyber criminal's mobile device does not have the phone number attached to the victim’s mobile device. This proof is carried out with four test case scenarios with the sign-up and sign-in processes on the same phone number and different phone numbers between devices and applications. It is hoped that this kind of protection model can reduce losses experienced by users of Financial Mobile Applications due to Account Takeover.
Analysis and Mitigation of Religion Bias in Indonesian Natural Language Processing Datasets

Previous studies have shown the existence of misrepresentation regarding various religious identities in Indonesian media. Misrepresentations of other marginalized identities in natural language processing (NLP) datasets have been recorded to inflict harm against such marginalized identities in cases such as automated content moderation, and as such must be mitigated. In this paper, we analyze, for the first time, several Indonesian NLP datasets to see whether they contain unwanted bias and the effects of debiasing on them. We find that two of the three data sets analyzed in this study contain unwanted bias, whose effects trickle down to downstream performance in the form of allocation and representation harm. The results of debiasing at the dataset level, as a response to the biases previously discovered, are consistently positive for the respective dataset. However, depending on the data set and embedding used to train the model, they vary greatly at the downstream performance level. In particular, the same debiasing technique can decrease bias on a combination of datasets and embedding, yet increase bias on another, particularly in the case of representation harm.
Fire Detection System At Labuhanbatu University Based On Internet Of Things (IoT)

Fire accidents are disasters that often occur compared to other fire disasters such as floods, landslides, earthquakes or tsunamis. Fires can occur at any time, and no one knows for sure when a fire accident will occur. The impact of a fire disaster is not only material that can disappear from human lives. The causative factors of fire disasters often occur due to human negligence and fires often occur in houses where the occupants have left them. Labuhanbatu University at night will be left by the owner and all lecturers and educational staff, only guarded by two security people with this condition, it is very dangerous when a fire occurs in one of the buildings. The purpose of this research is to focus on developing a fire detection system at Labuhanbatu University based on the Internet of Things to provide early warning of safety. The system uses three sensors, namely temperature sensor, gas sensor, and fire sensor. This research is R&amp;D research using the ADDIE model with the following stages: analysis, design, development, implementation, and evaluation. The results of the fire sensor test were 90% successful, the results of the sensor test as soon as possible were 90% successful, and the results of the temperature sensor test were 90% successful. This fire detection system can minimize or minimize the occurrence of fire accidents and losses because it is based on the Internet of Things providing early information when a fire occurs to education staff and lecturers at Labuhanabtu University. Overall, this fire warning system can function properly.&#x0D;
Comparison of Naive Bayes and PSO-Based Naive Bayes Algorithms for Prediction of Covid-19 Patient Recovery Data in Indonesia

A brand new disease known as COVID 19 was identified in 2019 but has yet to infect humans (World Health Organization, 2019). This group of viruses can infect mammals, including humans and birds, and cause sickness. People commonly contract coronaviruses from the flu and other minor respiratory diseases, but they can also spread serious diseases such as SARS, MERS, and the deadly COVID-19. Therefore, to avoid further casualties, this number must be decreased. It is crucial to understand the variables that can truly reduce the danger of death and gauge the propensity for recovery in Covid-19 patients. Several techniques in data mining can be used to forecast patient recovery rates depending on various characteristics. The criteria of this study included gender, age, province, and status. The Naive Bayes (NB) and Pso-based Naive Bayes algorithms are compared in this study using patient data sets to determine whether the strategy is more accurate. The findings of this study reveal that the NB method has a 94.07% accuracy rate, a precision value of 14%, a recall value of 1% and an AUC value of 0.613, according to the study data. The accuracy rate of the Naive Bayes based on PSO is 95.56%, the precision is 25%, the recall is 1%, and the AUC is 0.540.&#x0D;
Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest

