Reviewerly/exHarmony huggingface dataset:
Analisis Implementasi Link Aggregation dengan PIECES Framework pada Jaringan Transmisi Seluler

ABSTRAKPerkembangan layanan data seluler mulai teknologi 2G Enhanced GPRS (EGPRS) berkecepatan 473,6 kbps terus meningkat sampai teknologi 5G enhanced Mobile Broadband (eMBB) berkecepatan 10 Gbps berdampak pada penggunaan aplikasi eCommerce, media sosial, platform video dan meeting online juga semakin tinggi. Lokasi perangkat 2G sampai 5G berada dalam satu lokasi yang sama berdampak pada penggunaan perangkat dan jalur transmisi yang sama pula, sehingga utilisasi transmisi cepat mengalami congestion. Untuk mengatasi congestion digunakan metode Link Aggregation (LAG) untuk menambah kapasitas antara koneksi Indoor Unit (IDU) dengan router. Untuk mengevaluasi efektivitas implementasi LAG digunakan kerangka kerja Performance Information Economics Control Efficiency Service (PIECES). Dari evaluasi kerangka kerja PIECES dapat disimpulkan dengan implementasi LAG kapasitas link bertambah dari 1x1 Gbps menjadi 2x1 Gbps, congestion turun dari 68,07% menjadi 34,60%, performance meningkat 50% dan throughput meningkat 10,63 Mbps.Kata kunci: Transmisi seluler, congestion, kapasitas, LAG, PIECES ABSTRACTThe development of cellular data services is always growing from 2G Enhanced GPRS (EGPRS) with speed 473.6 kbps continues to 5G enhanced Mobile Broadband (eMBB) with speed up to 10 Gbps has an impact on the use of eCommerce, social media, video platforms and online meeting applications is also increasing. The location of 2G to 5G devices in the same location has an impact on the use of the same devices and transmission lines, so the utilization of transmission also getting congestion. To overcome congestion the Link Aggregation (LAG) method is used to add the capacity between Indoor Unit (IDU) and router connection. To evaluate the effectiveness of LAG implementation, the Performance Information Economics Control Efficiency Service (PIECES) framework is used. From the evaluation of PIECES framework, it can be concluded that implementation LAG make link capacity increased from 1x1 Gbps to 2x1 Gbps, congestion decreased from 68.07% to 34.60%, performance increased 50% and throughput increased 10.63 Mbps.Keywords: Cellular transmission, congestion, capacity, LAG, PIECES
Online fake job advertisement recognition and classification using machine learning

Machine learning algorithms handle numerous forms of data in real-world intelligent systems. With the advancement in technology and rigorous use of social media platforms, many job seekers and recruiters are actively working online. However, due to data and privacy breaches, one can become the target of perilous activates. The agencies and fraudsters entice the job seekers by using numerous methods, sources coming from virtual job-supplying websites. We aim to reduce the quantity of such fake and fraudulent attempts by providing predictions using Machine Learning. In our proposed approach, multiple classification models are used for better detection. This paper also presents different classifiers’ performance and compares results to enhance the results through various techniques for realistic results.
Temporal Residual Networks basedbeta-elliptic model and multi-head attentionfor online handwriting signature verification

Abstract In this work, we proposed a new system for online handwriting sig-nature verification based on beta elliptic modeling and TemporalResidual Neural Networks based Multi-Head Attention model. Thebeta-elliptic modeling was applied to divide the handwriting signaturetrajectory into strokes by inspecting the extremum velocity instantsand extract their dynamic and geometric proprieties. In the verifica-tion process, residual networks based temporal convolution was devel-oped to deals with sequential input data. Furthermore, we appliedMulti head attention model to focus on a few particular aspectsat a time and carry out an efficient and sequential data process-ing . The experiments were done on two public databases SVC-2004and SCUT-MMSIG, and we achieved the state-of-the-art performanceswith an Equal Error Rates equals to 0.114 and 0. 133 respectively.
Klasterisasi Penerima Bantuan Sosial Menggunakan Metode Simple Additive Weighting

This research is about a decision support system using the Simple Additive Weighting method to help determine the recipients of social assistance wich provided by the government with the aim to improve their standard of living. Wedomartani Village has a fairly large area with a large population. The problem is that the number of potential beneficiaries is not proportional to the available social assistance quota, so it is necessary to select recipients based on certain criteria to fit the target. The Simple Additive Weighting (SAW) method has been used to determine the nomination of candidates for social assistance. From the results of data processing for prospective recipients of Bantuan Langsung Tunai Dana Desa (BLT-DD) in 2020, If the largest 25 are taken from it, it shows that the distribution of the poverty level of the population in the area is not evenly distributed, so a policy made by the sub-district government is needed for more specific poverty reduction.
PEMODELAN HASIL REKAYASA KEBUTUHAN PERANGKAT LUNAK SISTEM JURNAL ELEKTRONIK TERINTEGRASI "IDEOGRAM"

