Reviewerly/exHarmony huggingface dataset:
Empirical Analysis of Island Model on Large Scale Global Optimization

Evolutionary algorithms (EAs) have shown their great capability of handling optimization problems. In the domain of large scale global optimization, many distributed EAs (dEAs) have been proposed for maintaining population diversity so as to enhance their efficacy. One well-known variant of dEA is the island model EA, in which several populations form islands communicating through migration. There are several key factors that affect the performance and search behavior of island model EA, such as population size of each island and migration topology. While most studies of dEA focus on low or medium dimensional problems, an investigation into the effects of these factors on high dimensional problems is greatly needed. This study presents an empirical analysis of island model EA on large scale global optimization problems. The analysis examines the solution quality, convergence speed, and population diversity of island model EA with different migration topologies, population sizes, migration rates, and migration frequencies on four benchmark function of 1,000 dimensions. The results render guidelines for using island model EA to solve large scale global optimization problems.
Cryptographic based Message Transfer using Block chain Technology

Black chain is a digitized, decentralized, public ledger for constantly on crypto currency transactions.Continually developing 'completed' blocks (the majority later transactions) would recorded and included to it over ordered request it permits advertise members will keep track about advanced cash transactions without focal record keeping.Each hub (a PC associated with the network) gets a duplicate of the piece chain, which will be downloaded naturally.Initially created similarly as those bookkeeping technique to those virtual money spot bit coin, black chains -which utilize what's known as dispersed record innovation organization (DLT) -need aid showing up clinched alongside an assortment for business provisions today.Currently, those innovation is principally utilized with check transactions, inside advanced currencies however it may be could reasonably be expected to digitize code Furthermore embed practically whatever record under the black chain.Completing along these lines makes a permanent record that can't make changed; furthermore, that record's genuineness could make checked by the whole group utilizing those piece chain As opposed to a single unified power.Black chain will be a tamper-evident, imparted advanced record that records transactions in a open alternately private peer-to-peer system.Dispersed will know part hubs in the network, the record lasting press fabric records, for a consecutive chain of cryptographic hash-linked blocks, those history about benefit exchanges that occur between those associates in the system.Every last one of affirmed and approved transaction obstructs are joined and anchored starting with the starting of the chain of the vast majority present block, subsequently the sake piece chain.The black chain In this way goes about Likewise a absolute wellspring of truth, Furthermore parts done a piece chain organize can see just the individuals transactions that are pertinent with them.
Exfiltrating data from an air-gapped system through a screen-camera covert channel

In recent years, many methods of exfiltrating information from air-gapped systems, including electromagnetic, thermal, acoustic and optical covert channels, have been proposed. However, as a typical optical channel, the screen-camera method has rarely been considered; it is less covert because it is visible to humans. In this paper, inspired by the rapid upgrades of cameras and monitors, we propose an air-gapped screen-camera covert channel with decreased perceptibility that is suitable for complex content. Our method exploits the characteristics of the human vision system (HVS) and embeds quick response (QR) codes containing sensitive data in the displayed frames. This slight modification of the frames cannot be sensed by the HVS but can be recorded by the cameras. Then, using certain image processing techniques, we reconstruct the QR codes to some degree and extract the secret data with a certain level of robustness due to the error correction capacity of QR codes. In the scenario to which our method applies, we assume that a program has been installed in the target system and has the authority to modify the frames without affecting the normal operations of valid users. Cameras, such as web cameras, surveillance cameras and smartphone cameras, can be receivers in our method. We illustrate the applicability of our method to frames with complex content using several different cover images. Experiments involving different angles between the screen and the camera were conducted to highlight the feasibility of our method with angles of $ 0°, 15°$ and 30° .
Prying CoW: Inferring Secrets across Virtual Machine Boundaries