The number of published academic papers has been increasing rapidly from year to year. However, this increase in publications must be linear with an emphasis on quality. To ensure that academic papers meet the required quality standard, the peer review process is necessary. The main objective of the assignment of reviewers is to find the appropriate reviewer who can conduct a review based on their field of research. However, there are potential obstacles when there is a conflict of interest in the process. This study aims to develop a method for assigning reviewers that overcomes such obstacles. Our approach involves combining the Latent Dirichlet Allocation (LDA), Classification, and Link Prediction methods. LDA is used to find topics from the research data of prospective reviewers to ensure that the assigned reviewers are well suited to the submitted article. These data were used as training data for classification using Random Forest. Finally, link prediction implemented to make reviewer recommendations. We evaluated and compared our proposed method with previous research that used cosine similarity as the last step in recommendation, using Mean Average Precision (MAP). Our proposed method achieved a MAP value of 0.87, which was an improvement compared to the previous approach. These results suggest that our approach has the potential to improve the effectiveness of academic peer review.&#x0D;
Requirement Elicitation Modeling Using Knowledge Acquisition in Automated Specification Method

Errors often occur during the requirements elicitation stage, causing failure of the software development process as a whole, so that the built system cannot be used optimally; these data are obtained from survey data from several large companies involved in technology development. To overcome this problem, this study tries to apply the elicitation requirements using the KAOS method in the case study of the SMM reseller ordering system to obtain system requirements that are in accordance with the goals and objectives of each existing stakeholder. Based on the elicitation of system requirements, functional requirements are generated that include automatic orders, automatic payments, manage product sales, manage orders, manage payment methods, manage problem orders, manage customer data, manage company information, automatic email notifications, and sales statistics information. The results of this study are a table of functional requirements that have been declared valid and in accordance with the goals and requirements of each stakeholder after evaluating and validating the results for each stakeholder involved.&#x0D;
The Use of the Pre-Trained BERT and GPT-3 Models to Automate the Composing of Use Case Descriptions

Composing the use case description, which comes in the form of a textual table, is an essential and critical task while developing software.Usually, this process results from manually extracting the actors and their use cases and defining other essential information like the flow of events for each use case.This paper proposes a new Intelligent Computer Aided Software Engineering (I-CASE) tool to automatically generate use case descriptions and use case diagrams from user requirements by utilisation of the pre-trained GPT-3 and BERT Large Language Model (LLM).The evaluation showed the tool's ability to outperform previous studies in creating use case descriptions based on high accuracy in extracting actors and their use cases from text written in natural English.While there is a lack of test cases, especially in natural languages other than English, this is the first use of the Large Language Model (LLM) to automate the generation of use case descriptions and draw the use case diagrams.
Multi-Objective Feature Selection for Structure Preservation in Multi-Label Data Using Particle Swarm Optimization

Abstract In order to make the data more manageable and improve the accuracy of machine learning models, data need to be preprocessed. Data preprocessing involves a series of steps such as data cleaning, data transformation, feature selection, and feature extraction. Data cleaning involves removing or correcting data that is incorrect, incomplete, irrelevant, or duplicated. Data transformation includes normalization, standardization, aggregation, and discretization. Feature selection involves selecting the most relevant features from the dataset while feature extraction involves transforming existing features into new features. Our proposed evaluation function combines a multi-label accuracy measure with a feature selection measure. In this paper a new assessment method based on swarm intelligence for feature selection. The suggested technique utilizes a multi-objective fitness function by utilizing mutual information between features and labels. We conduct experiments on five real-world datasets. The results show that proposed evaluation function in some cases outperforms the existing methods.
The Dynamics of Access to ICT and Technology Practices of Secondary School Teachers

