Reviewerly/exHarmony huggingface dataset:
An Ngram-Based Approach to Determine Trends and Patterns in the Social Networks

The recent progress in computing has made iteasier to collect and store huge amounts of information in a text. The growing size of text datasets in text mining and the high dimensionality associated with knowledge discovery is a great challenge that makes it difficult to classify documents into various categories and sub- categories. This paper focuses on how text can be mined from social networks and then categorized using n-grams to determine specific trends and patterns. The main aim of Knowledge Discovery is to extract knowledge from data in the context of large databases. The volume of information that is available is increasing every day. This data ranges from that used in business transactions to scientific data, sensor data, pictures, videos, etc. There is, therefore, a need for a system capable of extracting the core of available information and automatically generating reports, opinions, or summaries of data to aid organizations in better decision-making. Knowledge Discovery is a repetitive process where evaluation measures are often enhanced, mining done on data can be refined, there is an integration of new data, and the data is transformed to get accurate and more appropriate results. The data collected from social networks need to be filtered to capture specific text that will be useful to a PR brand following what clients say about their products online. There is a need for a technique that will provide a quick and precise way of fetching specific text from huge amounts of data on social networks to help analyze the feedback. This research analyzes the use of n-grams to fetch specific text from near-real-time customer feedback that is in the form of large data on Twitter to help Public Relations agencies determine the trends and patterns that will help them align their brands with customer preferences.
Mobile Application Malware Self-Diagnosis

Daily wise, people used various mobile applications, where those applications offer social communication, online shopping, gaming, and other services. However, the rapid spread of mobile applications increases the cyber-attacks surface exponentially, since there are millions of applications that have threats and can exploit user's poor knowledge of security, which leads to difficulties to protect the user from all threats by depending only on traditional methods. Based on that we start to develop an automated system that can protect mobile users without human intervention. The system was developed using the Case-Based Reasoning technique. We proposed a method that can solve a new security issue in mobile applications based on historical solutions stored on the case base has common similarities. For any security issue, some features or attributes can be used to be matched with historically solved security issues. The system selects the appropriate solution based on the nearest similar case from the case base. And the system stores the newly solved case in the case base to achieve self-learning.
Validity of Cosine Similarity Measurement of Functional Requirements and Steps Performed Using Cohen Kappa on SRS Scenery Artifacts

The Scenery application is informative and educative regarding tourist places in Indonesia. Differences in implementation in application development can cause a discrepancy between the client's needs that the software developer has made. Therefore, to measure success in developing the Scenery application, it is necessary to adjust and change the artifacts that form the basis of the SRS documentation. In this study, the artifact was part of the Requirements Specification in the form of Functional Requirements and part of the Use Case Description in the Step Performed. Through a Textual Similarity approach, the two artifacts were used as measuring tools to see the results of the Similarity of a software requirement between the Client and the Developer. This research uses the Cosine Similarity formula to implement Textual Similarity in Functional Requirements and Step Performed. There are results to help the functional needs of artifacts as much as 14 with document labeling from d1-d14 and for steps carried out as many as 14 with labeling from d15-d28. This study produced a matching value of -0.0290, meaning "less than chance agreement." This validity measurement can be used as a reference for improving the quality of artifacts in SRS.
Dynamic deployment prediction and configuration in hybrid cloud / edge computing environments using influence-based learning

Cloud computing has become the norm when it comes to web applications deployment over the last years. Yet, data-intensive applications have shown a great growth were more and more are being developed on a daily basis. Moreover, edge devices have gained a great boost in performance, allowing them to manage and analyze data very efficiently, thus making edge computing an equally as effective alternative. Taking into consideration the always increasing number of information that is being generated requires an enormous amount of resources, hybrid cloud / edge infrastructures seem to be most effective and promising. However, the resource management in such heterogeneous environments regards a difficult task. For this reason, in this paper, we propose a novel dynamic deployment and resources configuration framework for hybrid cloud / edge computing environments using static code analysis. The proposed framework utilizes an influence-based learning technique, for the prediction of the optimal deployment configurations of applications. The proposed solution is evaluated using a data set generated from 389,000 public repositories from GitHub, which proves that our solution manages to successfully identify the optimal deployment configuration of the applications achieving accuracy of over 90%.
Implementation of Object Recognition Integrated with Mixed Reality