E-Journal or Electronic Journal can be interpreted as online storage of information and communication. The characteristics of the E-Journal are that it utilizes electronic technology where publishers, writers, and readers can communicate with each other and use ICT, or written work data is stored so that it can be accessed anywhere and anytime when needed. One of the efforts to increase the effectiveness and efficiency of the scientific publication process requires using various ICTs. Design can use the electronic journal information system to solve problems in compiling a journal integration system based on software requirements analysis. In addition, the system development method used is a waterfall. The design uses UML, ERD, Wireframe, and database implementation using DBMS. The research results are realized in the form of a conceptual design of the system so that it can accommodate the needs to make the system. The development also can choose journals that are following their fields so that they can estimate open access journals based on the estimated submission time.
Pixel-Domain Just Noticeable Difference Modeling with Heterogeneous Color Features

With the rapidly emerging user-generated images, perception compression for color image is an inevitable mission. Whilst in existing just noticeable difference (JND) models, color-oriented features are not fully taken into account for coinciding with HVS perception characteristics, such as sensitivity, attention, and masking. To fully imitate the color perception process, we extract color-related feature parameters as local features, including color edge intensity and color complexity, as well as region-wise features, including color area proportion, color distribution position and color distribution dispersion, and inherent feature irrelevant to color content called color perception difference. Then, the potential interaction among them is analyzed and modeled as color contrast intensity. To utilize them, color uncertainty and color saliency are envisaged to emanate from feature integration in the information communication framework. Finally, color and uncertainty saliency models are applied to improve the conventional JND model, taking the masking and attention effect into consideration. Subjective and objective experiments validate the effectiveness of the proposed model, delivering superior noise concealment capacity compared with start-of-the-art works.
Low Resource Kannada Speech Recognition using Lattice Rescoring and Speech Synthesis

Objectives: Improving the accuracy of low resource speech recognition in a model trained on only 4 hours of transcribed continuous speech in Kannada language, using data augmentation.Methods: Baseline language model is augmented with unigram counts of words, that are present in the Wikipedia text corpus but absent in the baseline, for initial decoding.Lattice rescoring is then applied using the language model augmented with Wikipedia text.Speech synthesis-based augmentation with multi-speaker syllable-based synthesis, using voices in Kannada and cross-lingual Telugu languages, is employed.We synthesize basic syllables, syllables with consonant conjuncts, and words that contain syllables that are absent in the training speech, for Kannada language.Findings: An overall word error rate (WER) of 9.04% is achieved over a baseline WER of 40.93%.Language model augmentation and lattice rescoring gives an absolute improvement of 16.68%.Applying our method of syllable-based speech synthesis over language model augmentation and rescoring yields a total reduction of 31.89% in WER.The proposed approach of language model augmentation is memory efficient and consumes only 1/8 th the memory required for decoding with Wikipedia augmented language model (2 gigabytes versus 18 gigabytes) while giving comparable WER (22.95% for Wikipedia versus 24.25% for our method).Augmentation with synthesized syllables enhances the ability of the speech recognition model to recognize basic sounds thus improving recognition of out-of-vocabulary words to 90% and in-vocabulary words to 97%.Novelty: We propose novel methods of language model augmentation and synthesis-based augmentation to achieve low WER for a speech recognition model trained on only 4 hours of continuous speech.Obtaining high recognition accuracy (or low WER) for very small speech corpus is a challenge.In this paper, we demonstrate that high accuracy can be achieved using data augmentation for a small corpus-based speech recognition.
A Scalable High Utilization Itemset Mining Technique for Large Datasets Using A Bit-Based Model

Objectives: Utility-list based algorithms have gained a lot of traction due to their efficiency and the ease with which they may be modified.While there have been some enhancements, the problem of inefficiency persists.This research presents a solution to this issue by enhancing the utility-list building process, a crucial function that has received little attention in previous studies.Also, the research aims at reducing memory complexity and better performance than existing approaches.Methods: To expedite building, a new set of bitwise operations termed Bit combine construction (BCC) is proposed.In addition, BCC is supported by a unique data format called EBP (Efficiency Bit Partition).An innovative EBP-Miner algorithm is developed with this framework in mind, and it uses many techniques to narrow the search field.Findings: On widely used baseline methods, experimental findings reveal that EBP-Miner outperforms numerous state-of-the-art techniques, including FEACP as well as CLH-Miner approaches.The experiments were conducted with utilization value ranging from 20% to 100% of the nodes.The proposed system achieves an average of 390s runtime and utilization value of 90.25% which are outperformed the existing methods.Also, the approach has proven 20% lesser memory complexity than of the existing algorithms.Novelty: In the field of data mining, high utility itemset mining (HUIM) is an important challenge.The idea is to discover groups of data in a database that are particularly significant or profitable in order to unearth information that can aid in making decisions.The novelty of this study is on developing a better method for building algorithms for HUIM that make use of a bitwise data structure, and on suggesting a more time-and effort-effective strategy for building utility-lists.
Effects of Foreign Direct Investment on Total Factor Productivity in CEMAC

