Reviewerly/exHarmony huggingface dataset:
Iris images encryption based on QR code and chaotic map

In this paper an Iris image is encrypted based on QR (quick response) code and chaotic map. The main idea of the proposed system is generating a QR code depending on the input text and then extract the features from QR code by using convolution, these features are used for key generation. After that the permuted iris image is encrypted by using generated key, after that the resulting image will be encrypts using 2D logistic map. The randomness of generated key is tested using the measures of NIST, and quality of images that encrypted in this method are tested by using security analysis tests such as PSNR, UACI, NPCR, histogram, correlation and entropy. The security analysis shows that the proposed system is secure for iris image encryption.
Research on Extraction of Simple Modifier-Head Chunks Based on Corpus

The purpose of this study is to automatically extract a set of simple modifier-head chunks from a large-scale corpus. By analyzing the distribution of simple modifier-head chunks in usage, a set of formal rules of chunks extraction are formulated and a rule-based automatic extraction algorithm is designed. In the experiment of random sampling, the precision of extraction result with this method reaches 82.63%, which casts light on knowledge extraction based on large-scale corpus.
Improved Particle Swarm Optimization of Three-Dimensional Path Planning for Fixed Wing Unmanned Aerial Vehicle

Path planning for Unmanned Aerial Vehicle (UAV) targets at generating an optimal global path to the target, avoiding collisions and optimizing the given cost function under constraints. In this paper, the path planning problem for UAV in pre-known 3D environment is presented. Particle Swarm Optimization (PSO) was proved the efficiency for various problems. PSO has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. In this paper, the improved PSO with adaptive mutation to overcome its drawback in order to applied PSO the UAV path planning in real 3D environment which composed of mountains and constraints. The effectiveness of the proposed PSO algorithm is compared to Genetic Algorithm, standard PSO and other improved PSO using 3D map of Daklak, Dakrong and Langco Beach. The results have shown the potential for applying proposed algorithm in optimizing the 3D UAV path planning.&#x0D; Keywords: UAV, Path planning, PSO, Optimization.
Evaluation of ISODATA Clustering Algorithm for Surface Gold Mining Using Satellite Data

The regular mapping and monitoring of surface mining areas are highly important due to their potential negative impacts on the surrounding environment. In this research, the sentinel remote sensing multispectral data with Blue, Green, Red, and Near-Infrared was acquired, preprocessed and enhanced using Histogram Equalization Techniques. Sixteen different ISODATA Clustering Algorithm scenarios were designed with variations in Maximum Iterations and Convergence Threshold parameter values, in order to correctly map the surface gold mining areas. The results showed that the lower values of Maximum Iterations and higher values of Convergence Threshold yielded the most correctly classified surface gold mining map. The methodology developed in this research is not only robust but also cost and time effectively by using satellite remote sensing images in order to monitor and map the surface gold mining activities in the regions particularly in the dense forest covered areas.
Comparison between Kalman Filter and Interacting Multiple Model using 2-D trajectories

Track estimation or track smoothing is performed to minimize the errors in a tracking scenario. These errors are referred as noise in the field of tracking. Kalman filter and Interacting multiple model (IMM) filter is widely used in tracking problems since 1959. The importance and equations of Kalman Filtering (KF) are not discussed with IMM in literature although understanding KF is very important before working on IMM. In this paper the working of Kalman and its implementation in IMM is discussed along with equations in terms of time intervals t, t-1, t+1. Simulation tests were carried out and performance of IMM and KF is compared on the basis of estimation error plots. Tests are carried out on two different trajectories. KF is applied with Constant acceleration and constant velocity separately. Results indicate that KF models are not good in tracking and gives larger error rate. KF is cost effective when it comes to computations but when it comes to sensitivity of tracking, IMM is preferred to avoid error and incorrect estimates.
A New Approach of Data Pre-processing for Data Compression in Smart Grids: Invited Paper

