Reviewerly/exHarmony huggingface dataset:
NNRW-Based Algorithm Selection for Software Model Checking

Software model checking is the technique that can automatically verify whether a given software meets the correctness properties. Although a large number of software model checkers have been developed, it remains a difficult task for practitioners to select a suitable checker for the software at hand, given the fact that the underlying techniques and performance trade-offs of these tools are hard to accurately characterize. In this paper, we study the algorithm selection problem for software model checking and apply the neural network techniques, in particular the neural networks with random weights (NNRW) to solve this problem. We also carry out a thorough experimental evaluation of the performance of three typical NNRW (i.e. RVFL, ELM, SCN) based on publicly available dataset. Our results demonstrate strong viability and usefulness of NNRW for this problem. To the best of our knowledge, this is the first work that applies NNRW techniques to algorithm selection for software model checking.
Analysis of the Perception of Students of the Autonomous University of Baja California Sur for the Use of m-Learning

Currently there is a fairly widespread tendency to investigate the use of mobile devices in education, since this type of device makes learning more flexible due to the possibility of using it anywhere and anytime. There are numerous projects that refer to the use of m-Learning and its incorporation into educational processes. This paper presents the results of an experience that was applied to a group of students of the Academic Department of Computational Systems (DASC) of the Autonomous University of Baja California Sur (UABCS) with the objective of analyzing the use of mobile devices as support in the classroom. The results show that the students improved the achievement rate, showing availability for the use of m-Learning. In terms of perception, the recognition of the benefits of using m-Learning environments stands out.
A General Framework of Smart Contract Vulnerability Mining Based on Control Flow Graph Matching

A good smart contract can provide safe and reliable services to efficiently solve real-world problems. However, smart contracts written by developers create potential security vulnerabilities and can result in huge economic losses. Therein, detecting potential vulnerabilities in smart contracts plays an important role in maintaining the security of the blockchain. There are a series of schemes work on finding bugs in smart contracts, such as Oyente and ContractFuzzer, etc. However, existing solutions only address several types of security vulnerabilities on a single platform. In this paper, we propose a general framework to detect potential vulnerabilities of smart contracts. In order to abstract the execution logic of smart contracts, we need to convert them into control flow graphs. We use the Ullmann algorithm to discover vulnerabilities from these graphs. Also, we give a running example to illustrate how our framework can be used to detect the reentrancy problems written by solidity.
Experimental adaptive quantum state tomography based on rank-preserving transformations

Quantum tomography is a process of quantum state reconstruction using data from multiple measurements. An essential goal for a quantum tomography algorithm is to find measurements that will maximize the useful information about an unknown quantum state obtained through measurements. One of the recently proposed methods of quantum tomography is the algorithm based on rank-preserving transformations. The main idea is to transform a basic measurement set in a way to provide a situation that is equivalent to measuring the maximally mixed state. As long as tomography of a fully mixed state has the fastest convergence comparing to other states, this method is expected to be highly accurate. We present numerical and experimental comparisons of rank-preserving tomography with another adaptive method, which includes measurements in the estimator eigenbasis and with random-basis tomography. We also study ways to improve the efficiency of the rank-preserving transformations method using transformation unitary freedom and measurement set complementation.
Multi-Scale Anti-Occlusion Correlation Filters Object Tracking Method Based on Complementary Features

Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.
On the equivalence of several classes of quaternary sequences with optimal autocorrelation and length $ 2p$

&lt;p style='text-indent:20px;'&gt;Quaternary sequences with optimal autocorrelation property are preferred in applications. Cyclotomic classes of order 4 are widely used in the constructions of quaternary sequences due to the convenience of defining a quaternary sequence with the cyclotomic classes of order 4 as its support set. Recently, several classes of optimal quaternary sequences of period &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ 2p $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, which are all closely related to the cyclotomic classes of order 4 with respect to &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \mathbb{Z}_p $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; were introduced in the literature. However, less attention has been paid to the equivalence between these known results. In this paper, we introduce the unified form of this kind of quaternary sequences to classify these known results and then conclude the unified forms of these optimal quaternary sequences. By doing this, we disclose the relationship between the optimal quaternary sequences derived from different methods in the literature on one hand. And on the other hand, when the new obtained optimal quaternary sequence period is &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ 2p $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and the cyclotomic classes of order 4 are involved, the methods and the results given in this paper can be used to identify if the sequence is new or not.&lt;/p&gt;
Estimation of Some Genetic and Physiological Variables of Iraqi Desert Snake Cerastes gasperettii

