Reviewerly/exHarmony huggingface dataset:
Location extraction from Traffic Event-related Text

One of the contents that will play an important role in the trip planning system is content related to geospatial. To ensure that the trip planning system is optimal and safe, information about traffic conditions, events, as well as their location is required. People often talk about traffic conditions on social media, such as traffic jams, detours, or accidents. These activities provide textual data related to traffic events. Location is regarded as essential information from traffic event-related texts to identify where the event took place. This study uses Indonesian short text for location extraction with named entity recognition (NER) technique. Data from twitter-based social media (lewatmana.com) are collected. Bidirectional Long Short-Term Memory - Conditional Random Field (BiLSTM - CRF) model and Indonesian POS tagger are used to develop the named entity recognition model for location extraction. Our current model shows promising results with 91.21% accuracy.
Air Pollution Index (API) Analysis at Jakarta in 2019-2020 using Fuzzy C-Means and Gaussian Mixture Model

This study aims to compare the Air Pollution Index (API) clustering between fuzzy c-means (FCM) with gaussian mixture model. This study used air quality data on each parameter in 2019-2020 from five monitoring stations, that is Bundaran HI (DKI1), Kelapa Gading (DKI2), Jagakarsa (DKI3), Lubang Buaya (DKI4), and Kebon Jeruk (DKI5). Determination of the optimum cluster number on Fuzzy C-Means based on Partition Coefficient (PC), Classification Entropy (CE), Separation Index (SI), Silhouette Index, and Effectiveness. The optimum cluster number in the Gaussian Mixture Model is based on BIC and Silhouette Index values. Almost all Silhouette values on Fuzzy C-Means are more significant than the Silhouette Gaussian Mixture Model. Fuzzy C-Means is more suitable for clustering Jakarta Air Pollution Index (API) than The Gaussian Mixture Model method.
Identification of Conflict Opinion in Aspect-Based Sentiment Analysis using BERT-based Method

Aspect-based sentiment analysis (ABSA) is an NLP task for predicting sentiment polarities of specific aspects in a given opinion sentence. Recent research shows that deep learning and language modeling like BERT has become state-of-the-art in NLP tasks, including ABSA. However, most methods still ignore conflict opinion or methods that reached high performance in 2-class (positive and negative), and 3-class (positive, negative, and neutral) classification will be degraded when applied in a 4-class classification where conflict opinion is included. In this paper, we propose a BERT-based method that can identify and handle aspects containing conflict opinions in three steps: (i) designing input representation for BERT-based sentence-pair classification task, (ii) processing two-label sentiment classification for each aspect, and lastly (iii) translating the second step result to 4-class sentiment classification. Experimental results on the SemEval-2014 restaurant domain dataset demonstrate that our proposed method has effectively identified conflict opinion and achieved better results on 3-class and 4-class classification tasks.
Deep Learning for Robust Speech Command Recognition Using Convolutional Neural Networks (CNN)

The rapid development of mobile devices has made human-computer interaction through voice increasingly popular and effective. This condition is made possible by the rapid growth of Automatic Speech Recognition (ASR) technologies. ASR can convert human speech signals into text and interpret it as a command for computer systems to perform. One of the remaining challenges for ASR is that the system’s performance can degrade significantly in noisy environments. This research aims to build a speech recognition system capable of recognizing human speech in clean and noisy conditions, enabling the system to recognize speech commands even in noisy conditions. Deep Neural Network (DNN) methods are the dominant methods for ASR. But, there is increasing interest in using a convolutional neural network (CNN) instead. In this study, we develop CNN-based architectures for robust ASR for speech commands. We explore various depths of CNN’s layers of CNN architecture to improve a robust speech recognition system. We also optimized the best model using early stopping and two types of optimizers, i.e., Adam and SGD (Stochastic gradient descent). Our experiment shows that CNN exhibited an accuracy of 90.64%, while the DNN model exhibited 86.74% accuracy in clean conditions. In noisy conditions, an increasing number of CNN layers improves ASR’s robustness. The CNN method achieves 77.38% accuracy in clean conditions and 87.59% in noisy conditions.
Android-based Forest Fire Danger Rating Information System for Early Prevention of Forest / Land fires