The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Loeve, or wavelet transforms. One drawback from adopting such an approach is that it is independent of the use of the compressed data, which may induce significant optimality losses when measured in terms of final utility (instead of being measured in terms of distortion). We therefore revisit this paradigm by tayloring the data pre-processing operation to the utility function of the decision-making entity using the compressed (and therefore noisy) data. More specifically, the utility function consists of an Lp-norm, which is very relevant in the area of smart grids. Both a linear and a non-linear use-oriented transforms are designed and compared with conventional data pre-processing techniques, showing that the impact of compression noise can be significantlv reduced.
Secure i-Voting Scheme with Blockchain Technology and Blind Signature

In the last four years, blockchain technology affected largely all aspects of our lives. Blockchain started to launch a new technological revolution of storing digital transactions over the Internet, verifying the authenticity, licensing and providing the highest degree of security and encryption. Blockchain usage started with digital currency then its implementation extended to many industries such as voting, health records, copywriters, real estates and so on. However, it is time to upgrade the election scenario from practicing paper-based elections to use modern technologies in order to facilitate our lives. The fact that the blockchain technology has demonstrated almost infinite immutability and high resistance against hacking, lends credit to employ it in securing election data from fraud by saving every single piece of data, record or transaction with unchangeable history. In this paper, we propose and test implement a robust online voting system based on blockchain in order to prevent election forgery and ease the voting process for citizens. The essence of our research lies in abandoning alterable traditional databases and replacing them with two private blockchains that use the peer-to-peer network. Along with the blockchains, we utilized blind signature to maintain vote/voter privacy in order to safeguard voter eligibility validation against manipulation and forgery. Lastly, we discuss a threat model, and suggest solutions overcome it; we also suggest a solution to identity impersonation and vote-selling problems.
Writing Scientific Manuscripts

One of the key pieces of academic currency is a manuscript. It is how academic surgeons communicate their craft to others in the field. There are numerous forms of manuscripts including systematic reviews, meta-analyses, commentaries, editorials, and primary research papers. Writing not only permits you to disseminate your findings through peer-reviewed journals, but it also provides you the opportunity to critically review the literature, evaluate your results, and place your findings into the greater context of your scientific area. While oral presentations are no doubt important, the written word remains a document that can be referenced and reviewed for many years to come. This chapter will address how to approach scientific writing through a systematic and strategic approach to deliver a clear and concise product.
TEACHERS’ TRAINING SYSTEM OF NATURAL AND MATHEMATICAL DISCIPLINES OF UKRAINE IN STEM-EDUCATION

The analysis of scientific works and directions of researches of scientists in the context of STEM education are defined. Changes, taking place in future teachers’ curricula and activities that allow teachers to improve their skills and gain knowledge in innovative technologies are analyzed.
Nash Equilibrium Strategy of Port Construction Considering Seawater Erosion

Lu, Q.; Wang, Z., and Wang, R., 2019. Nash equilibrium strategy of port construction considering seawater erosion. In: Li, L.; Wan, X.; and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research Journal of Coastal Research, Special Issue No. 98, pp. 330–334. Coconut Creek (Florida), ISSN 0749-0208.The main construction approaches to ports are based on government investment and contractor construction. This method faces a particular drawback for the implementation of construction: the contractor fails to carry out the work in strict accordance with the plan. By considering the problems of government supervision and contractors' strict adherence to construction plans, as well as to seawater erosion after port construction, this paper uses Nash equilibrium to solve the optimal mixed strategy for both parties. The main influencing factors of the strategy are analyzed. Then this paper finds that the high probability of erosion can stimulate the government and contractor to do their best, and the sum of gains and losses also influence the best strategy. Based on analysis results, the corresponding suggestions are put forward.
Defining the limits of application and the values of integration variables for the equations of train movement