his article aims to analyze the effects of foreign direct investment (IDE) on total factor productivity (PTF) in the Economic and Monetary Community of Central Africa (CEMAC). Using annual data for the period 1990-2020, the methodology was based on the Generalized Moments Method (MMG). The results of this research indicate that three variables positively influence total factor production. These are total factor productivity lagged one period, foreign direct investment and human capital. On the other hand, trade openness, labor force, gross fixed capital formation and political stability negatively explain total factor production. This suggests a series of policy recommendations.
NETWORK OF INFORMAL TOURISM EDUCATION ORGANIZATIONS IN PARE ENGLISH VILLAGE

The learning model is a strategy used by teachers to increase learning motivation, learning attitudes among students, able to think critically, have social skills, and achieve more optimal learning outcomes. Referring to this, the development of learning models continues to change from traditional models to more modern models. Researchers used the ethnographic method during a pandemic for six months, January-June 2020. This method was considered appropriate to obtain data on the lives and actions of a group in certain situations in society from the point of view of their culture of life. The data collection process, both primary and secondary, is carried out continuously. Based on the results of the research, it can be concluded that there are several things that support the success of students studying at English language educational institutions in Pare. The learning method is one of the supporting factors. For this reason, the method used is the 'Boarding Teaching' and 'In Class Scheduled' methods. These two methods are carried out by carrying the concept of 'fun teaching' or an interesting method that does not seem too formal and rigid. By using this kind of method students become more interested and enthusiastic to learn. In fact, the point is that the learning that is carried out focuses on practice, because English is a skill, not just knowledge. In general, the learning model carried out in English language education institutions in Pare is a similar method, namely the 'Fun Englsih' method, which is learning English in a pleasant environmental situation and games that are usually carried out to train students to use English and make it easier for students remember the material being taught such as vocabulary and expressions in English. Another method used is group discussion or group discussion.
Hybrid Approach-RSMOTE for Handling Class Imbalance with Label Noise

The class imbalance problem is the main problem in classification.This issue arises because real-world datasets frequently exhibit an imbalance as a result of a class with more instances than other classes.In handling class imbalance, a Hybrid Approach that blends data-level and algorithm-level approaches produce good results.However, apart from the class imbalance, which reduces classification accuracy, the complexity of the data also has an effect.The complexity of this data causes a minority noise sample which lies between the minority and the majority.In order to determine how close minority samples are to their homogeneous and heterogeneous nearest neighbors, it is necessary to calculate the relative density.The greater the proximity to the homogeneous nearest neighbors, the greater the relative density, which causes the minority samples to be in a safe state but otherwise be categorized as noisy samples.This research will combine the application of the Hybrid Approach with A self-adaptive Robust SMOTE (RSMOTE), which is an adaptive method from SMOTE that applies the concept of relative density in the over-sampling process on minority samples.The research contribution is to implement the Hybrid Approach-RSMOTE in handling class imbalance with noise by using relative density in over-sampling and also to improve classification performance.The results showed that the Hybrid Approach-RSMOTE and Hybrid Approach-SMOTE had given good results in handling class imbalance.However, the Hybrid Approach-RSMOTE gave better results in the Precision, Recall, F1-Measure, and G-Mean and showed significant differences.Based on the results of the study, it can be stated that the performance of the Hybrid Approach in handling class imbalance is influenced by the selection of the over-sampling method.The results show that RSMOTE can be considered an over-sampling method in the Hybrid Approach.
Cloud Monitoring Data Analysis and Simulation Based on Machine Learning

With the rapid development of Internet technology, cloud computing has gradually been widely used in people's lives.However, due to the huge and complex data volume, limited storage space and maintenance difficulties, there are still some problems in cloud monitoring data analysis.This paper intends to use the combination of machine learning to study the analysis and experimental simulation of cloud monitoring data, in order to improve the data processing ability of the monitoring system.This paper mainly uses the experimental analysis method and information entropy to study the cloud monitoring system and its data processing and analysis capabilities.The experimental data show that the error of computer tools is less than 6%, which shows that the method is feasible and worthy of application.
A Deep Neural Network Model for Realtime Semantic-Segmentation Video Processing supported to Autonomous Vehicles

