Reviewerly/exHarmony huggingface dataset:
UTILIZATION OF COMMUNITY SOCIAL CAPITAL TO FIGHT OFF CHILD SEXUAL BUSINESS

Forms of verbal sexual abuse of children are inviting, seducing, insulting and threatening. Non-verbal forms of child sexual abuse include touching, stroking, hugging, forcing and attacking. Social capital is very important and very necessary to be utilized as an antidote to child sexual bullying. Social capital is a component and core element of the support system. A support system is needed, in synergy with each other and consisting of all stakeholders: Central Government, Ministries, Regional Government, Regional Heads, POLRI, Sub-Departments, School Principals, Teachers, Homeroom Teachers, Guidance Teachers, RT/RW Heads, Parents, Religious Leaders , Community Figures, Non-Governmental Organizations and all members/components of society. POLRI, Schools (Principals, Homeroom Teachers, Counseling Teachers, Teachers), Parents, Religious Leaders and Community Figures have the duty and responsibility to explain to and train children to be able to behave and be skilled in behaving alertly to the dangers/threats of child sexual abuse.
Reverse Engineering Analysis of CNC-Engraving-Based Stamps for Identifying Production Conditions

The identification of fraudulent stamps is an important problem in various fields, such as finance, law enforcement, and customs. In this paper, we propose a reverse engineering analysis of stamps manufactured by a CNC engraving machine, which can provide important information for identifying the production conditions of stamps. We first analyzed the images of a large number of CNC-engraved stamps to extract key features such as stroke width and spacing, which are closely related to the manufacturing process. We then developed a database of known stamp production conditions, which enabled us to identify the production parameters of unknown stamps through a process of feature matching. Our results showed that even when two stamps are produced by the same engraving machine and appear similar in most aspects, small differences in production conditions can be identified and quantified through our feature-based analysis. This makes it possible to distinguish between two stamps with greater reliability, which can be useful in forensic analysis and legal proceedings. Overall, our research demonstrates that reverse engineering analysis of CNC-engraved stamps can be an effective tool for identifying production conditions and improving the reliability of stamp authentication.
BUDAYA PENGGUNAAN ALAT BANTU PENERJEMAHAN (CAT TOOLS) DALAM PROSES MENERJEMAHKAN

Translation is the transferring process of language from source language into target language with the translation results being made as natural as possible. The problems of the research are positive and negative implications for the work culture of translators and the social changes that occur in society because of using CAT Tools. The purpose of the research is to find out the positive and negative implications of CAT Tools on the work culture of translators as well as identify social changes that occur in society because of using CAT Tools. The method used in this research is qualitative descriptive method. The data collection techniques used were interviews and observation. The results obtained are that there are several positive and negative implications of using CAT Tools on work culture of translators, besides that, the using of CAT Tools creates changes in social values in society.
Word classes in Head-driven Phrase Structure Grammar

Abstract In Head-driven Phrase Structure Grammar the distinction between nouns, verbs, and other parts of speech has a central role in the definition of syntactic word classes, but other distinctions play a role as well, ranging from valence over selection requirements to degrees of saturation. Syntactic word classes are, hence, defined in a factorial way. It will be shown that this provides a way to characterize the properties of functional and minor categories, such as determiners and complementizers, without a proliferation of part-of-speech distinctions. While the role of the part-of-speech distinction in the definition of syntactic word classes is given much attention, the chapter is brief on its role in the definition of semantic word classes, since the latter are defined in terms of model-theoretic notions, such as state of affairs, relations, and propositions, rather than in terms of syntactic notions. The chapter discusses the role of the part-of-speech distinction in the definition of morphological word classes.
IoT and Blockchain Technology Adoption Models to Increase Transaction Transparency in The Car Rental Industry

The car rental industry is a collection of companies that offer car rental services for a certain period according to an agreement between the customer and the car rental company. The high demand for customers to rent cars creates lucrative business opportunities for the car rental industry. But the risk of fraud from some customers who manipulate transactions from agreement to agreement becomes a serious problem for car rental companies. On the other hand, Internet of Things technology provides a high level of supervision over a transaction, while blockchain technology has the advantage of providing transparent and immutable transactions for all parties who enter the blockchain network. So, the purpose of this research is to help car rental companies improve the monitoring and transparency of car rental transactions to reduce the risk of customer fraud. The identification of issues is accomplished using a qualitative technique that is based on a review of the relevant literature. Previous research is used to study research that has been conducted by several previous studies for the research to be novel. The result of this research is a model for adopting blockchain technology and IoT for car rental. The paper also displays the process flow for integrating car rental transactions and parts with IoT devices, as well as connections between blocks with a hash value.
Optimizing Server Monitoring: Web-Based Application for System Operations