B. Bodnar: ORCID 0000-0002-3591-4772; M. Kapitsa: ORCID 0000-0002-3800-2920; D. Bobyr: ORCID 0000-0003-1441-3861; D. Kyslyi: ORCID 0000-0002-4427-894X
Adaptive Measurement Noise Covariance Matrix R for JPDAF based Multitarget Tracking

In multitarget tracking, several targets of interest are being tracked simultaneously with the help of any optimal estimator. Kalman Filter (KF) and Extended Kalman Filter (EKF) have proved to be very good estimators. Multitarget tracking finds its applications in diverse fields like pattern recognition, computer vision, radar tracking, robotics, etc. Several algorithms have been implemented for multitarget tracking including Probabilistic Data Association Filter (PDAF), Joint Probabilistic Data Association Filter (JPDAF), Nearest Neighbor Standard Filter (NNSF), Global Nearest Neighbor (GNN), Neural Networks (NNs), etc. Joint Probabilistic Data Association Filter (JPDAF) is the multitarget version of Probabilistic Data Association Filter (PDAF), in which joint association probabilities are computed and tracks are then updated based upon theses probabilities. Measurement noise covariance matrix R in Kalman filter needs to be transformed from polar to cartesian coordinate system. The optimal value of R should be calculated for the good performance of filter. In this paper, measurement noise covariance matrix R has been computed using transformation and more than 80% of the desired results have been achieved by performing tracking using JPDAF algorithm.
A Study on the Location of Bus &amp; Truck Automobile Maintenance Company

Automobiles are largely divided into passenger cars with less than five passengers and commercial vehicles such as construction machines, trucks, mixers, special lorries, and large buses. Automobile maintenance companies are also divided into passenger cars and commercial vehicles. Because commercial vehicles are the livelihood vehicles of individual carriers engaged in cargo transportation, passenger transportation, and construction, fast and accurate maintenance is important. Access to the expressway is also important because the car is large and uses highways. In addition, the time required for troubleshooting is long, so sufficient space must be secured for parking the vehicle, and ease of entry and exit of the vehicle in the maintenance shop should be fully considered. For this reason, commercial vehicle maintenance companies have higher initial investment costs than car maintenance companies, and it is difficult to supply and maintain maintenance personnel. Therefore, it is necessary to carefully examine and analyze various related factors for successful commercial vehicle maintenance company selection. However, most existing commercial vehicle maintenance companies often choose their location based on the empirical judgment of the founder, the customs and the case, without a clear analysis. In this study, we show how to derive the location selection factors to be considered when establishing professional maintenance company for commercial vehicles and to select the optimal location by using AHP (Analytic Hierarchy Process) method.
Design and Implementation of a Secure Mobile Banking System Based on Elliptic Curve Integrated Encryption Schema

Mobile banking relies on the Internet for transfer data between the customer’s device and bank, thereby making financial data vulnerable to theft unauthorized people. As such, a secure system can be built to provides confidentiality, integrity and authentication. Also, most of the approved banking systems use security protocols to maintain the confidentiality of secret data, but most of these protocols suffer from problems at the handshake level (secret key exchange), thereby leading to the exposure of secret keys. In this study, a secure mobile banking system was designed. The proposed system was divided into three sections, namely, the sides in relation to the bank, the server, and the application, which was run on a mobile device. The model used the proposed Elliptic Curve Integrated Encryption Schema (ECIES) for the exchange of secret data between the client (the application on a smartphone) and the server. The ECIES that was offered used the Elliptic-curve Diffie–Hellman (ECDH) as the key agreement algorithm, the Advanced Encryption Standard (AES) in Galois/Counter Mode (GCM), i.e. the AES-GCM, as the encryption algorithm, and the MAC-384 as the Message Authentication Code (MAC) function, while the hash functions (SHA512 and SHA384) were utilized for the keys derivation process. Furthermore, the suggested system authenticated the user through a second password when a transaction was performed to send money.
Semantic Enrichment of Linked Archival Materials