By exploiting a side channel created by Copy-on-Write (CoW) operations of modern file systems, we present a novel attack which allows for detecting files in a shared cloud environment across virtual machine boundaries. In particular, we measure deduplication operation timings in order to probe for existing files of neighbouring virtual machines in a shared file system pool. As a result, no assumptions about the underlying hardware and no network access are necessary. To evaluate the real-world implications, we successfully demonstrate the feasibility of our attack on the ZFS file system. Our results clearly show that the presented attack enables the detection of vulnerable software or operating systems in a victim’s virtual machine on the same file system pool with high accuracy. Furthermore, we discuss several potential countermeasures and their implications.
Robust Multi-UAV Route Planning Considering UAV Failure

This paper describes a robust multi-UAV route planning problem in which any one of the vehicles could fail during plan execution at any visited location. The UAVs must visit a set of fixed locations; if one UAV fails, the other vehicles must cover any unvisited locations. The objective is to optimize the worst-case cost. This paper formulates the problem with a min-sum objective (minimizing the total distance traveled by all vehicles) and a min-max objective (minimizing the maximum distance traveled by any vehicle). A Genetic Algorithm (GA) was used to find approximate robust optimal solutions on seven instances. The results show that the GA was able to find solutions that have better worst-case cost than the solutions generated by other approaches that were tested.
A Separate 3D Convolutional Neural Network Architecture for 3D Medical Image Semantic Segmentation

To exploit three-dimensional (3D) context information and improve 3D medical image semantic segmentation, we propose a separate 3D (S3D) convolution neural network (CNN) architecture. First, a two-dimensional (2D) CNN is used to extract the 2D features of each slice in the xy -plane of 3D medical images. Second, one-dimensional (1D) features reassembled from the 2D features in the z -axis are input into a 1D-CNN and are then classified feature-wise. Analysis shows that S3D-CNN has lower time complexity, fewer parameters and less memory space requirements than other 3D-CNNs with a similar structure. As an example, we extend the deep convolutional encoder–decoder architecture (SegNet) to S3D-SegNet for brain tumor image segmentation. We also propose a method based on priority queues and the dice loss function to address the class imbalance for medical image segmentation. The experimental results show the following: (1) S3D-SegNet extended from SegNet can improve brain tumor image segmentation. (2) The proposed imbalance accommodation method can increase the speed of training convergence and reduce the negative impact of the imbalance. (3) S3D-SegNet with the proposed imbalance accommodation method offers performance comparable to that of some state-of-the-art 3D-CNNs and experts in brain tumor image segmentation.
KFFM-based Large-scale Project Earned Value Management Completion Prediction

Earned value management lacks research on the project completion trend forecasting method at the following two points: The project completion trend forecast is based on the state indicators at the project monitoring point, and the project execution efficiency before the monitoring time point is rarely considered; the project performance evaluation index How to reduce the non-efficiency noise caused by uncontrollable factors in the existing prediction methods when singularities occur. The above issues will affect the accuracy and stability of the earned value management forecasting system. This paper attempts to introduce the Kalman filtering method (KFFM) into the earned value management completion prediction calculation, and comprehensively considers the project execution efficiency in the logical order of â€œprediction-measure-correctionâ€, and improves the prediction accuracy through dynamic correction of cost/progress indicators. And effectively reduce the impact of the earned value management forecasting system due to singularities
An Exploration of the Impact of Partial Scalar Invariance on the Estimation of Latent Means in a Bifactor Model