A laboratory is a specific room or space equipped with equipment for conducting experiments, investigations, and the like. The use of a laboratory must be based on a certain scientific method. This enables all experiments, research, testing activities, calibration, learning practices, and certain material product development to run smoothly and achieve their goals. The aim of this thesis is to develop a smart lab based on mixed reality and object recognition technology, which is useful for detecting or recognizing objects in an image based on their shape or dataset. This research uses object recognition as an approach that is expected to improve the ease of operating IoT devices to detect objects in the laboratory. The input used is the real-time view of the object. The detected object image is displayed using Hololens 2 and Magic Leap, which will show relevant information.
Artificial Intelligence-Based Chatbot Model Providing Expert Advice to Potato Farmers in Kenya

This research paper presents a solution to the declining yield in potato farming in Kenya, which has been attributed to a limited supply of quality seeds and access to expert advice. The study used IBM Watson Assistant, an AI-based chatbot framework, and GIS to offer expert advice and link farmers to quality seed producers. The paper first introduces potato farming in Kenya and provides a brief history of conversation agents before delving into the theory behind chatbots, including their classifications and general architecture. The methodology section outlines the five significant steps taken in the research, including data collection, implementation, testing and training, and evaluation. The evaluation phase used performance indicators which are presented in detail. The results demonstrate that this potato farming chatbot model had a score of 97.7% in terms of message coverage, a score of 78.4% in terms of conversation containment, and was 88.05% effective, users were 60% satisfied with the model and the likelihood of use of the model in the future was at 80%. The study concludes that this integrated potato farming chatbot model is a practical solution for farmers to improve their yield, and the recommendations made based on user feedback and expert input could improve the model further. Overall, the study presents a promising location-based approach to addressing food security challenges in Kenya through technology-driven solutions.
Jammer Detection on Embedded Android Implementation: Blue Bird Group Case Study

As one of the largest transportation companies in Indonesia. Blue Bird Group know that keeping our driver competition healthy is one of the biggest concerns of our engineering team. The malicious behaviour of one driver can affect other drivers’ performance. The presence of the Internet of Things (IoT) in Blue Bird Group’s fleet can provide a predictive detection of such behaviour so that the operation team can act accordingly. This paper provides an overview of the Blue Bird Group’s implementation of jammer detection to minimize the usage of jamming devices and build trust among our drivers. In each of our fleets, there is an IoT device embedded to help our driver receive orders and the company track its fleet. The idea is to embed an artificial intelligence model on the IoT device and use Android API to provide information for the artificial intelligence model to process. Blue Bird Group also build a web-based control centre that provides information on the malicious driver’s behaviour. The first model has 94.18% accuracy and 11% loss, and the second model has 100% accuracy and 0% loss.
Deploying Quantum Key Distribution into the Existing University Data Infrastructure

Quantum Key Distribution (QKD), also known as quantum cryptography, uses quantum physics to transmit symmetric encryption keys and is secure against attacks by quantum computers. The paper deals with the practical deployment of a commercial QKD device into an existing academic network. As part of the implementation, problems with long-term monitoring, the influence of connectors reflection and the use of different versions of the quantum protocol on the parameters of quantum transmission were solved.
Masked Face Recognition and Identification Using Convolutional Neural Networks

Since the beginning of the COVID-19 pandemic, everyone has been instructed to wear a face mask that blocks the lower part of their facial area. Only a few existing face recognition models are able to detect and recognize masked faces yet, the average accuracy has worsened when compared to the ones that detect an entire face of a person. Therefore, the objective of this study is to develop new models capable of detecting and recognizing masked faces, and compare their performance with existing models. Three convolutional neural networks (CNNs) are used, namely VGG16, VGGFace, and InceptionResNetV2 (IRv2). Many experimental cases are considered including the cases where only masked-face images are used, or the other two cases where both full-face and masked-face images are used together. The models give maximum accuracy at 93.3% where 50 individuals are recognized and identified.
Natural Disaster Monitoring Information System from Social Media Data Using Naïve Bayes Algorithm

In Indonesia, there have been several natural disasters, such as earthquakes, tsunamis, landslides, floods, and others. Because Indonesia is situated where the Eurasian, Pacific, and Indo-Australian plates converge, this potential natural disaster is caused by this location. Social media information is expanding quickly and becoming more useful. Social media helps to alert people of the disaster’s location during a disaster like a flood. Twitter is used as a data search engine in this work. Twitter has been utilized effectively to update the public on current events during emergencies. In order to learn more, we can conduct a search using pertinent hashtags to determining for the incident’s location. The test’s results will show a map of the Indonesian region, and the disaster’s epicenter will be determined using the geolocation provided by the tweet data. The Naive Bayes approach will be used for classification. The clustering process occurs in real time across every region of Indonesia. In this investigation, the accuracy value was 75% based on the k-fold cross-validation test, utilizing a fold value of 3.
Hybrid Speckle De-Noising Filters for Ultrasound Images