By using the metadata for the fonds of “Chen Cheng-po’s Paintings and Documents” (CCP) in the database of the Archives of the Institute of Taiwan History (IHT, Academia Sinica, Taiwan), we develop and enhance a semantic data model for converting the data into a linked data project, focusing on data modeling, data reconciliation, and data enrichment. The research questions are: 1) How can we keep the original rich and contextual information of the archival materials during a LOD task?; 2) How can we integrate heterogeneous datasets about the same real-world resources from libraries, archives, and museums, while keeping the different views distinct?; and, (3) How can we provide added value for semantic metadata of archives in terms of instance-based and schema-based types of enrichment? The project adopts the Europeana Data Model (EDM) as the main model and extends the properties to fit the contextual characteristics of archival materials. Various methods are explored to preserve the hierarchical structure and context of the archival materials, to enrich semantic data, and to connect data from different sources and institutions. We propose four approaches to enriching data semantics by: 1) directly using external vocabularies; 2) reconciling local links to other linked data sources; 3) introducing contextual classes for the appropriate contextual entities; and, 4) utilizing named entity extraction. The results can contribute to the best practice for developing linked data for art-related archival materials.
Latent Profile Analysis According to the Intelligence-Creativity Mindsets of University Students: Difference in Learning Flow by Laten Group

본 연구는 대학생을 대상으로 지능 마인드셋과 창의성 마인드셋 척도를 이용하여 유사한 응답을 가진 집단을 잠재프로파일 분석을 통하여 밝히고, 각 집단 별 학습몰입의 차이를 살펴보고자 하였다. 본 연구는 K광역시에 거주하는 2개 대학교 학생 289명을 대상으로 실시하였으며, 그 결과는 다음과 같다. 첫째, 대학생의 지능-창의성 마인드셋 유형에 따른 잠재프로 파일 분석 결과 세 집단이 도출되었으며, 1집단은 지능 성장, 창의성 성장 마인드셋이 가장 높은 ‘고-성장 마인드셋’집단이라고 명하였으며, 2집단은 지능 성장, 창의성 성장 마인드셋이 중간 정도 높은 수준이라 ‘중-성장 마인드셋’집단이라고 하였다. 마지막으로 네 점수가 모두 유사하게 나타나 ‘균형적’집단이라고 하였다. 두 번째 연구결과를 살펴보면 세 집단에서 학습몰입의 총점을 유의미한 차이를 보였으며, ‘고-성장 마인드셋’집단이 다른 두 집단에 비해 유의미하게 높은 것으로 나타났다. 학습몰입의 하위요인으로는 ‘정서적 요인’과 ‘통제감 및 행동적요인’에서 유의미하게 ‘고-성장 마인드셋’집단이 높은 것으로 나타났다.The purpose of this study was to identify the groups with similar responses using the intelligence mindset and creativity mindset scales of college students, and to examine the differences in learning flow by each group. This study was conducted with 289 students. The results are as follows. First, as a result of analyzing latent profile according to the intelligence-creativity mindsets scores of college students, three groups were derived. The first group called the high-growth mindset group with the highest intelligence growth and creativity growth mindset, and the second group called the medium-growth mindset group because the intelligence growth and creativity growth mindsets was moderately high. Finally, all four scores are similar, which is called the ‘average’ group. The results of the second study showed that the total scores of learning flow were significantly different among the three groups, and that the high-growth mindset group was significantly higher than the other two groups. The sub-factors of learning commitment were significantly higher in the high-growth mindset group in emotional factors and control and behavioral factors.
Spatial Correlation Based Outlier Detection in Clustered Wireless Sensor Network