잠재평균의 집단 간 차이를 타당하게 비교하기 위해서는 모형의 측정 구조가 집단 간에 동일하다는 가정에 대한 평가가 선행되어야 한다. 그러나 경험 데이터를 활용한 연구에서는 요인부하량이나 절편이 집단 간에 동일하다는 가정이 위배되는 사례가 빈번히 발생하며, 이러한 상황에서 연구자는 동일성이 위배되는 측정변수의 요인부하량이나 절편에 대한 동일성 제약을 완화한 상태에서 잠재평균 분석을 수행하는 접근을 취하곤 한다. 본 연구에서는 다양한 잠재변수 모형 중 Bifactor 모형을 적용하여 잠재평균 분석을 수행하는 상황을 가정하여, 집단 간 절편동일성 가정이 위배되는 경우에 절편동일성에 대한 부분 동일성 제약의 다양한 수준에 따라 절편동일성 검증의 적합도 평가 및 잠재평균 차이 검증의 제1종 오류에 미치는 영향을 탐색하였다. 이를 위해 각 집단에 절편의 비동일성 수준을 부여하여 모의자료를 생성하고, 생성된 모의자료로 절편동일성 검증과 잠재평균 분석을 실시하였다. 그 결과, Bifactor 모형의 전체 표본크기가 커질수록 절편동일성 모형에 대한 CFI와 RMSEA의 변화량이 증가하였고, 집단 간 표본크기의 차이가 작아질수록 RMSEA의 변화량이 증가하였다. 또한 집단 간 표본크기의 차이가 작고 절편동일성이 위배된 측정변수에 대한 절편의 집단 간 차이와 비율이 증가할수록 절편동일성에 대한 기각률이 높아졌다. 잠재평균 차이 검증에 대한 제1종 오류는 전체 표본크기와 비동일 절편의 비율이 커질수록 증가하였다.In order to make a valid comparison on the group differences of the latent means, an evaluation should be made on the assumption that the model s measurement structure is invariant across the groups. However, in empirical studies, the invariance assumption of factor loadings or intercepts is often violated. In this situation, researchers would proceed the latent means analysis after relaxing the equality constraints imposed on the variables that do not meet the invariance assumption. However, the extent to which the degree of partial measurement invariance influences on the results of the latent means analysis should be carefully examined. In this study, we specified a Bifactor model with varied levels of partial scalar invariance, and explored the impact of the level of partial invariance on the model-data fit of the partial scalar model and the type I error of the latent means analysis. The findings of the study showed that the differences of CFI and RMSEA between the scalar invariance model and the baseline model increased as the total sample size of the Bifactor model increased and the sample sizes of the two groups are balanced. As the reduction rate and the proportion of invariant intercepts increased, and the difference of sample sizes between groups decreased, the rejection rate of scalar invariance model increased. In addition, as the proportion of invariant intercepts and total sample size increased, TypeⅠerror of latent mean analysis increased.
Iterative Approach of Gravity and Magnetic Inversion through Geostatistics

Summary In this work was demonstrated iterative approach of potential fields (gravity and magnetics) inverse problem solution with the aim of correct accounting of sedimentary section. The approach consist of simple model creation and further potential fields forward solution and comparison with the base case and after that model modification. Different iterations of simple model construction were tested on the “ground truth” detailed model with the main features of the West Siberia Palaeozoic deposits. The objective of the research is to minimize effect of sedimentary layers on the gravity and magnetic fields by modeling with geostatistical approach (stochastic simulation with trends) and explain how to use this approach according to real data.
Implementasi Metode Waterfall Pada Pengembangan Sistem Informasi Penerimaan Siswa Baru Berbasis Web Pada SMK Negeri 2 Adiwerna

Abstract: The development of information technology more advanced and very rapidly, one of which is the internet. Wtih the internet, people are easier in getting information quickly and efficiently. Especially in the provision of information for an organization/institution or company that requires data management system quicly, precisely and accurately. With the internet , an institution such as school can provide more complete information, up to date, interavtive adn dynamic. At SMK Negeri 2 Adiwerna information system related to school still dilevered in the form of manual book and acceptnce of new student still done manually. Recap process also has not run optimally so often the occuranceof data errors. In addition, applicants not only come from Tegal even outside Tegal aea that requires parents and prospective students to come to school to obtain information about the school and make the regristation process causing long quees, resulting in less efficiency in time and a lot of costs tobe incurred. That’s why the author tries to make the new web base admissins information system at SMK Negeri 2 Adiwerna. Trough this system is expected to facilitate prospective students and parents in getting information about the school without having to come queuing to school.&#x0D; &#x0D; Keyword: Information System, New Student Reception SMK Negeri 2 Adiwerna Online
3D Geological Image Synthesis from 2D Examples Using Generative Adversarial Networks