Ultrasound imaging uses reflected echo signals from high-frequency sound waves transmitted into a body with high water content to create images of the internal soft tissue, bone, and blood flow. Different kinds of noise are introduced during the signal acquisition and processing stages. Ultrasound images are mostly affected by speckle noise. High-quality images can improve the observational accuracy of diagnostic exams. Speckle noise degrades an image's quality, making it difficult to recognize, analyze, and measure. To reduce speckle noise in ultrasound images and improve diagnostic capability, an investigation and comparison study of the different image filters currently available was done. Thereafter a hybrid filter combination of speckle, gaussian, median and average filter was used to remove speckle noise in ultrasound images. The proposed hybrid filters outperformed other filters in terms of PSNR, SNR, SSIM and MSE.
PolyVerse: An Edge Computing-Empowered Metaverse with Physical-to-Virtual Projection

Incorporating physical-world objects and information into the virtual world is a key aspect of enhancing immersiveness in the metaverse. However, existing solutions are neither efficient nor scalable when projecting enormous objects due to their reliance on expensive specialized equipment. To fill this gap, we propose PolyVerse, an edge computing-powered metaverse platform that supports practical Physical-to-Virtual (P2V) projection while preserving low costs. PolyVerse tackles two challenges in P2V. First, P2V inherently requires real-time projection of enormous physical objects, which is difficult to achieve without specialized equipment. To overcome this issue, we alternatively utilize edge computing-based methods to collect, process, and analyze large-scale physical-world data with cost-effective edge devices and AI models. Workloads are adaptively scheduled among edge devices through a heuristic algorithm to reduce latency. Second, metaverse users may encounter inconsistent states among multiple metaverse service providers, which can be caused by potential networking issues or malicious state tampering when projecting vast objects. To this end, we develop a blockchain-based metaverse management scheme among service providers to ensure a consistent view for users. States are initially constructed as a Metaverse State Tree that supports efficient accumulation, retrieval, and membership proof generation. The tree digests are further secured by blockchain consensus to ensure consistency. Finally, we develop a metaverse campus prototype where real-world pedestrians are projected into the virtual world in real-time. Evaluation shows that PolyVerse can project objects within an average latency of 250ms.
Efficient Camera Trap Image Annotation Using YOLOv5

Using camera traps to acquire wildlife images is becoming more common within conservancies. The information provided by these camera traps enhances understanding of wildlife behaviour and population patterns. The detection and counting of animals present in each of the captured images is valuable information as it can be used to guide conservation efforts. Manual annotation of these wildlife images is a tedious painful process. It is becoming more common to use tools that either use AI to annotate camera trap datasets or use AI to aid in annotation. These AI tools are usually trained on species endemic to a particular region. The ability to fine-tune such models to species endemic to one's particular region is important to save much of the time conservationists manually look through the misclassified images. In this paper, we present a case study where we used a YOLOv5 object detection model trained to detect the presence and count the number of impala and other animals from a dataset collected by researchers at the Dedan Kimathi University of Technology Conservancy. We analyze the results of the AI's performance with respect to a manually annotated dataset. The model was able to annotate 72% of the dataset at a human level of accuracy. The work here shows promise with regard to time spent labelling camera trap images by leveraging the presence of particular species to auto-annotate a majority of the dataset.
Classification of Text on Social Media Data Using the TF-IDF Approach, Word2Vec and Transfer Learning

On social media, there is a lot of information that can be used for further analysis, such as text data, including posts, comments, and reviews. The data has unstructured properties. The problem in this study is the difficulty of analyzing text on social media data to determine standard and non-standard words, so this research utilizes an approach from word representation with TF-IDF, Word2Vec, and transfer learning and using the CNN algorithm. This study produced 3 models in classification performance. In the evaluation model 3 obtained better accuracy compared to model 1 and model 2 because the number of epochs and batch sizes were different. For model 3, the accuracy obtained based on batch values of 64 and epoch values of 20 is 82% for the TF-IDF-CNN model, while the second model uses Word2Vec-CNN with batch values of 128 and epoch values of 60 to obtain 86% accuracy for the Transfer Learning-CNN model with batch values of 256 and epoch values of 100, the accuracy obtained is 91%.
Functional Suitability Analysis of Indonesian Qur'an Applications Based on ISO 25010