The LocalAM application is an application used to manage and optimize local assets on the server. In this study, several weaknesses were identified in the development of web-based tools to collect information from servers, where there is no solution that fully meets the existing needs. The results of this application research and development effort indicate that the system implementation plan that had been prepared previously had been successfully executed. The LocalAM application is able to efficiently retrieve various important parameters such as delivery load, memory usage, disk I/O activity, and network on the server. In addition, this application has been successfully built and can be accessed through the website platform using access authentication, which makes it easy for users to access and use it. The findings from the study also show that this application works well and provides great benefits to users in carrying out data collection and server monitoring tasks. In addition, this study states that the LocalAM application has succeeded in filling the gaps in the server data collection process with a web-based approach.
QoE-Driven Multimedia Streaming Leveraging Distributed Network Function Virtualization at Edge

Driven by the limitation of legacy network communications, recent years have seen a paradigm shift in network architecture, from the conventional TCP/IP towards Software Defined Networking (SDN). The main feature of SDN is to give more flexibility to the network by pushing control from forwarding devices into scalable centralized orchestration. This paradigm enables using forwarding storage and processing capacity for caching content at the network level. The objective of this study is to address the Quality of Experience (QoE) problems in video applications. To this end, we propose a network-assisted QoE-aware bitrate adaptation algorithm that works in cooperation with MPEG-DASH’s proposed SAND technology and virtualized in-network caching function (denotes vDANE). In the proposed framework, client quality adaptation can be assisted by vDANE according to network bandwidth and availability of the appropriate segment/bitrate stored in vDANEs hosted by OpenFlow switches. Simulation results indicate that the proposed method can improve QoE parameters compared with other solutions.
Minimum Message Length Mixture Modelling of Rainfall Data

Rainfall is an important part of people daily life, especially farmers. Agriculture, and also tourism, have been affected greatly by rainy season. As the climate changes, the chance of rainfall amount to change gets higher. Several studies have been performed regarding rainfall data including studies on rainfall model as well as clustering of regions based on rainfall data. However, assumption used in the studies is that the data comes from one class population. This paper reports a study conducted on rainfall data with assumptions that rainfall pattern changes and its data can come from several class populations. The rainfall data used in this study is taken from Badung Regency. The class population is also assumed to come from several distributions including Gamma, Gaussian, and Student t distributions. In developing the model, the Minimum Message Length (MML) principle is applied for both parameter estimation and model selection processes. The result shows that rainfall data comes from a one class Gamma distribution model. None of the classes has chosen Gaussian and Student t distributions as their class models. The model with a gamma value less than 1.0 is better fit with a less message length = 1076.329 bits compared to that with a gamma value greater than or equal to 1.0 with a message length = 1086.851 bits. The results also show that MML slightly performed better compared to Akaike Information Criterion (AIC) and Schwarz’s Bayesian Information Criterion (BIC), in terms of the probability bit-costings calculated from the modelling of sampled data. This shows that the uncertainty region involved in the MML modelling accommodates the unseen existing data better. The selected model implies that rainfall data is not normally distributed and tends to have higher probabilities for less rainfall amounts. This result can be further used in agricultural and tourism sectors in predicting the rainfall amount in the next period.
ExpLIMEable: A Visual Analytics Approach for Exploring LIME

We introduce ExpLIMEable for enhancing the understanding of Local Interpretable Model-Agnostic Explanations (LIME), with a focus on medical image analysis. LIME is a popular and widely used method in explainable artificial intelligence (XAI) that provides locally faithful and interpretable post-hoc explanations for black box models. However, LIME explanations are not always robust due to variations in perturbation techniques and the selection of interpretable functions. The proposed visual analytics application aims to address these concerns by enabling the users to freely explore and compare the explanations generated by different LIME parameter instances. The application utilizes a convolutional neural network (CNN) for brain MRI tumor classification and allows users to customize post-hoc LIME parameters to gain insights into the model’s decision-making process. The developed application assists machine learning developers in understanding the limitations of LIME and its sensitivity to different parameters, as well as the doctors in providing an explanation to machine learning models, enabling more informed decision-making, with the ultimate goal of improving its robustness and explanation quality.
RSAM: Byzantine-Robust and Secure Model Aggregation in Federated Learning for Internet of Vehicles using Private Approximate Median