The significant losses and negative impacts due to forest and land fires cause the need for an effort to prevent forest and land fires from an early stage. Therefore doing prevention before a disaster occurs has a low cost and smaller losses compared to efforts to manage disasters that have already occurred. The Remote Sensing Applications Center-LAPAN has produced fire hazard rating information. Information on fire hazard ratings which are up to date, fast and precise can be used to monitor forestry resources. The development of an accountable monitoring system can be a reference in efforts to control forest and land fires. At the same time participate in maintaining the sustainability of Indonesia’s natural resources sustainably. The development of an Android-based information dissemination system was created to facilitate the dissemination of information on fire hazard ratings to the public. This android-based information system provides information about the hazard rating of forest/land fires and analyzes the potential for forest/land fires and their spread through smartphones. It can be used to support decisionmaking to make strategic steps in preventing forest/land fires in Indonesia more widely.
QoS Analysis of Site-to-Site VPN and Its Integration Potential for Securing Communication on Electric Vehicles

Today, one of the challenges and hot issues related to Electric Vehicles (EV) is communication security, especially in the exchange of sensitive information on EVs. Virtual Private Network (VPN) is present as an alternative for those who need a communication network with reliable connectivity and security. VPNs have different types of protocols, some of which are Point-to-Point Tunneling Protocol (PPTP) and Ethernet over IP (EoIP) combined with IPSec (EoIP/IPSec). The purpose of this study aimed to analyze and compare the Quality of Service (QoS) generated by PPTP and EoIP/IPSec, then see the potential for integration of the combination between EoIP and IPSec as an effort of securing communications on EVs. QoS measurements were carried out using the TIPHON standard whose parameters consist of delay, jitter, packet loss, and throughput, as well as additional parameters such as CPU consumption and network security review. Throughput testing on the VPN client side was also carried out. The results of this study indicate that the combination of EoIP and IPSec, both in terms of performance and security, is possible and has the potential to be integrated into EVs.
Performance Comparison of Topic Modeling Algorithms on Indonesian Short Texts

The number of short texts produced daily has increased significantly as a form of social communication commonly used on the internet. Extracting topics from extensive collections of short texts is one of the most challenging tasks in natural language processing, but it has numerous applications in the real world. The purpose of this study is to compare the topic extraction performance of the Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), and Gibbs Sampling Dirichlet Multinomial Mixture (GSDMM) algorithms from Indonesian short texts. The data was gathered from news articles about electric vehicles published on the online news site (Kompas.com). Regarding topic coherence scores, our results show that LDA outperforms NMF and GSDMM. However, human judgment indicates that the word clusters produced by NMF and GSDMM are easier to conclude.
A 0.03 mm<sup>2</sup> Inductorless 50 Gb/s Multiplexer for Josephson Arbitrary Waveform Synthesizers

In this paper we present a high-speed 50 Gb/s current mode logic (CML) 2:1 Multiplexer (MUX) for driving of the Josephson Arbitrary Waveform Synthesizers (JAWS). JAWS can be used in quantum physics-related applications. For example, JAWS can be used as a voltage standard in metrology applications or as a reproducible quantum-based microwave signal source to control superconducting qubits in quantum computing. JAWS requires at its input a very long sigma-delta modulated sequence of current pulses, which is translated by the device into a highly accurate quantized analog voltage at its output. In that sense, JAWS can be seen as a quantum-accurate digital-analog converter (DAC). The very long sequence of digital bits is usually divided into several parts and stored in a memory. The parts are read out cyclically, and multiple parallel streams are serialized into a high-speed 50 Gb/s serial data stream using several multiplexer stages, providing the resulted combined control sequence to the JAWS input. The MUX presented in this work is the last stage of a multiplexer chain operating at the highest frequency and interfacing the JAWS. Quantum computers with a large number of qubits require scalable and power-efficient control electronics. To address this concern, we optimized the presented design for low power consumption to fit within a very low available cooling budget. The design is based on a 130 nm SiGe BiCMOS technology and operates from a 3V single supply voltage. The maximum data rate is 50 Gb/s with a 140 mV single-ended voltage swing. This circuit has a miniature core area of 0.03 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and a very low power consumption of 10 mW.
Developing Semantic Annotation Representation of Social Media Sentiments and Metadata as Resource Description Framework