This study nominate the first blood indicate ranges for desert snake (Cerastes gasperettii) , Twenty-eightsamples of the species under study were collected during field trips in the study area (Anbar desert locatedat western sector of Iraq about 400 kilometers west of Baghdad City).The examined hematologic values ofthe (Cerastes gasperettii) snake showed the highest erythrocyte ,WBC , hemoglobin ,MCH , MCHC andhematocrit value in male when a compared with females, The MCV value was found to be the highest infemales and the lowest in male, karyological study revealed that the chromosome number is 2n (diploid)and the fundamental number (ZW) is 34 in male and female. DNA fragment was observed by agarose gelelectrophoresis for CTNNB1 and WAC specific primers, the derived fragments ranged from approximately600 bp, Additionally, in the CTNNB1 specific primer amplification of DNA fragments was found in maleand female and ranged from approximately 900-bp DNA band
PEDOCHEMICAL STUDIES ON DESERTIC SOILS OF EL SADAT CITY AND NOUBARIA USING SPECTRAL REFLECTANCE.

The current investigation aims to study the geomorphological and pedochemical characteristics of El Noubaria and El-Sadat city soils using the spectral reflectance and GIS technology. Soil classification and land evaluation are also carried out. Moreover, an electromagnetic induction instrument (Profiler EMI-400) was used to assessing soil salinity in the field for selected parts in these areas.Three main geomorphic units were identified in El-Sadat area namely, Flood Plain (3,02% from this area), Alluvial Plain (32,25%), and Aeolian Plain (58,13%). Also, three main geomorphic units were identified in El Noubaria studied area namely, Alluvial Plain (2,78% from this area), Elongated Hills (32,79%), and Calcareous Valley (58,7%). Fifty-five profiles were selected representing the soils of El Sadat units and sixty-four profiles were selected to represent the soils of El Noubaria units. Land and site features are observed and registered. Soil profiles were dug deep down to 150 cm, morphologically described, and samples were collected representing the subsequent layers in each profile for integrated physiochemical analyses. Moreover, the EMI-400 instrument was used for assessing soil salinity in the field for three selected parts in the two studied areas. Soils of the two studied areas are deep having almost flat to gently undulating topography and well drainage system. The soils of El-Sadat area have mostly loamy sand texture while, the soils of El Noubaria have loamy texture. All studied soils are slightly to moderately alkaline, non-to slightly saline especially in some parts of El Noubairia having low Organic matter (OM) and gypsum contents. The soils of El-Sadat area are slightly calcareous, whereas, that of El Noubaria area are strongly calcareous. Most of El-Sadat soils are classified as Typic Torripsaments and some as Typic Torriorthents. While, most of El Noubaria soils are classified as Typic Haplocalcids. According to the land suitability evaluation, most of studied soils are categorized into grade S1. The other soil categories could be maintained by reclaiming their limitations. The statistical analyses indicated that, there was a high significant correlation coefficient between EMI-400 readings and lab determined EC (dSm-1) for measuring soil salinity in the two upper layers in all studied soils especially at band of 15 KHz. The integration between RS and GIS technology was used to produce results and all geomorphic, suitability and salt distribution maps in this work.
DESIGN OF AUTOPILOT TANK PROTOTYPE MONITORING SYSTEM ON GROUND STATION USING GOOGLE MAPS