Traffic congestion has been a huge problem, especially in urban area during peak hours, which causes a major problem for any unmanned/autonomous vehicles and also accumulate environmental pollution. The solutions for managing and monitoring the traffic flow is challenging that not only asks for performing accurately and flexibly on routes but also requires the lowest installation costs. In this paper, we propose a synthetic method that uses deep learning-based video processing to derive density of traffic object over infrastructure which can support usefull information for autonomous vehicles in a smart control system. The idea is using the semantic segmentation, which is the process of linking each pixel in an image to a class label to produce masked map that support collecting class distribution among each frame. Moreover, an aerial dataset named Saigon Aerial with more than 110 samples is also created in this paper to support unique observation in a biggest city in Vietnam, HoChiMinh city. To present our idea, we evaluated different semantic segmentation models on 2 datasets: Saigon Aerial and UAVid. Also to track our model’s performance, F1 and Mean Intersection over Union metrics are also taken into account. The code and dataset are uploaded to Github and Kaggle repository respectively as follow: Saigon Aerial Code , Saigon Aerial dataset .
Analysis of Combination Algorithms for Denoising and Contrast Enhancement Images

Reducing noise and increasing image contrast is part of the purpose of enhancing image quality; instead, it will impact change the diversity of information in the image based on the Shannon entropy value. Decrease quality caused by noise salt and pepper in this research or abnormal contrast in the image causes objects in the image to become unclear. Low contrast has a major impact on image quality, including noise reduction processes affecting image information so that the quality of the reduced image becomes something to consider for large noise. Iterative Denoising and Backward Projections with CNN (IDBP-CNN) and Different Applied Median Filter (DAMF) is a good solution for denoising a large percentage of noise with good quality results image. In other research for contrast enhancement, Triangular Fuzzy Membership-Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) and Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving (AFCEDP) is claimed to a good solution to solve low contrast of the image. Therefore, this study is to find the best combination of denoising and contrast enhancement to get good image results with step denoising followed by contrast enhancement. Based on the experimental testing is got the best combination is the DAMF + AFCEDP algorithm with an average of PSNR 35dB and an average difference Shannon entropy of 0.0130.
Recognition of Balinese Traditional Ornament Carving Images with Convolutional Neural Network and Discrete Wavelet Transform

Balinese carvings are less known to the public due to the lack of information about Balinese carvings. Minimum information about Balinese carvings can be overcome by utilizing advances in information technology in the field of image processing, namely the introduction of Balinese carving patterns. In the pattern recognition model of an image, there are several things that can be analyzed, such as the recognition method used, feature extraction, including the model in preprocessing to reduce noise in a Balinese carving image. In this study, the Convolutional Neural Network (CNN) was used to classify Balinese carving images combined with Discrete Wavelet Transform (DWT) in extracting image features. The introduction was made to 25 categories of Balinese carving ornaments. Tests are generated based on the level of accuracy generated in the testing process. Analysis of the results was carried out on the resulting model, namely the analysis of the combination of CNN with DWT and without DWT. Testing the data set with 212 training data and 129 testing data using all DWT channels. Based on the results of the tests that have been carried out, it is found that using the DWT extraction feature produces a higher testing accuracy value, namely 35.66% for 25 classes and 74, 42% for 3 carving classes. Meanwhile, without using DWT, it produces an accuracy value of 32.56% for 25 classes and 66.67% for 3 carving classes. In future research, it is hoped that there will be an improvement in the data set and good shooting with a balanced and adequate number for the 25 carving classes that have been obtained.
Anti-Forensics with Steganographic File Embedding in Digital Image Using Genetic Algorithm

In this study, a steganography method on digital images as anti-forensics by utilizing genetic algorithms was proposed. Genetic Algorithms are artificial intelligence whose functions are optimization and search. The purpose of this research is to optimize steganography as anti-forensic by applying a Genetic Algorithm and combined with the Hilbert curve, lempel Ziv Markov chain, and least significant bit. The result provides a new steganography method by combining various existing methods. The proposed method will be tested for image quality using PSNR, SSIM, Chi-Squared steganalysis and RS-Analysis, and extraction test. The novelty obtained from the developed method is that the steganography method is as optimal as anti-forensic in keeping confidential data, has a large embedding capacity, and is able to be undetected using forensic methods. The results can maintain data confidentiality, have a large embedding capacity, and are able to be undetected using forensic methods. The proposed method got better performance rather than the previous method because PSNR and SSIM values are high, secret data can be received back as long as the pixel value doesn't change, and the size of the embedding capacity. The proposed method has more ability to embed various types of payload/ secret data because of the way it works, which splits byte files into binary. The proposed method also has the ability not to be detected when forensic image testing is carried out.