Reviewerly/exHarmony huggingface dataset:
Robust Line-Based Radial Distortion Estimation From a Single Image

The pinhole model utilized in most computer vision algorithms becomes unfeasible because of lens distortion. Thus it is a must to compensate lens distortion to make the pinhole model available. In this paper, we propose a new robust line-based distortion estimation method to correct radial distortion. Our method works from a single image and is able to estimate the distortion center rather than assuming it is at the image center. Distortion parameters are estimated from parameters of circulars arcs, on the basis that straight lines are imaged as circular arcs under one-parameter division model. A new feature selection scheme by refining circular arcs is introduced to make the process of distortion estimation fully automatic and more robust. Moreover, a linear optimization algorithm is applied to calculating parameters in each selection run, making our feature selection scheme more efficient. Experiments on synthetic images and real images show that our method performs well in radial distortion estimation even with severely distorted images.
Different Decision Fusion Methods for Modular Common Vector Approach

The partial occlusions in the face image negatively affect the performance of the face recognition system. Modular versions of some methods are used to overcome this problem. Modular Common Vector Approach(MCVA) was successfully applied partial occlusion problem. In this work, we apply some well-known decision fusion methods (product rule, borda count, and majority voting) to the decision stage of MCVA approach to increase the performance. Several face recognition experiments conducted on AR face database provide promising results.
Improving Calculation Accuracy of Digital Filters Based on Finite Field Algebra

The applications of digital filters based on finite field algebra codes require their conjugation with positional computing structures. Here arises the task of algorithms and structures developed for converting the positional notation codes to finite field algebra codes. The paper proposes a method for codes conversion that possesses several advantages over existing methods. The possibilities and benefits of optimization of the computational channel structure for digital filter functioning based on the codes of finite field algebra are shown. The modified structure of computational channel is introduced. It differs from the traditional structure by the fact that there is no explicit code converter in it. The main principle is that the “reference” values of input samples, which are free from the error of the analog-digital converter, are used as input samples. The proposed approach allows achieving a higher quality of signal processing in advanced digital filters.
Web Attacks Detection Based on Patterns of Sessions

In recent years, we continued to see a trend of increasing number of web application vulnerabilities. Web application firewall or WAF for short is widely used to detect known attacks on web applications. Unfortunately, WAF is a signature intrusion detection and prevention system. Therefore, WAF requires the creation and maintenance of a large number of rules up to date. In addition, as practice shows, detection rules often require customization for a particular application. Therefore, for a more complete protection, WAF should be supplemented with an anomaly detection system. The article proposes a hybrid anomaly detection system based on a user session model. Anomaly detection is based on the detection of deviations of the current user session from the reference session model based on the variable order Markov model. To improve the reliability of attack detection, when assessing the session anomaly, models for the validity of the values of the HTTP request and SQL queries to the database are created for each stage of the session.
Development of Incident Response Tool for Cyber Security Training Based on Virtualization and Cloud

We developed a virtualization-based infringement incident response tool for cyber security training system using Cloud. This tool was developed by applying the concept of attack and defense which is the basic of military war game modeling and simulation. The main purpose of this software is to cultivate cyber security experts capable of coping with various situations to minimize the damage in the shortest time when an infringement incident occurred. This tool acquired the invaluable certificate from Korean government agency. This tool shall provide CBT type remote education such as scenario based infringement incident response training, hacking defense practice, and vulnerability measure practice. The tool works in Linux, Window operating system environments, and uses Korean e-government framework and secure coding to construct a situation similar to the actual information system. In the near future, Internet and devices connected to the Internet will be greatly enlarged, and cyber security threats will be diverse and widespread. It is expected that various kinds of hacking will be attempted in an advanced types using artificial intelligence technology. Therefore, we are working on applying the artificial intelligence technology to the current infringement incident response tool to cope with these evolving threats.
Digital Self-Learning: Engaging Students in Studying Salat Al-Janazah (The Islamic Funeral Prayer) Based-on Digital Self-Learning

Abstract As a part of Muslim society, students have a responsibility in doing Salat Al-Janazah, the ritual of prayer if there is a Muslim died. The ability to perform Salat Al-Janazah is included as part of compulsory subjects in schools. Unfortunately, there are still many students who have difficulty learning this prayer. The difficulty is due to the difference between the Salat Al- Janazah and the obligatory prayers. In addition, the prayer is not performed every day, so it is possible students learn it when in need. This problem can be solved through the technological approach. This research aims to (1) develop a digital self-learning to help the student in learning Salat Al-Janazzah by themselves; (2) Evaluate the quality of the media developed based on ISO/IEC 25010 on functional suitability, performance efficiency, compatibility, and usability aspects. In the media developed, the basic knowledge in learning Salat Al-Janazah will be transformed into digital-based material. By using digital self-learning, students can study in flexibility pattern and learn in comfortable rhythm according to their situation. Testing the quality of all aspects of ISO/IEC 25010 showing the media developed already eligible for use in learning Salat Al-Janazah.
Text-Augmented Knowledge Representation Learning Based on Convolutional Network

