Relational Autoencoder for Feature Extraction

Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new features. In this paper, we propose a Relation Autoencoder model considering both data features and their relationships. We also extend it to work with other major autoencoder models including Sparse Autoencoder, Denoising Autoencoder and Variational Autoencoder. The proposed relational autoencoder models are evaluated on a set of benchmark datasets and the experimental results show that considering data relationships can generate more robust features which achieve lower construction loss and then lower error rate in further classification compared to the other variants of autoencoders.Comment: IJCNN-201

PENERAPAN OPTIMASI ALGORITMA C45 DENGAN NAIVE BAYES PADA PEMILIHAN INTERNET SERVICE PROVIDER

Internet Service Provider adalah sebuah badan usaha yang bergerak di bidang jasa pengelola Internet. Sebagai sebuah badan usaha penyedia jasa internet yang bersifat komersil, ISP memiliki penilaian tersendiri dari calon cutomer. Penilaian yang biasanya digunakan sebagai tolak ukur pemilihan ISP oleh calon customer antara lain adalah penilaian service hotline, nilai Customer Interconnect Rate (CIR), Harga , Customer Satisfactions, SLA dan Perangkat Last Mile. Permasalahan yang biasanya muncul pada calon customer adalah penentuan prioritas penilaian pemilihan terhadap beberapa parameter penilaian ISP. Hal yang dapat dilakukan adalah dengan menggunakan teknik klasifikasi untuk memprioritaskan penilaian dalam pemilihan ISP. Pada penelitian yang dilakukan, pemilihan algoritma klasifikasi C45 digunakan sebagai algoritma yang membentuk prioritas penilaian dalam pemilihan ISP dalam bentuk pohon keputusan. Hal ini didasarkan atas keunggulan algoritma C45 yang mampu memangkas struktur hierarki pohon keputusan terhadap parameter yang dimiliki dalam proses klasifikasi sehingga memudahkan dalam implementasi pengambilan keputusan. Untuk menangani adanya pembentukan akar yang memiliki nilai ganda pada perhitungan gain ratio di algoritma C45, pada penelitian yang dilakukan disempurnakan dengan konsep probabilitas yaitu naïve bayes. Hasil dari penelitian yang dilakukan adalah adanya model pohon keputusan yang dapat digunakan untuk penilaian pemilihan ISP dengan algoritma klasifikasi C45 yang dioptimalkan dengan naïve bayes

Naïve Bayesian Classifier for Selecting Good/Bad Projects during the Early Stage of International Construction Bidding Decisions

Since the 1970s, revenues generated by Korean contractors in international construction have increased rapidly, exceeding USD 70 billion per year in recent years. However, Korean contractors face significant risks from market uncertainty and sensitivity to economic volatility and technical difficulties. As the volatility of these risks threatens project profitability, approximately 15% of bad projects were found to account for 74% of losses from the same international construction sector. Anticipating bad projects via preemptive risk management can better prevent losses so that contractors can enhance the efficiency of bidding decisions during the early stages of a project cycle. In line with these objectives, this paper examines the effect of such factors on the degree of project profitability. The Naïve Bayesian classifier is applied to identify a good project screening tool, which increases practical applicability using binomial variables with limited information that is obtainable in the early stages. The proposed model produced superior classification results that adequately reflect contractor views of risk. It is anticipated that when users apply the proposed model based on their own knowledge and expertise, overall firm profit rates will increase as a result of early abandonment of bad projects as well as the prioritization of good projects before final bidding decisions are made

A PROBABILISTIC MACHINE LEARNING FRAMEWORK FOR CLOUD RESOURCE SELECTION ON THE CLOUD

The execution of the scientific applications on the Cloud comes with great flexibility, scalability, cost-effectiveness, and substantial computing power. Market-leading Cloud service providers such as Amazon Web service (AWS), Azure, Google Cloud Platform (GCP) offer various general purposes, memory-intensive, and compute-intensive Cloud instances for the execution of scientific applications. The scientific community, especially small research institutions and undergraduate universities, face many hurdles while conducting high-performance computing research in the absence of large dedicated clusters. The Cloud provides a lucrative alternative to dedicated clusters, however a wide range of Cloud computing choices makes the instance selection for the end-users. This thesis aims to simplify Cloud instance selection for end-users by proposing a probabilistic machine learning framework to allow to users select a suitable Cloud instance for their scientific applications. This research builds on the previously proposed A2Cloud-RF framework that recommends high-performing Cloud instances by profiling the application and the selected Cloud instances. The framework produces a set of objective scores called the A2Cloud scores, which denote the compatibility level between the application and the selected Cloud instances. When used alone, the A2Cloud scores become increasingly unwieldy with an increasing number of tested Cloud instances. Additionally, the framework only examines the raw application performance and does not consider the execution cost to guide resource selection. To improve the usability of the framework and assist with economical instance selection, this research adds two Naïve Bayes (NB) classifiers that consider both the application’s performance and execution cost. These NB classifiers include: 1) NB with a Random Forest Classifier (RFC) and 2) a standalone NB module. Naïve Bayes with a Random Forest Classifier (RFC) augments the A2Cloud-RF framework\u27s final instance ratings with the execution cost metric. In the training phase, the classifier builds the frequency and probability tables. The classifier recommends a Cloud instance based on the highest posterior probability for the selected application. The standalone NB classifier uses the generated A2Cloud score (an intermediate result from the A2Cloud-RF framework) and execution cost metric to construct an NB classifier. The NB classifier forms a frequency table and probability (prior and likelihood) tables. For recommending a Cloud instance for a test application, the classifier calculates the highest posterior probability for all of the Cloud instances. The classifier recommends a Cloud instance with the highest posterior probability. This study performs the execution of eight real-world applications on 20 Cloud instances from AWS, Azure, GCP, and Linode. We train the NB classifiers using 80% of this dataset and employ the remaining 20% for testing. The testing yields more than 90% recommendation accuracy for the chosen applications and Cloud instances. Because of the imbalanced nature of the dataset and multi-class nature of classification, we consider the confusion matrix (true positive, false positive, true negative, and false negative) and F1 score with above 0.9 scores to describe the model performance. The final goal of this research is to make Cloud computing an accessible resource for conducting high-performance scientific executions by enabling users to select an effective Cloud instance from across multiple providers

Advances in Information Security and Privacy

With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue