Reviewerly/exHarmony huggingface dataset:
Adaptive Symmetrical Virtual Keyboard Based on EOG Signal

Electrooculography (EOG) signal is a bio-signal that could play important roles in building communication between human and machine. This bio-signal is extracted from gaze motions by attaching electrodes to the skin around the eye. This paper presented the design and development of gaze-motion controlled adaptive virtual keyboard. The keyboard has the ability to arrange character layout based on specified frequency of a character. The designed virtual keyboard has 45 keys that comprising 26 letters, 10 numerals, 4 symbols, a space, a delete, an enter, a return and a central point key. The keyboard layout has six zones. Characters in the same zone have equal distance to the central point. The distance is the least gazing that possible to move from the central point to the character. The most frequently used character will shift to the shortest distance position. Seven paragraphs were typed using this virtual keyboard. The keywords of each paragraph are `bukittinggi', `zimbawe', `andalas', `sukarno', `randang', `kapal' and `dokter'. The performance of the adaptive virtual keyboard was evaluated using these seven keywords. Results shows that the number of gaze motion required to type the keyword after the re-arrangement of layout decreased about 27%. This adaptive ability can reduce the number of gazing to prevent eye fatigue.
Graph-based Feature Selection Filter Utilizing Maximal Cliques

Huge amounts of data are collected every millisecond all around the world. This ranges from images and videos to an increasing amount of sensor data. Thus, it gets difficult for humans to decide on the most important features anymore. But reducing the feature vector is an important and necessary task to achieve higher precision in classification tasks. Detecting anomalies and classifying data points is crucial for a variety of objectives in many domains. Therefore, this work focuses on feature selection for binary decision problems (e.g. anomaly detection, binary classification). We propose a novel graph-based feature selection filter, which takes into account both the importance and correlation of features at the same time. The graph-based feature selection filter recommends a subset by applying a rating function onto the maximal cliques of the graph. The evaluation is based on a comparison of the accuracy of multiple machine learning algorithms and datasets between different baseline feature selection approaches and the proposed approach. Results show that the proposed approach delivers the highest accuracy in about 69% of the cases compared to existing approaches, while reducing the number of features.
Mixed convolutional recurrent neural networks

In this work, we design a neural network named MCRNN for classifying signals with slight distinction. This model combines advantages of both Convolutional Neural Network and Recurrent Neural Network, allowing the network to distinguish between long signals with long time-frequency maps.
Anti-forensics of a NAD-JPEG Detection Scheme Using Estimation of DC Coefficients

Bianchi et al. proposed a method to detect the non-aligned double JPEG (NAD-JPEG) compression using the presence of distortions in the Integer Periodicity Map (IPM) of DC coefficients of any JPEG image. However, we found that the IPM can be easily altered without affecting the visual quality of an image. In this paper, we propose a new anti-forensics scheme that alters the IPM to deceive the Bianchi et al. scheme. In our proposed method, a statistical model of the DC coefficients from singly compressed JPEG image is used to generate an estimated image which is free from quantization artifacts that are present in the IPM. The estimated image is subjected to NAD-JPEG compression. It was found that the DC values of NAD-JPEG image are no longer be the multiples of the corresponding primary quantization step size $$q_1$$. As a result, the DCT coefficients do not cluster around the lattice related to the $$q_1$$ and the IPM of the double JPEG compressed image seems to be the IPM of a singly compressed JPEG image. Experimental results show the effectiveness of the proposed anti-forensics scheme as the accuracy of the said forensics method get reduced to less than $$50\%$$ in case of anti-forensically modified images.
Acquiring Ontology Axioms through Mappings to Data Sources

Although current languages used in ontology-based data access (OBDA) systems allow for mapping source data to instances of concepts and relations in the ontology, several application domains need more flexible tools for inferring knowledge from data, which are able to dynamically acquire axioms about new concepts and relations directly from the data. In this paper we introduce the notion of mapping-based knowledge base (MKB) to formalize the situation where both the extensional and the intensional level of the ontology are determined by suitable mappings to a set of data sources. This allows for making the intensional level of the ontology as dynamic as the extensional level traditionally is. To do so, we resort to the meta-modeling capabilities of higher-order description logics, in particular the description logic Hi ( DL-Lite R ) , which allows seeing concepts and relations as individuals, and vice versa. The challenge in this setting is to design efficient algorithms for answering queries posed to MKBs. Besides the definition of MKBs, our main contribution is to prove that answering instance queries posed to MKBs expressed in Hi ( DL-Lite R ) can be done efficiently.
Real-Time Motion Detection for Android Smartphones

Real-time motion detection can be used to monitor a wide range of physical activities, such as those typically leveraged by exergames, but the literature present few algorithms. Motivated by this demand, we aimed at evaluating and proposing motion detection algorithms in Android smartphones in order to comply with three requirements: accuracy, precision, and recall above 95%. We evaluated the existing algorithms, Google and Step Detector. The results of this evaluation showed the Google algorithm as the most promising, but it did not meet all the requirements. Therefore, a new algorithm, based on it, was proposed and evaluated. Thus, this paper introduces the Castor algorithm and a tool for analyzing motion detection algorithms. In our evaluation, Castor was able to overcome Google in most scenarios and could adapt to lower-speed movements, when Google's algorithm could not. As contributions, this paper presents Castor as an algorithm to detect movements and also a tool which can be extended for implementing and evaluating new algorithms.
How You See Me: Understanding Convolutional Neural Networks

Convolutional Neural networks(CNN) are one of the most powerful tools in the present era of science. There has been a lot of research done to improve their performance and robustness while their internal working was left unexplored to much extent. They are often defined as black boxes that can map non-linear data effectively. This paper answers the question, “How does a CNN look at an image?”. Visual results are also provided to strongly support the proposed method. The proposed algorithm exploits the basic math behind CNN to backtrack the important pixels. This is a generic approach which can be applied to any architecture of a neural network. This doesn't require any additional training or architectural changes. In literature, few attempts have been made to explain how learning happens in CNN internally, by exploiting the convolution filter maps. This is a simple algorithm as it does not involve any cost functions, filter exploitation, gradient calculations or probability scores. Further, we demonstrate that the proposed scheme can be used in some important computer vision tasks such as object detection, salient region proposal, etc.
OPEN TEAM TOURNAMENT OF YAROSLAVL REGION ON PROGRAMMING AMONG STUDENTS

The article describes Yaroslavl programming tournament, its specifics, experience, variants of tasks of intramural and extramural rounds, preparation and realization of the tournament of programs that implement the strategies of some games.
Blockchain-Based Threat Registry Platform

This document presents a reference architecture for a Blockchain-based Threat Registry platform, named BBTR, to share information about treats among different actors. The design of the BBTR guarantees integrity and availability of the data stored in it and is also compatible with privacy requirements, allowing different actors to participate as users of this shared BaaS (Blockchain as a Service). The paper also shows how this approach can be combined with AI techniques to extract valuable information from the threats directly from the Blockchain, empowering the final solution with a decision-making engine. It also includes its validation in a use case in the Health care domain.
Data Smoothing for Software Effort Estimation

The goal of this paper is to improve the estimation performance of software development effort by mitigating the problem caused by outliers in a historical software project data set, which is used to construct an effort estimation model. To date, outlier removal methods have been proposed to solve this problem; however, they are not always effective because removing outliers reduces the number of data points (= software projects in our case) in a data set, and a model built from a small data set often suffers from lack of generality. In such a case, estimation performance can become even worse. In this paper we propose a method called data smoothing to mitigate the problem of outliers without reducing the number of data points. We consider that data points are outliers if they do not meet the assumption of Analogy-Based Estimation (ABE) such that “projects with similar features require similar development efforts.” The proposed method changes the effort values (person-months or person-hours) in a data set so as to satisfy this assumption; and by this way, all outliers become non-outliers without decreasing the data points. As a result of experimental evaluation using 8 software development data sets, we found that the proposed data smoothing showed the same or higher effort estimation accuracy than the non-smoothing case, while conventional outlier removal method showed worse accuracy in some data set.
Predicting Eurovision Song Contest Results by Interpreting the Tweets of Eurovision Fans

The Eurovision Song Contest is Europe's most well-known international competition for musical performance and songwriting. The winner of the contest is determined by a voting system that takes into account the opinions of viewers across the continent and around the world. This paper attempts to identify a link between the overall sentiment of Twitter users toward the performances and the eventual winner selected by Eurovision voters. More than 900,000 English and Spanish-language tweets that were posted during the 2019 Eurovision Song Contest were collected for this study. The tweets were analyzed to identify the sentiment and target performance for each. The results of this analysis were used to determine a predicted rank for each performance, resulting in a Spearman ρ correlation coefficient of 0.559 and a Kendall τ correlation coefficient of 0.403.
The Use of Direction Matrix to Determine Autonomous Quad-Copter Drone Path when Facing Obstacle

This paper presents a method to conduct path planning for Multi-Rotor Autonomous Aerial Vehicle such as quad-copter drone (Drone) in an urban environment. The priority of the path plan will be the safety of the drone from collisions with obstacles and efficiency of the path. In our proposed method, initially, the drone only knows its position and its target position. The agent studies the environment obstacles towards its target while conducting a movement. To perform the drone obstacle avoidance movement, we introduce the concept of 3 by 3 Direction Matrix usage to improve the A* algorithm. The experimental environment in this study will be dynamic, where various obstacles exist when flying at a relatively low altitude. The method is tested on a 3Dimensional environment, where the Drone could also face various dynamic obstacles. In addition, the target, which is the end destination in this experiment, is also dynamic. The target is able to move and the Drone must be able to plan a path and reach the target object. The experiment result shows that the proposed method is effective to be used as drone path planning in a dynamic environment.
Computer forensic analysis protocols review focused on digital evidence recovery in hard disks devices

Abstract Fourth Industrial Revolution includes cybersecurity as a key technology, given the increase in the number of devices connected to the Internet, the volume of relevant information available and the incidence of cybercrime on them. Among the lines of study of cybersecurity appears the computer forensic analysis, which seeks to reveal and analyze information in systems based on an analysis of digital evidence. This process involves a series of phases that ensure the integrity of the sources of information from beginning to end. Occupational studies in the area show a deficit of qualified and qualified personnel to cover the demand of processes related to this topic. In order to close this gap, the National Learning Service at Services and Business Management Center implements the Digital Forensic Laboratory. In this paper, workflows of computer forensic analysis procedures described in scientific documents are analyzed and a proposal of a protocol applicable to the laboratory is presented, which allows to rescue digital evidences of hard disks. A six-step procedure is proposed, ranging from the diagnosis of the case, to the presentation of results, through the collection of evidence, the copies of files, the analysis of data and the extraction of information.
Application of augmented reality technology in biological learning

Abstract The writing of this paper aims to introduce Augmented Reality technology to the world of education in schools in Indonesia precisely in Biology learning. Previously there have been several studies discussing the application of Augmented Reality technology to the world of learning but the object of research that was studied was too broad, while the research that the author did was more focused on learning biology. The research method used in this paper is a descriptive research method by giving questions in the form of questionnaires to 50 respondents. Augment Reality technology in its application to Biology learning is expected to contribute innovation and add variations in existing learning media before, from the results of research conducted, it was found that Augmented Reality technology received many positive responses in its application to the world of education. Augmented Reality can be made using some software that is used such as 3D Blender, Unity and other supporting software. With the application of Augmented Reality technology in the world of education is expected to be able to add learning methods to be more interactive and interesting.
ANALISIS PREDIKSI DEBIT SUNGAI AMPRONG DENGAN MODEL ARIMA (AUTOREGRESSIVE INTEGRATED MOVING AVERAGE) SEBAGAI DASAR PENYUSUNAN POLA TATA TANAM

An accurate determination of water availability in the 10-day period of the Amprong River has an important role in the planting system to support the agricultural production process in DI. Kedungkandang, because if the availability of water is not precisely determined, there will be an error in regulating irrigation water and its use is not as expected. To overcome these problems, an analysis system is needed that is able to make predictions well. One of the time series models is the ARIMA (Autoregressive Intregated Moving Average) model. The model was built by 9 period discharge data, namely 2008/2009 until 2016/2017, to predict the discharge of period 2017/2018. Of the ten tentative models obtained, there are only five models that are worth using. The best model is the ARIMA model (2,0,1) (1,2,1) 36 with the value of MSE = 22,90; KR = 6.00; MSD = 8.05; MAD = 2.04; MAPE = 18.53 and MPE = -8.98. In second crop season the crop intensity of paddy increased from 55.79% to 64.50%, and the production of GBK increased by 13.50%. While the third crop season paddy crop intensity increased from 37.22% to 49.99%, and GBK production increased by 25.54%.
Multi-view Image Generation by Cycle CVAE-GAN Networks

In this paper, we address the problem of multi-view image generation from a single view. To this end, we investigate Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN) as effective solutions to this problem. Inspired by CycleGAN playing an important role in image-to-image translation applications, we utilize the idea of cycle consistency to generate images in multi-view. With VAE and GAN as the basic components, we propose Cycle Conditional-VAE-GAN (Cycle CVAE-GAN) to tackle the problem within four steps. First, the source image with target-view condition and the target image are both mapped to the shared latent variable space by two encoders. Second, we sample a variable from the shared variable space as the input of a designed decoder for the low-resolution target image generation. Third, we repeat the previous two steps. It is worth mentioning that the inputs of those two encoders are the generated low-resolution target image with source-view condition and the source image. Then the reconstructed source image can contribute to the cycle consistency loss. Finally, a GAN framework with a dual-input U-Net generator and a patch discriminator are proposed to generate high-resolution and realistic target images. Experiments on the Multi-View Clothing (MVC) dataset demonstrate that the proposed method achieves better results than the state-of-the-art models.
Drug Users Prediction Using Backpropagation EducationalMethod