Over the last few years, wireless sensor networks (WSNs) have attracted tangible involvement particularly in the field of surveillance and monitoring works. Usually the data measured by the sensor nodes is contaminated with noise and this data which is deviated very much from its normal behavior is called as outlier or abnormal data. This paper puts forward outlier detection by cluster head based on spatial correlation approach. The performance of the proposed algorithm is evaluated using simulation in terms of detection accuracy (DA) and false alarm rate (FAR). It is also compared with existing DODCF algorithm. Improvements have been observed in the proposed algorithm in terms of DA, FAR, and message complexity.
Research on Security Risk Assessment Based on the Improved FAHP

Abstract In order to reduce the subjectivity of information security risk assessment process and improve assessment efficiency, we propose a new method of information security risk assessment based on improved FAHP (Fuzzy Analytic Hierarchy Process) to analyse the information security-related standards for domestic and international risk assessment. We establish a Hierarchical Security Assessment Model and introduce refinement indicators and Intuitionistic Fuzzy Sets to reduce subjective judgment factors in the assessment of traditional risk. We then applied an e-commerce company in case analyse the security risk and the results are satisfactory and in line with the actual situation of the company. The indicator system of this method is more objective and comprehensive and the evaluation process is more efficient, which provide new ideas for risk assessment of existing information security companies.
Stepwise Noise Elimination for Better Motivational and Advisory Texts Classification

There is little research into designing artificial motivational agents. The end-goal of our studies is therefore to create a dialogue system that would motivate users to do their everyday tasks using natural language. In this paper, we present a method of distinguishing texts containing motivational advice from regular texts to sort out noise in training data for our dialogue system. We implemented a novel method of chaining two shallow networks together by utilizing the output results of the first network to determine the input for the second one. We achieved F-score of 0.94 and 0.97 with our proposed method. The contributions of this paper are threefold: first, we successfully identified 14 hand-crafted features that make a text motivational/advisory. Secondly, we were able to create a classifying algorithm that distinguishes motivational/advisory texts from regular ones. Finally, our proposed method can be applied to other text classification tasks.
Auditability: An Approach to Ease Debugging of Reliable Distributed Systems

There are various means of providing dependability, one of which is fault removal and debugging is one popular example of a fault removal technique. During debugging, the system is executed, and execution data is collected, to be analysed later, to determine if the execution has satisfied the system specification. In a distributed system, the data collection is done centrally via a monitor, and the assumption typically is that all the nodes are correct. However, in open distributed systems such as the Internet of Things (IoT), there is no central authority to enforce this assumption and nodes may behave arbitrarily by violating protocol steps, making processes such as debugging very challenging. We call the data collection process for such processes auditing and a program that reliably records such data as being auditable. In this context, we make the following novel contributions towards auditability enforcement: (i) we define the auditability problem and (ii) identify a necessary condition for a program to be auditable. We then provide examples of auditable programs. Subsequently, (iii) we show an impossibility result for strong auditability. To circumvent this impossibility, we study a weaker problem and discuss the ramifications of certain implementations. Finally, we show that auditability is at least as difficult as the problem of fair exchange. This is the first formal work towards the design of reliable systems through audibility.
Sepsis Detection in Sparse Clinical Data Using Long Short-Term Memory Network with Dice Loss

This paper aims to present a methodology for sepsis prediction from clinical time-series data. Sepsis is one of the most threatening states which could occur while treating a patient at the intensive care unit. Therefore its prediction could significantly improve the quality of the patient treatment.In this work, we address the problem ofsepsis prediction with Long Short-Term Memory (LSTM) network with specialized deep architecture with residual connections. The output of the network is sepsis prediction score at each point in time.Feature normalization into the fixed range of values is applied including replacing missing values with numerical representation from outside the normalized range. Therefore, the LSTM network is able to include missing values in the learning process. Also, the rarity of sepsis occurrence in the provided dataset is a challenging problem. This problem is addressed by the application of dice loss providing automatically weighted classes by the occurrence of the feature.The proposed method leads to 0.281 normalized utility score on the full test set as the best official Phys- ioNet/Computing in Cardiology (CinC) Challenge 2019 entry ofECGuru10 team.