In Internet-of-Vehicles (IoVs), Federated Learning (FL) is increasingly used by smart vehicles to process various sensing data. FL is a collaborative learning approach that enables vehicles to train a shared machine learning (ML) model by exchanging their local models instead of their sensitive training data in a distributed manner. Secure aggregation, as a privacy primitive for FL, aims to further protect the local models.c However, existing secure aggregation methods for FL in IoVs mostly suffer from poor security against Byzantine attacks, e.g., malicious vehicles submit fake local models, which are common in IoVs and greatly degrade the accuracy of the final shared model without being detected. In this paper, we propose a new secure and efficient aggregation approach, RSAM, for resisting Byzantine attacks FL in IoVs. RSAM first securely calculates an approximate median of local models of the distributed vehicles via the divide-and-conquer strategy as the aggregation model in each training round, providing the strong Byzantine robustness that is similar to the real median (a proven robust rank-based statistic) does, where median means the coordinate-wise median. Furthermore, RSAM is a single-server secure aggregation protocol that protects the vehicles' local models and training data against inside conspiracy attacks based on zero-sharing. Finally, RSAM is efficient for vehicles in IoVs, since RSAM transforms the sorting operation over the encrypted data to a small number of comparison operations over plain texts and vector-addition operations over ciphertexts, and the main building block relies on fast symmetric-key primitives. The correctness, Byzantine resilience, and privacy protection of RSAM are analyzed, and extensive experiments demonstrate its effectiveness.
Cyberbullying Detection: An Investigation into Natural Language Processing and Machine Learning Techniques

Cyberbullying has been a concerning issue ever since the Internet and smartphones became very popular. There are several Cyberbullying types such as videos, images, text, and audio, but the most common occurrence of cyberbullying is through hate speech and offensive language via text message. Making it a suitable task for application of Natural Language Processing (NLP). Social media which includes various platforms like Instagram, Facebook, and Twitter plays a big part in this matter, as it is the medium for communication that has often been the home of hate speech. Many researchers have taken an initiative to generate a tool in detecting cyberbullying. This paper provides a comparative study using several deep learning algorithms such as BERT (Bidirectional Encoder Representations from Transformers), Bi-LSTM (Bidirectional Long Short-Term Memory), and Bi-GRU (Bidirectional Gated Recurrent Unit) in detecting tweets containing cyberbullying. Preprocessing is included in this study, followed by tokenization and embedding. The evaluation of these models focuses on accuracy, precision, recall and F1-Score, which measure their ability to correctly classify different types of cyberbullying from a dataset of tweets. Furthermore, this study also used a confusion matrix as part of the evaluation process. In our study, we found that the BERT model outperformed the BiGRU BiLSTM model in terms of accuracy, achieving about 96%. Followed by Bi-LSTM and Bi-GRU obtained accuracy of 95% and 94% respectively. The result of this comparative analysis indicates all three models exhibit strong performance, while the BERT model had a higher ability to correctly classify cyberbullying instances.
Obstacle Avoidance Algorithm of Controlled UAV Formation Based on Improved 3D Artificial Potential Field Method

Obstacle avoidance technology of UAV formation is one of the important research directions of autonomous UAV formation flight. In order to shorten the effective flight path of UAV formation flight and maintain the consistency of formation flight, this paper improves the traditional 3D artificial potential field method. The path planning problem is optimized by introducing the obstacle auxiliary Angle collected by the sensor, and the distribution and height of the obstacle are used to determine whether it needs to be transferred to three dimensional calculation. When conversion to three dimensions is required, the average gravitational potential field is optimized based on the introduction of relative velocities between the UAVs. Simulation results show that the improved UAV formation obstacle avoidance algorithm can effectively shorten the flight path, thus achieving rapid crossing of obstacles to reach the target point while maintaining formation consistency, with high robustness and adaptability.
PCA-SVM for a Lightweight ASL Hand Gesture Image Recognition