Abstract The Artificial Neural Network in this study has been used to predict drug users by using the backpropagation method with the Matlab 7.7 software program. The variables consist of the number of drug users, the type of drug used and the employment status of drug users. The data input from 2010 to 2012 while the target data is 2013 data consequenlty if input data and target data are processed on artificial neural networks will produce output that can predicts 2014 drug users. Predictions actually consider the value which has not been seen in the future based on past data. Prediction is the process of predicting something by correcting the previous action. To predict a condition, it requires proper calculation to answer the problem. The calculation can use artificial neural networks with the backpropagation method.
Detailed Quantum Cryptographic Service and Data Security in Cloud Computing

This paper presents a holistic security solution for cloud computing environment by proposed a new secured cloud framework, which known as cryptographic cloud computing environment (CCCE) [14, 15]. CCCE entails both a trusted cloud-user and an innovative quantum cryptographic service (QCaaS). QCaaS is a new security service that responsible to solve the key generation, key distribution and key management problems, which emerged through the negotiation between the communications parties (cloud-user and cloud provider). Such service includes a proposed symmetric quantum encryption algorithm that is explained in previous work, usually called quantum advanced encryption standard (QAES). QAES build by the integration between a quantum key distribution (QKD) and an enhanced version of AES using two different modes, On-line and Off-line mode. Additionally, CCCE implemented using the system center manager 2012-SPI, which in turn, installed and configured based bare-metal Hyper-V, it poses more secured data transmission channels by provisioning secret key among cloud's instances. Finally, CCCE reveals a higher secure data transmission comparing to other cloud security systems especially in key generation, key management, defeating almost type of attacks, and the impact rate of the proposed solution on the performance of cloud resources.
Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique

Imbalanced datasets typically occur in many real applications. Resampling is one of the effective solutions due to producing a balanced class distribution. Synthetic Minority Over-sampling technique (SMOTE), an over-sampling technique is used in this study for dealing the imbalanced dataset by add the number of instances of a minority class. This technique is used to decrease the imbalance percentage of the dataset by generating new synthetic samples. Thus, a balanced training dataset is produced to replace the class imbalanced. The balanced datasets were obtained and trained with machine learning algorithms to diagnose the disease’s class. Through the experiment findings on the real-world datasets, oral cancer dataset and erythemato-squamous diseases dataset from the UCI machine learning datasets, an over-sampling method showed better results in clinical disease classification.
Technology Framework of the Intelligent Command and Control System

Abstract With the rapid application and development of artificial intelligence technology in the civilian field, the research on intelligent combat and command systems in the military field is also intensifying. In order to improve the cognition and decision-making ability of international command and control system, this paper elaborates the overall technical framework of the system, and brings forth intelligent solutions and development suggestions in situation cognition, planning, decision making, and action control.
A Novel Conditional Random Fields Aided Fuzzy Matching in Vietnamese Address Standardization

Address standardization is the process of recognizing and normalizing free-form addresses into a common standard format. In today's digital economy, this process is increasingly challenging such as in ecommerce fulfillment, logistic planning, geographical data analysis, real-estate, and social network mining, etc. Traditional approaches mostly follow two directions: Named Entity Recognition (NER) and fuzzy matching. Particularly, for Vietnamese address, neither these two approaches are efficient due to sparse and erroneous data. In this paper, we propose a novel approach that leverages NER model as a suggestion to re-rank potential address candidates obtained by the fuzzy matching stage. We develop a log-linear model for this re-ranking purpose. Our experiments showed that it outperforms both NER and fuzzy matching approaches with an accuracy of 88%, and suggested further applications on different language data.
A network covert timing channel detection method based on threshold secret sharing

Abstract Network covert timing channel (NCTC) is a kind of covert channel that acquires strong concealment by modifying the interpacket delays of legitimate network traffic and can evade detection by conventional network security mechanisms such as firewalls. Existing detection schemes are not able to detect multiple types of covert channels. Moreover, the robustness of the detection method is low when the network environment changes. Therefore, detecting NCTC is a challenging task. In this paper, an NCTC detection method based on threshold secret sharing is proposed. The new approach utilizes the principle of threshold secret sharing to tolerate the loss or the destruction of partial subsecrets, improves the robustness of the detection method, and solves the problem that the current detection method cannot resist environment changes. Experimental results show that the proposed scheme in this paper has strong robustness to a changing network environment such as when network jitter, packet loss, and packet injection occur in the network transmission process. The approach can detect varieties of NCTCs with a guaranteed true positive rate and greatly improve the versatility and robustness.