Knowledge graphs describe concepts, entities in the objective world and relations in a structured form, thus providing a better way to manage and understand the infinite information on the Internet. Although there are various knowledge embedding models, most of them only focus on factual triples. In fact, there are usually concise descriptions for entities, which cannot be well employed by these existing models. For instance, a knowledge embedding model based on convolutional networks (ConvKB [9]), has shown remarkable results in the knowledge link prediction, which have not fully utilized the complementary texts of entities. Therefore, we propose a text-augmented embedding model based on ConvKB, which firstly uses bidirectional short and long term memory network with attention (A-BiLSTM) to encode the descriptions of the entities, then combines the structure of the symbol triples embeddings and text embeddings with novel gate mechanism (in the form of the LSTM gates). In this way, structural representations and textual representations can all be learned. The experiments have shown that our method is superior to the previous ConvKB in tasks like link prediction.
Towards Designing Agent Based Virtual Reality Applications for Cybersecurity Training

Agents are becoming increasingly ubiquitous in people's lives. There is potential for agents to help with scalability of digital learning and training. This paper presents the findings from two ideation workshops centered around designing a short interaction with a digital agent in Virtual Reality (VR), in the context of cybersecurity training. The workshop discussions were subjected to thematic analysis to extract design insights. The design insights fall into three broad categories: thematic (e.g. different metaphors to translate cybersecurity concepts), stylistic (e.g. less realistic art), and mechanical (e.g. VR gestures). These insights will guide the design and development of a VR training environment.
A Modified Naïve Bayesian-based Spam Filter using Support Vector Machine

The ever-growing problem which is threatening the current mailing system is spam. Spam is nothing but an unsolicited bulk e-mail frequently sent in a financial nature which generates the need for creating an anti-spam filter. Amongst many spam filtering techniques, the most advanced method "Naïve Bayesian filtering" using the Support Vector Machine (SVM) have been implemented. Spammers are very careful about the filtering techniques. For that very reason, dynamic filtering is needed and the proposed method meets the demand. The algorithm splits the received email into tokens and uses Bayes' theorem of probability to calculate the probability of spam for each token to determine the total spam probability of the mail. Implementation of SVM instead of corpora is one of the added features of the algorithm. The most challenging feature was to take the words as well as whole sentences as input in the SVM as tokens and feature vectors. The inclusion of sentences in the dataset training has increased the accuracy of detecting spam and ham. Natural Language Tool Kit (NLTK) has been used as a useful language processing tool to tokenize the sentences and also to understand the meaning of the same types of sentences to some extent. As a test mail is being compared by word to word and also sentence to sentence from the training datasets to determine if the mail is spam or not, it will improve the performance of the filter. With some simple modifications, the filter can be used in both server and client end. The efficiency increases gradually with the increased number of email it processes.
On the Efficacy of Dynamic Behavior Comparison for Judging Functional Equivalence

Since it was first proposed in 1992 under the name of "behavior sampling", the idea of judging whether software systems are functionally equivalent by observing their responses to common stimuli (i.e. tests) has been used for a range of tasks such as software retrieval, functional redundancy measurement and semantic clone detection. However, its efficacy has only been studied in one small experiment, with limited generalizability, described in the original paper proposing the approach. The results of that experiment suggest that a relatively small number of randomly generated tests (i.e. 4) is sufficient to recognize non-functional-equivalent software 85% of the time. This number has therefore been adopted as "sufficient" in numerous applications of the approach. In this paper we present a much larger study which suggests at least 39 randomly generated tests are actually needed to achieve this level of effectiveness, but that a far fewer number of tests generated using coverage-based heuristics are sufficient. Since these results are much more generalizable, they have implications for future applications of behavioral sampling for dynamic behavior comparison.
Mixed Reality Application as a Learning System of Motion Systems using Pyramid Hologram Technology

Abstract The purpose of this study was to develop Biology learning media with Mixed Reality applications using Pyramid Holograms. This research is a qualitative descriptive study. The research was conducted in two stages, namely the problem identification stage and the development stage. The problem identification stage is done by questionnaire and student test results and the development stage by designing Mixed Reality media. The results of the study were in the form of motion system material media with Pyramid Hologram technology that can be used in the learning process.
The Use of Discrete Meyer Wavelet for Speech Segmentation