The COVID-19 pandemic highlighted the importance of access to Information and Communication Technology (ICT) for effective teaching and learning. Access to ICT is a complex and dynamic phenomenon that significantly impacts teachers' technology practices. There is a need for more extensive studies to explore the dynamics of access and teacher technology practices in various contexts. This study explored the influence of access on the technology practices of secondary school teachers in the Western Cape, South Africa. A qualitative online survey was used to explore the lived experiences of teachers during the COVID-19 pandemic. A diverse sample, in terms of gender, age, years of experience of teachers, and socio-economic status of schools, of 22 teachers voluntarily participated in the study. Van Dijk's cumulative model of access to ICT provided a useful lens to explore how and when the dimensions of access supported or hindered teachers' appropriation of ICT. This study found that most respondents were primarily exposed to traditional face-to-face teaching before March 2020. The suspension of in-school teaching, however, caused a dramatic shift in the way teachers interacted with technology, learning materials and learners. Three dimensions of digital inequality, associated with physical access, manifested in South Africa, namely the Virtual Classroom, WhatsApp, and Radio-Television groups. Most participants in this study returned to traditional teaching practices with increased use of technology when schools reopened again. The findings of this study suggest that access to technology is not a linear progression of successive stages that by default culminates in usage. This paper proposes a more holistic contextualised approach to access to create enabling environments for the use of ICT and empower teachers to use ICT in their teaching practices.
SSIVD-Net: A Novel Salient Super Image Classification &amp; Detection Technique for Weaponized Violence

&lt;p&gt; Detection of violence and weaponized violence in closed-circuit television (CCTV) footage requires a comprehensive approach. In this work, we introduce the \emph{Smart-City CCTV Violence Detection (SCVD)} dataset, specifically designed to facilitate the learning of weapon distribution in surveillance videos. To tackle the complexities of analyzing 3D surveillance video for violence recognition tasks, we propose a novel technique called, \emph{SSIVD-Net} (\textbf{S}alient-\textbf{S}uper-\textbf{I}mage for \textbf{V}iolence \textbf{D}etection). Our method reduces 3D video data complexity, dimensionality, and information loss while improving inference, performance, and explainability through the use of Salient-Super-Image representations. Considering the scalability and sustainability requirements of futuristic smart cities, the authors introduce the \emph{Salient-Classifier}, a novel architecture combining a kernelized approach with a residual learning strategy. We evaluate variations of SSIVD-Net and Salient Classifier on our SCVD dataset and benchmark against state-of-the-art (SOTA) models commonly employed in violence detection. Our approach exhibits significant improvements in detecting both weaponized and non-weaponized violence instances. By advancing the SOTA in violence detection, our work offers a practical and scalable solution suitable for real-world applications. The proposed methodology not only addresses the challenges of violence detection in CCTV footage but also contributes to the understanding of weapon distribution in smart surveillance. Ultimately, our research findings should enable smarter and more secure cities, as well as enhance public safety measures.&lt;/p&gt;
SSIVD-Net: A Novel Salient Super Image Classification &amp; Detection Technique for Weaponized Violence

&lt;p&gt;Detection of violence and weaponized violence in closed-circuit television (CCTV) footage requires a comprehensive approach. In this work, we introduce the \emph{Smart-City CCTV Violence Detection (SCVD)} dataset, specifically designed to facilitate the learning of weapon distribution in surveillance videos. To tackle the complexities of analyzing 3D surveillance video for violence recognition tasks, we propose a novel technique called \emph{SSIVD-Net} (\textbf{S}alient-\textbf{S}uper-\textbf{I}mage for \textbf{V}iolence \textbf{D}etection). Our method reduces 3D video data complexity, dimensionality, and information loss while improving inference, performance, and explainability through salient-super-Image representations. Considering the scalability and sustainability requirements of futuristic smart cities, the authors introduce the \emph{Salient-Classifier}, a novel architecture combining a kernelized approach with a residual learning strategy. We evaluate variations of SSIVD-Net and Salient Classifier on our SCVD dataset and benchmark against state-of-the-art (SOTA) models commonly employed in violence detection. Our approach exhibits significant improvements in detecting both weaponized and non-weaponized violence instances. By advancing the SOTA in violence detection, our work offers a practical and scalable solution suitable for real-world applications. The proposed methodology not only addresses the challenges of violence detection in CCTV footage but also contributes to the understanding of weapon distribution in smart surveillance. Ultimately, our research findings should enable smarter and more secure cities, as well as enhance public safety measures.&lt;/p&gt;