Muslims are required to read, study, and comprehend the contents of the Quran because it is the sacred book of Islam and serves as a guide for life in the world. Numerous Al-Quran applications with features that facilitate reading have facilitated the reading, study, and comprehension of the Quran as the result of the technology's rapid advancement. The goal of this study is to test and analyze the four Al-Quran applications from Indonesia that have the highest rating on the Play Store: Al Quran Indonesia, My Quran, Quran Best, and Quran Plus. This is because these apps present different types of features and reflect the latest Quran apps. These Quran applications will be evaluated in the functional suitability section according to ISO 25010. Each sub-characteristic will be assessed one by one based on the formula from ISO 25010. Although the general outline of this application is similar, it turns out that the existing application has a different focus of purpose. So that a standard function is made that at least must exist in an Al Quran application. The results obtained from this study show that the existing application has fulfilled the characteristics of functional suitability well.
Evolving Cauchy Possibilistic Clustering for Data Stream Modeling and Classification

This article proposes a novel approach to clustering called eCauchy, which is based on the Cauchy density function. This method is similar to the well-known possibilistic c-means clustering (PCM), which was developed to address the limitations of fuzzy c-means algorithms (FCM). Density-based clustering is particularly useful when dealing with noisy data and frequent outliers. The recursive measure of density can be adapted for various data mining tasks, including regression and classification. The eCauchy algorithm is simple to use and can handle big-data problems. It evolves dynamically by adding and removing clusters based on a few initial parameters, such as maximum and minimum density. This allows for the identification of clusters with different shapes and sizes and is robust to outliers and high levels of noise. The paper demonstrates the universality of this algorithm and its modifications through several simple examples that can also be found in more complex forms in real-world applications.
Anomaly Detection on Time Series Sensor Data Using Deep LSTM-Autoencoder

Anomaly detection is crucial in various applications (e.g., cybersecurity, manufacturing, finance, IoT), and an automatic and reliable anomaly detection tool is necessary for accurate prediction. The proposed method in this paper focuses on using deep LSTM Autoencoder on time-series data from loT water level sensors deployed on a water catchment. The method uses unsupervised anomaly detection with deviation methods, which involves lower-dimensional embeddings and reconstruction error. The LSTM Autoencoder model includes feature selection by keeping vital features, and learns the time series' encoded representation. The LSTM model is trained for prediction with three hidden layers based on the encoder's latent layer output. Afterwards, given the output from the prediction model if the reconstruction loss of a data point is greater than reconstruction error threshold the value will be labeled anomaly. We also propose and compare an unconventional method of calculating reconstruction error of each sequence with an aim of reducing false positives and false negatives then compare it with frequently used method. The results show that the LSTM Autoencoder performs well on noisy and real-world datasets for detecting anomalies and also the proposed unconventional method of calculating reconstruction loss increases the models accuracy in identifying anomalies.
Heavy Haptics: Simulating Weight in Virtual Reality Applications

Virtual Reality technologies have drastically improved over the last few years, but the physical experience of interacting with virtual objects and mass is still quite limited. Specially developed controllers and gloves technically exist, but most are still in development and not yet established as readily available industry solutions. This paper introduces “Heavy Haptics”, a novel haptic approach that combines offset tracking, rumble feedback and pressure sensors to elicit the sensation of weight without the need for dedicated hardware. We review related works on weight simulation to derive our design and present a quantitative evaluation method. To assess the system, we conducted a small pilot experiment with four VR experts. Our results suggest that Heavy Haptics is comparable to more sophisticated solutions using dedicated hardware. However, additional studies confirming our results need to be conducted in the future.
MagicMoves: A Gesture Creation Framework for Virtual Reality Applications

This paper presents MagicMoves, a novel, non-machine learning-based approach to gesture recognition for Unity, a widely used platform for developing virtual Reality applications. The need for an accessible and customizable gesture recognition system in Unity is emphasized, given the challenges associated with machine learning approaches, such as the requirement for extensive datasets and potential alienation of developers without deep machine learning knowledge. MagicMoves is designed to prioritize minimal overhead and high customization capabilities, allowing developers to record and incorporate their own gestures into their applications. The system is capable of recognizing a wide range of gestures, even those that are overly similar. The paper also discusses the technical requirements for developing such a framework, including gesture editing, gesture recognition, and user interface and integration.