Social Media has become a tool abiding the press in this modern society. Everyone can write their minds and build their mass media to publish opinions. Thus, in this manuscript, we develop a resource description framework scheme (RDFS) to enrich the information and metadata from Indonesian tweets regarding their New Capitol. This work focused on applying a popular method (i.e., the Tweetskb scheme) to construct the RDF of those tweets. We also developed the Schema to fulfill our need to contain all the information to RDF. RDF Triples were generated by connecting several established vocabularies to ensure the connection between its related nodes has meaning. The sentiment polarity (i.e., neutral, positive, and negative sentiment) is used in this manuscript. Thus, our proposal can be used as an initial work to make use of twitter's metadata to predict how reliable a user is, how the community interact with a certain topic, spam detection, clustering, and even implementing machine learning and deep learning sentiment analysis in a manner of knowledge graph.
SESS: Utilization of SPIN for Ethnomedicine Semantic Search

Indonesia has biodiversity which is very beneficial for human life. Existing applications for ethnomedicine have been developed using conventional methods that only utilized SPARQL Protocol and RDF Query Language (SPARQL), so they still have limitations in representing knowledge and its retrieval. Those conventional methods are which based of relational database and ontology that has not utilized inference in its query process. Therefore, this work proposed SPIN for Enthnomedicine Semantic Search (SESS), a framework of the semantic search for medicinal plants that were developed by using SPIN (SPARQL Inferencing Notation). SESS has two main parts, the ontology design included SPARQL Inferencing Notation (SPIN) library and query process. The experiments were assessed in terms of execution time, query variation and accuracy. The obtained results showed a ratio of precision at 1, recall at 0.98 and the average value of the f-measure was 0.99. Utilizing SPIN also decrease the time consuming to obtain the result by around .
AI verification platform using block chain with distributed ledger technology (DLT)

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Sanjeev Kumar Sharma; AI verification platform using block chain with distributed ledger technology (DLT). AIP Conf. Proc. 27 February 2023; 2427 (1): 020006. https://doi.org/10.1063/5.0101178 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Enhancement of K nearest neighbour approach to solve the issue of pattern classification

k Nearest Neighbour (kNN) method is a frequently implemented method for improve the pattern classification and solve the problem of imbalanced dataset that has discovered so many classification and clustering applications. In this paper, issues of classification were focused and changes to the nearest neighbouring method were suggested which exploit data from a dataset structure. The issue of class imbalance has recently attracted considerable attention from researchers in data mining strategies. The results of the present experiments using unique client identifier (UCI) repository datasets show that the classifiers generated the work much effective when compared with classic kNN & are much accurate, but not dramatically slow. This produced some fascinating and encouraging outcomes, which inspired more work to develop the kNN process. The purpose of this paper is to suggest versions of kNN which are appropriate for categorization of issues, since it leverage intrinsic of knowledge within data sets, like the composition of the datasets.
Overview of data mining with Python modules

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Sanjeev Kumar Sharma, Mrinal Paliwal; Overview of data mining with Python modules. AIP Conf. Proc. 27 February 2023; 2427 (1): 020070. https://doi.org/10.1063/5.0101171 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Mixtures of Probit Regression Models with Overlapping Clusters

With the advent of ubiquitous monitoring and measurement protocols, studies have started to focus more and more on complex, multivariate and heterogeneous datasets. In such studies, multivariate response variables are drawn from a heterogeneous population often in the presence of additional covariate information. In order to deal with this intrinsic heterogeneity, regression analyses have to be clustered for different groups of units. Up until now, mixture model approaches assigned units to distinct and non-overlapping groups. However, not rarely these units exhibit more complex organization and clustering. It is our aim to define a mixture of generalized linear models with overlapping clusters of units. This involves crucially an overlap function, that maps the coefficients of the parent clusters into the the coefficient of the multiple allocation units. We present a computationally efficient MCMC scheme that samples the posterior distribution of the parameters in the model. An example on a two-mode network study shows details of the implementation in the case of a multivariate probit regression setting. A simulation study shows the overall performance of the method, whereas an illustration of the voting behaviour on the US supreme court shows how the 9 justices split in two overlapping sets of justices.
Assessing the Corporate Performance via Managerial Autonomy in Oman: Mediating Role of Management Accounting Information System