Other studies have previously researched creating a new dataset called Gesture Modified National Institute of Standards and Technology (MNIST Gesture), similar to the MNIST dataset for American sign language (ASL) hand gestures. Several methods, such as long short-term memory (LSTM), have been used for gesture detection on the dataset. However, there is a research opportunity to provide a learning model that requires less training data. Our research aim is to use PCA-SVM for lightweight hand gesture recognition. The first step of our research is to collect the MNIST Gesture dataset from Kaggle. Then, we do preprocessing and prediction model training. We then compare PCA with the mean decrease in impurity (MDI) and the least absolute shrinkage and selection operator (LASSO) method in their performance to reduce dataset dimensions in the SVM training process. Lastly, we compare our proposed model with two state-of-the-art benchmark models: long short-term memory (LSTM) and convolutional neural network (CNN). The test results show that the optimum number of PCA-SVM principal components in ASL hand gesture recognition is 50. Then, PCA performs better than MDI and LASSO on dimension reduction with an SVM model. Finally, the PCA-SVM model bests LSTM in hand gesture recognition by accuracy, precision, recall, and f1-score. However, PCA-SVM and CNN both have an accuracy higher than 0.99. On the other hand, PCA-SVM is more lightweight than CNN because it has a shorter training time and testing time, namely 3.5 s and 0.2 s, respectively.
Transfer Learning Based Model for Colon Cancer Prediction Using VGG16

Colon cancer, or a colorectal cancer, is a malignant neoplasm that originates in the colon. It is one of the most prevalent forms of cancer globally, with significant impacts on morbidity and mortality rates. The essential task is to detect it and detect it at an initial phase for curing the patient precisely. The artificial intelligence plays important roles in the colon cancer prediction. The authors proposed various models on colon cancer prediction using ML and DL. The existing approaches are unable to achieve good accuracy for the colon cancer prediction. This research work suggests a transfer learning based framework for the colon cancer prediction. This framework is planned on the basis of VGG16 and CNN in colon cancer prediction. The proposed framework is implemented in python and results is analysed concerning accuracy, precision, recall.
Managing Security Hazards in BYOD: A Comparative Analysis of Artificial Intelligent Techniques

Bring Your Own Device (BYOD) policies have gained popularity in organizations, offering flexibility and increased productivity. However, they also introduce security hazards that pose risks to sensitive data and systems such as data leakage, weak authentication, malware and viruses, unsecured Wi-Fi networks, device loss or theft, and compliance issues. These problems continuously affect to the organization. Therefore, these problems can be solved by adopting artificial intelligent (AI) techniques. This paper investigates the possibility of potential solutions by adopting AI techniques such as machine learning-based anomaly detection, behavior analysis, predictive analytics, and natural language processing will be examined as potential solutions. The objective of this paper is to identify and compare various AI techniques in addressing the security hazards associated with BYOD. The methodology involves a comprehensive review of relevant articles and research papers to gather insight findings. The results of this research will provide a summary of available AI techniques in monitoring the BYOD security hazards.
Sentiment Analysis of Game Reviews on STEAM using BERT, BiLSTM, and CRF

The Steam platform releases thousands of games almost every week, providing customers with many options, which require various considerations to make a purchase decision. Steam has features that can help consumers consider purchasing games, such as the help dashboard that provides information on game reviews and ratings. However, based on user input reviews, game ratings are still manual, which may cause human error and inaccurate results. To help gamers get the desired game information and increase customer purchasing power towards available games, sentiment analysis is one technique for classifying opinions. This study focuses on sentiment analysis of game reviews, which will result in classification into two classes, positive and negative. It will be measured for its performance based on the f1 score evaluation results. The proposed model that we created is to compare sentiment analysis models using the baseline Bi-directional Long-short Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and fine-tuning Bidirectional Encoder Representations from Transformer (BERT). We then propose another model, BiLSTM-CRF, by examining the effect of using the last four hidden states of BERT and comparing it with the last one hidden state of BERT to be used in the BiLSTM-CRF model. Based on the results of sentiment analysis models we developed for the ten most reviewed games on Steam, the BiLSTM model combined with CRF on the last four hidden states yielded the best performance, with an Fl score of 95.2 %. This score exceeds the baseline transformer-based model's Fl score of 88.0%. This is because combining BiLSTM and CRF allows the log-likelihood of the BiLSTM emission sample to be obtained more accurately so the model does not lose its context.
Feature Selection for Unsupervised Machine Learning

Compared to supervised machine learning (ML), the development of feature selection for unsupervised ML is far behind. To address this issue, the current research proposes a stepwise feature selection approach for clustering methods with a specification to the Gaussian mixture model (GMM) and the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k-\mathbf{means}$</tex> . Rather than the existing GMM and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k-\mathbf{means}$</tex> which are carried out based on all the features, the proposed method selects a subset of features to implement the two methods, respectively. The research finds that a better result can be obtained if the existing GMM and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k-\mathbf{means}$</tex> methods are modified by nice initializations. Experiments based on Monte Carlo simulations show that the proposed method is more computationally efficient and the result is more accurate than the existing GMM and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k- \mathbf{means}$</tex> methods based on all the features. The experiment based on a real-world dataset confirms this finding.