Summary Recently, Generative Adversarial Networks (GAN) have been proposed as a potential alternative to Multipoint Statistics (MPS) to generate stochastic fields from a large set of training images. A difficulty for all the training image based techniques (including GAN and MPS) is to generate 3D fields when only 2D training data sets are available. In this paper, we introduce a novel approach called Dimension Augmenter GAN (DiAGAN) enabling GANs to generate 3D fields from 2D examples. The method is simple to implement and is based on the introduction of a random cut sampling step between the generator and the discriminator of a standard GAN. Numerical experiments show that the proposed approach provides an efficient solution to this long lasting problem.
SEMANTICS IN OBEY CLOTHING T-SHIRT DISCOURSE: A STUDY OF SEMANTICS

In this study, it is analyzed lexical meaning in a text of written language which is applied on Obey’s T-shirt. These meanings furthermore constructed descriptive interpretations. The theory used in this study is theories of lexical semantics in Mansoer Pateda (2010). Reseach method in this study is descriptive analysis which is conducted by using analytic approach and operational approach of lexical meaning. This approach includes meaning component segmentation and lexical meaning analysis. The result of this study which is conducted on eight product samples is that each product has its own characteristic which can be constructed through interpretation based on lexical meaning of each entity in the product. The lexical meaning which is analyzed is capable to construct meaning related to the object outside the language. Both meaning, lexical and cognitive meanings are able to construct an interpretation as a meaning representation of a text as a whole part.
Sequential Regression with Missing Data Using LSTM Networks

We study regression for variable length sequential data suffering from missing samples and introduce a long shortterm memory (LSTM) based sequential regression algorithm. In most sequential regression studies, one considers data sequence is complete, i.e., does not contain any missing data. However, the missing data problem appears in a large number of areas such as finance and medical imaging. The remedies to resolve this problem depends on certain statistical assumptions and imputation techniques. However, the statistical assumptions does not hold in real life and the imputation of artificially generated inputs results in sub-optimal solutions. In our experiments, we achieve significant performance gains with respect to the classical algorithms.
An Efficient Arabic HMM System Based on Convolutional Features Learning

Published works recently indicate that the generic features extracted from the convolutional neural networks are very powerful. This paper shows that CNN feature can be used with a HMM system [1] for Arabic handwritten word recognition, to yield classification results that outperform the handcrafted features. These features are usually based on heuristic approaches that describe either basic geometric properties or statistical distributions of raw pixel values. The CNN features based HMM is shown satisfactory recognition accuracy on the well-known IFN/ENIT database and outperformed some other prominent existing methods.
Development of seasonal solar radiation estimation models for Dokuz Eylul University campus area for controller of a two-axis solar tracker

In urban locations, it is generally hard to find enough spaces for solar power plants. However, micro-energy generation systems like solar trackers can be located even in crowded cities and becoming a significant technology in solar energy systems. In this study, the measured global solar radiation data for the research area, Tinaztepe/Izmir (38°22'15.3N 27°12'28.5E), were logged, then analysed using statistical methods and seasonal daily global solar radiation mathematical models of Tinaztepe/Izmir were developed. The results of these mathematical models give more precise daily average solar radiation value for Tinaztepe/Izmir than previous Izmir global solar radiation models.
Elastic Property Reflectivities as Basis Vectors for Intercept-Gradient Crossplots

Summary The reflectivity of any isotropic elastic property can be expressed as a sum of velocity and density reflectivities. So too can the standard AVO parameters, intercept, gradient and curvature. This allows elastic property reflectivities to be expressed as sums of AVO parameters. By approximating the curvature term where necessary elastic property reflectivities are shown to be vectors in AVO intercept-gradient (AB) space, the vector angles and lengths having a small dependence on Vp/Vs ratio. By using pairs of normalized elastic property reflectivities as basis vectors the position of reflectivity points in AB space may be directly determined from elastic property contrasts. This gives more intuitive insight to the structure of intercept-gradient crossplots. Expressions for the impedance equivalent of any reflectivity expression, such as elastic or extended elastic impedance, can be directly derived from the standardised reflectivity expression.
Hubungan Minat Baca dan Penguasaan Kosakata dengan Pemahaman Bacaan Berbahasa Inggris