This work describes the segmentation of oral speech in the Yakut language. The phonetic structure of the Yakut language is described. For segmentation of sound recording, wavelet analysis is used. The choice of a discrete Meyer wavelet is based. The wavelet library of the Scilab open source software package is described. The results of calculations in the Scilab package are presented. The automatic segmentation of oral speech in the Yakut language is investigated. An example of sound segmentation is given. Reasons for reducing accuracy and complexity in speech segmentation are identified.
Theory Ordinals Can Replace ZFC in Computer Science

The theory Ordinals can serve as a replacement for the theory ZFC because:• Ordinals are a very well understood mathematical structure.• There is only one model of Ordinals up to a unique isomorphism, which decides every proposition of the theory Ordinals in the model.• The theory Ordinals is much more powerful than ZFC. Standard mathematics that has been carried out in ZFC can more easily be done in Ordinals. Axioms of ZFC are in effect theorems of Ordinals. The type Ordinal is larger than any set in ZFC because no set can be placed in one-to-one correspondence with instances of the type Ordinal.• The theory Ordinals is algorithmically inexhaustible, i.e., it is impossible to computationally enumerate theorems of the theory thereby reinforcing the intuition behind [Franzén, 2004]. Contrary to [Church 1934], the conclusion in this article is to abandon the assumption that theorems of a theory must be computationally enumerable while retaining the requirement that proof checking must be computationally decidable.• There are no “monsters” [Lakatos 1976] in models of Ordinals such as the ones in models of 1st-order ZFC. Consequently unlike ZFC, the theory Ordinals is not subject to cyberattacks using “monsters” in models such as the ones that plague 1st-order ZFC.• The theory Ordinals formally proves its own consistency contra [Gödel 1931]• Longstanding issues such as the nonexistence of large cardinals and the nonexistence of nonstandard models [Cohen 1966] are resolved in the theory Ordinals.The theory Ordinals is based on intensional types as opposed to extensional sets of ZFC. Using intensional types together with strongly-typed ordinal induction is key to proving that there is just one model of the theory Ordinals up to a unique isomorphism.
Applying Ontology Knowledge Representation Technology and Semantic Searching Methods to Support the Production of High Quality Longan Fruit

The objectives of this research were to develop and ontology and apply semantic search methods to the management of knowledge on the production of the tropical fruit, longan, in Northern Thailand. Factors affecting the quality of longan includes thinning of flowering crops, timing and methods of pruning the trees, the use of potassium chlorate to stimulate flowering, appropriate fertilization, and eradication of diseases and pest insects. The information gained from a substantial literature review on these matters is presented in three sections in the paper: (1) knowledge collection, and (2) the development of an ontology consisting of 7 major information classes, and (3) illustration of the development of ontology-based information accessing systems with semantic searching capabilities. Our testing of information retrieval from the ontology using semantic searching showed a Precision of 100% for information retrieved, with Recall of 93.50% and F-measure 96.64%. These results supported the further implementation of the Thai Longan Production Knowledge Service System.
A Region-based Level Set Formulation Using Machine Learning Approach in Medical Image Segmentation

A new region-based active contour model in level set formulation is proposed to segment medical images with poorly defined boundaries. From literature, it is observed that the traditional methods often fail to detect weak boundaries for images with intensity inhomogeneity. However, the machine learning (ML) algorithms are highly effective for such images but due to the noise most pixels are misclassified. Therefore in this paper, we propose a region-based active contour model using ML. In this paper, we consider an active contour driven by local Gaussian distribution (LGD) fitting energy which is known as LGD model. Further, this active contour LGD model is integrated with fuzzy k-nearest neighbor (k-NN) for added accurate segmentation. Also the energy stop function (ESF) of LGD model is modified to combine with k-NN. The results obtained are compared with the existing state-of-the-art models and the proposed method is clearly a triumph. The experimental results proves that the proposed model provides higher accuracy results for medical image segmentation and is robust compared to the other existing methods.
Information Retrieval System in Bangla Document Ranking using Latent Semantic Indexing