The study aims to find out the ways in which corporate performance can be enhanced with the help of establishing managerial autonomy and implementing management accounting information systems in manufacturing organizations in Oman.The study is novel for considering factors of integration, reliability, flexibility, and efficiency to enhance corporate performance in Oman.In this qualitative study, the manufacturing sector has been considered, as it has shown significant growth that can impact Oman's economy positively if an efficient enough system is implemented.By applying analytical tests through SPSS and AMOS, results have shown that the impact of managerial autonomy and factors of efficiency, reliability, and flexibility in management accounting information systems are significant and positive on corporate performance in Oman.But the impact of integration is insignificant.Most of the organizations in Oman are yet in the decision-making process for the implementation of the Management Accounting Information System; the outcomes of this study can help in the decision-making, policy-making, and implementation processes, thus contributing to the performance enhancement of the manufacturing corporations in Oman.
Foreign Direct Investment in Less Developed Countries: Do Institutions Still Matter? Evidence for Central African Countries

This paper analyses the FDI flows determinants in less developed countries with evidence in Central African countries.We mention that if institutions matter in attracting FDI flows; this is not only due to good institutions or political regime types as focused in economic literature but also due to economic sectors which must be considered as investors decision procedure.
Adaptive Hybrid Bivariate Double Density Discrete and Complex Wavelet for Image Denoising

Image denoising is an important step in eliminating any noise impact in any image transmission process.Recently we presented two approaches for Bivariate based image denoising.They were Double Density Discrete Wavelet Transform (DD DWT) and Double Density Dual Tree Complex Wavelet Transform (DD CWT).In both techniques we decomposed noisy images with either DD DWT or DD CWT decompositions and then applied the Bivariate based denoising technique for noise removal.In this paper we propose an adaptive hybrid technique for Bivariate based image denoising that is based on the synthesis of DD-DWT bands or DD-CWT bands but with different weights, to deliver enhanced image features with less denoising impact especially around image edges, which is the most effected by noisy transmission channels.This proposed technique has been also enhanced by edge sharpening and Eigen analysis, as two separate stages.Simulation result comparisons have been performed between the proposed hybrid band adaptive DD-DWT and DD-CWT technique and the two primary techniques DD-DWT, DD-CWT, as well as other superior literature techniques such the original bivariate denoising technique with both original Complex Wavelet Transform and Double Density decompositions.This work in specific compares between Double Density DWT and Double Density CWT decompositions, proposes new filter design that suits each of them and proposes a hybrid technique between as will be shown.
Supervised Learning Algorithm on Unstructured Documents for the Classification of Job Offers: Case of Cameroun

Nowadays, in data science, supervised learning algorithms are frequently used to perform text classification.However, African textual data, in general, have been studied very little using these methods.This article notes the particularity of the data and measures the level of precision of predictions of naive Bayes algorithms, decision tree, and SVM (Support Vector Machine) on a corpus of computer jobs taken on the internet.This is due to the data imbalance problem in machine learning.However, this problem essentially focuses on the distribution of the number of documents in each class or subclass.Here, we delve deeper into the problem to the word count distribution in a set of documents.The results are compared with those obtained on a set of French IT offers.It appears that the precision of the classification varies between 88% and 90% for French offers against 67%, at most, for Cameroonian offers.The contribution of this study is twofold.Indeed, it clearly shows that, in a similar job category, job offers on the internet in Cameroon are more unstructured compared to those available in France, for example.Moreover, it makes it possible to emit a strong hypothesis according to which sets of texts having a symmetrical distribution of the number of words obtain better results with supervised learning algorithms.
Deep Learning in Autoencoder Framework and Shape Prior for Hand Gesture Recognition

Gesture is a form of nonverbal communication and is widely used in everyday life. Gesture recognition system facilitates human computer interaction thus creating an interface, which helps computer to understand human body language. In this article, an automated hand gesture recognition system is proposed using deep learning framework. The proposed scheme classifies the input hand gestures, each represented by a set of feature vector into some predefined number of gesture classes. The deep learning algorithm is used here to recognize hand gestures. The proposed scheme consists of mainly three stages: preprocessing, feature extraction, and classification. The images of hand gestures are stored in a database and are preprocessed to get into suitable form for further processing. The histogram-oriented gradient (HOG) feature vectors are then extracted from the preprocessed gesture images. The feature vectors are trained using deep learning techniques based on which the hand gesture from test set is classified into different classes. The proposed scheme is tested on three different hand gesture databases. The results obtained by the proposed scheme are compared with different state-of-the-art techniques and are found to be better as compared to the four existing state-of-the-art techniques. The performance of the proposed scheme is validated by using different percentage of the training samples with k-fold cross validation.