This research is aimed at finding out the nature and strength of relationships between reading interest, vocabulary mastery with English reading comprehension, both separately and simultaneously. The research was conducted at students of first and second semester of Islamic Religion Education Department of STIT Palapa Nusantara Lombok-NTB with 50 students taken as samples using random sampling. The technique used to analyze data was the statistical technique of regression and correlation. The result of the research indicates that there are positive correlation between: (1) reading interest with English reading comprehension; (2) vocabulary mastery with English reading comprehension; and (3) reading interest and vocabulary mastery with English reading comprehension. Based on the result of the research, it can be concluded that English reading comprehension can be developed by increasing reading interest and vocabulary mastery.
DeCACHe: Supporting Designers in Creating Cognition-Centered Adaptive Cultural Heritage Activities

Cultural heritage (CH) institutions attract wide and heterogeneous audiences, which should be efficiently supported and have access to meaningful CH content. This introduces numerous challenges when delivering such experiences, given that people have different cognitive characteristics which influence the way we process information, experience, behave, and acquire knowledge. Our recent studies provide evidence that human cognition should be considered as a personalization factor within CH contexts, and thus we developed a framework that delivers cognition-centered personalized CH activities. The efficiency and the efficacy of the framework have been successfully assessed through two user studies, but non-technical professionals (e.g., CH designers) may face difficulties when attempting to use it and create personalized CH activities. In this paper, we present DeCACHe, which supports CH designers in creating cognition-centered personalized CH activities throughout different phases of the design lifecycle. We also report a user study with seventeen professional CH designers, who used our tool to design CH activities for people with different cognitive characteristics.
IMPLEMENTASI ALGORITMA FISHER YATES UNTUK PENGACAKAN SOAL PADA APLIKASI MOBILE LEARNING KUIS FIQIH BERBASIS ANDROID

The conventional learning process carried out face-to-face, is less effective as a source of learning, because the focus of students' interest has now shifted to their device so that students have difficulty repeating learning material. This study aims to produce learning media based on mobile learning on the android platform which is used as a source of learning fiqh applications for students. This mobile learning application is built by applying the Fisher Yates Shuffling algorithm or commonly known as the Fisher Yates algorithm which is now widely used in the process of developing randomization applications. This randomization method is the optimal randomization method in application development, being able to randomize the amount of material about the science of jurisprudence and prevent cheating users who only memorize answers without understanding the material when the questions are fixed or not random. Applications are built using MySQL as a database designed to manage and manipulate data quickly and easily. PHP and Perl are used as scripting programming languages for the internet and collaborate on the Android platform. The system development method used is RUP (Rational Unified Process) by collecting various best practices found in the software development industry.System testing is done using a white box and black box testing shows that the Fisher Yates algorithm can be applied in the mobile learning quiz application as a randomizer about questions. User Acceptance Model (UAT) test results show that this mobile learning application can help the process of learning the science of jurisprudence as well as being a reference in seeing the ability of students to learn the science of jurisprudence. Keywords: Mobile learning, Fisher Yates Algorithm, Fiqih, MySQL, RUP, UML
PERANGKAT LUNAK KEAMANAN DATA MENGGUNAKAN ALGORITMA KRIPTOGRAFI SIMETRI TINY ENCRIPTION ALGORITHM (TEA)

Data security is one of the most important aspects in information technology. With a high level of security, it is expected that the information presented can be maintained its authenticity. The method for forming systems uses object-oriented methods. By using the Tiny Encryption Algorithm (TEA) Algorithm which is a symmetric key cryptographic algorithm, this problem can be overcome. The strength of this algorithm lies in feistel networks (including substitution operations, permutations and arithmetic modular), the implementation of which is analyzed using the encryption and encryption message encryption process in text format (as in * .txt format). Data security software is expected to be achieved so that other researchers who will research further can develop it again. Keywords: Data Security, Algorithms, Symmetry, TEA DOI : http://doi.org/10.5281/zenodo.3625286