Nowadays, like the English and other languages, Bangla also plays a significant role to strengthen the web repository. The storing rate of Bangla information is augmented day-by-day. Because of the numerous documents in the World Wide Web, it is very difficult for a user to retrieve the desired information. Furthermore, finding the useful documents tends to be more time spending as well as an annoying job. These demands emerge to develop an Information Retrieval (IR) system to document ranking for Bangla language. In this paper, we have built such a retrieval system where users can find their needed documents which correspond to their own query strings throughout the ranking index. Although a lot of works have been done for English and other languages to rank the documents, unfortunately, we have found a very negligible amount of contributions in Bangla Language. Many methods such as - Boolean model, Maximal Marginal Relevance (MMR), Portfolio Theory (PR), Quantum Probability Ranking Principle (QPRP), Query Directed Clustering (QDC), Vector-based TFIDF and so on, have been proposed to implement the document ranking system. Here, we have applied a new approach, called Latent Semantic Indexing (LSI) to do the same task for Bangla documents. LSI uses the mathematical method called Singular Value Decomposition (SVD). After that, we have applied the cosine similarity to rank all the documents. We believe that the performance result of our proposed system has reached the trustworthy level.
Combination Base64 and GOST algorithm for security process

Abstract Cryptography in various communications both online and offline is very necessary and generally cryptography is used to secure data in the form of text while non-text data requires special algorithms. Base64 is an encoding algorithm that can convert non-text data into binary data so that the data can be secured with cryptographic algorithms such as the GOST algorithm. The combination of the BASE64 and GOST algorithms is expected to be able to secure all types of data that exist and have the ease of returning to their original form without damaging the original data, besides this combination will increase data security because the combination of these two algorithms produces ciphertext that is difficult to know if the process is done cryptanalyst by third parties. The results of the combination of Base64 and GOST algorithms are expected to be able to deliver dynamic security contributions to various existing data objects so that they can be used both online and offline.
Proposal for a direct measurement of the von Neumann entropy and the relative entropy of coherence

Quantum coherence is the most distinguished signature of quantum mechanics, also recognized to be an essential resource for many promising quantum technologies, playing a central role in phenomena related to quantum information science, quantum thermodynamics and quantum biology, just to mention few examples. However, the resource theory of coherence is in the preliminary stage of its development, being still limited to the study of the manipulation and quantication of coherence, mostly from a theoretical viewpoint. Here, we propose an experimental method to directly measure the relative entropy of coherence, which according to the resource theory is one of the main quantiers of coherence. To achieve this aim, we discuss a procedure to measure the von Neumann entropy of a generic quantum state directly in terms of the Shannon entropy of the probability distribution of outcomes in a defined measurement basis. In both cases, by direct we mean that tomographic methods are not required. Two quantum-optical applications of our method are discussed in order to give support to our predictions.
Adaptation of Structure of Information-Measuring System Based on Fuzzy Logic

It is shown that modern information-measuring systems (IMS) need to function under conditions of uncertainty because of various reasons. The paper considers the uncertainty of the model type of the object of research, given in the form of stiff system of differential equations (SDE) and of non-stiff SDE as well. It is noted that the results of processing real time measurements depend on the timeliness and correctness of the decision on the SDE stiffness and, accordingly, on the need to use a “non-stiff filter” from the IMS algorithms database. The paper considers the problem of synthesis of the algorithm to filter measurements and to adapt the IMS software under conditions of the stiff SDE describing the model of the information process. The non-stiff filtering algorithm and the structure of an operatively advising expert system designed to automate the procedure to select specific filtering algorithm depending on the current problem situation are synthesized. The system of fuzzy production rules describing the logic of the operatively advising expert system is given.
MRI-based brain tumor segmentation using Gaussian mixture model with reversible jump Markov chain Monte Carlo algorithm

A brain tumor is the 15th deadly disease in Indonesia according to the WHO in 2018. In medical treatment, brain tumors can be detected through Magnetic Resonance Imaging (MRI). The main problem is how to separate the brain tumor area as the Region of interest (ROI) with the other healthy part (Non-ROI) in the MRI. In the computational statistics, a method used in image segmentation is cluster analysis. Model-Based Clustering with Gaussian Mixture Model (GMM) is often used to find the cluster where the tumor is placed. The EM Algorithm and Bayesian coupled with Markov chain Monte Carlo (MCMC) could be used to optimize the GMM. However, both EM and Bayesian MCMC are assumed that the number of clusters is fixed. Therefore, to select the optimum number of clusters, we have to use certain cluster selection criteria. This process makes the segmentation quite complicated and is not automatic. This study tries to employ the GMM using Reversible Jump Markov Chain Monte Carlo Algorithm (GMM-RJMCMC) to segment the MRI-based brain tumor and compare it with the GMM-MCMC. The use of RJMCMC is expected to accelerate the calculation process, which can provide the number of optimum clusters automatically; moreover, the MRI image segmentation could become more adaptive. The result shows that from the Correct Classification Ratio (CCR), the GMM-RJMCMC could provide an equal segmentation results compared to the GMM-MCMC, however, GMM-RJMCMC has the advantage, that is faster in executing the algorithm, this makes GMM-RJMCMC more efficient in finding the optimum number of clusters.