Indonesia is one of the largest archipelago countries in the world. Therefore, it is necessary to safeguard the vital object especially within the border area of the (NKRI). The unmanned tank rides are one of the rides that can be used as combat rides on the battlefield in maintaining security and sovereignty (NKRI). Utilization of unmanned tank rides on the durability and defence functions of the nation can minimize casualties. In realizing the control system and monitoring of unmanned tank rides using the design build Monitoring system Autopilot Prototype Tank on the Ground Station using Google Maps. In this design defines the needs of the system on the ground station. The need for electronic maps to display the location of the prototype tanks on the system interface, using one of the Gmap.net program libraries or libraries that can be integrated with the C# programming language. The need for data transactions through UART, using the library or series of communication program libraries for this need. The need for prototype tank location indicator and waypoint location in the form of marker on map, using one of Gmap Marker function from library or program library Gmap.net. The need to describe a straight line between waypoint points, using the polyline function of the Gmap.net library or program library. The need to describe the area on the waypoint, using the circle function of the Gmap.net library or program library. Autopilot control of the prototype tanks that have been performed can be done well using Visual Studio 2012 programming. Monitoring system on the interface of the system that is made shows the system can run well, with the results of the system can display prototype travel to every waypoint made.&#x0D; &#x0D; Keywords: Visual Studio 2012, Gmap.net, Prototype, Autopilot.
A High-Performance Model based on Ensembles for Twitter Sentiment Classification

Background and Objectives: Twitter Sentiment Classification is one of the most popular fields in information retrieval and text mining. Millions of people of the world intensity use social networks like Twitter. It supports users to publish tweets to tell what they are thinking about topics. There are numerous web sites built on the Internet presenting Twitter. The user can enter a sentiment target and seek for tweets containing positive, negative, or neutral opinions. This is remarkable for consumers to investigate the products before purchase automatically. Methods: This paper suggests a model for sentiment classification. The goal of this model is to investigate what is the role of n-grams and sampling techniques in Sentiment Classification application using an ensemble method on Twitter datasets. Also, it examines both binary and multiple classifications, which are classified datasets into positive, negative, or neutral classes. Results: Twitter Classification is an outstanding problem, which has very few free resources and not available due to modified authorization status. However, all Twitter datasets are not labeled and free, except for our applied dataset. We reveal that the combination of ensemble methods, sampling techniques, and n-grams can improve the accuracy of Twitter Sentiment Classification. Conclusion: The results confirmed the superiority of the proposed model over state-of-the-art systems. The highest results obtained in terms of accuracy, precision, recall, and f-measure.======================================================================================================Copyrights©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
Pengembangan video animasi materi lambang negara indonesia untuk meningkatkan motivasi belajar dan daya ingat peserta didik kelas iii di mi ma’arif pamotan

Education in Indonesia must have an effort to develop creative, affective and quality learning activities so that the knowledge possessed has a long-term impact. This research uses R&amp;D (Research and Development) research methods. R&amp;D methods are often used to make products or develop products beforehand. In addition, researchers in collecting data through interviews with informants at MI Ma'arif Pamotan, observation and documentation. In analyzing the results of interviews regarding the lack of memory of students on state emblem material, researchers analyzed using the quantitative analysis technique of percentage and paired t-test analysis techniques. From the two analytical techniques in the study, it is known that the results of the validation questionnaire assessment from the experts stated that the textbook products developed by researchers are feasible to be applied in schools to overcome learners who lack performance in learning motivation and memory.&#x0D;
Low-Dose CT Image Denoising Using a Generative Adversarial Network Based on U-Net Network Structure

Low-dose Computed Tomography (CT) technology is widely used because it can greatly reduce the harm of scanning radiation to human body. However, due to the reduction of radiation, the projection data will be polluted and the reconstructed CT image will eventually have a lot of noise and artifacts. Therefore, how to improve the quality of CT images on the premise of reducing the radiation dose of CT scan has become a hot topic in the field of CT imaging. This article will combine the generative adversarial network (GAN) with U-net encoding and decoding structure applied in the low-dose CT (LDCT) image denoising study. Compared with other network structures, this network can extract image features more effectively and retain image details. At the same time design of stationary wavelet transform loss function, the realization of low dose CT effectively improve the quality of the images.
Gun Source and Muzzle Head Detection

There is a surging need across the world for protection against gun violence. There are three main areas that we have identified as challenging in research that tries to curb gun violence: temporal location of gunshots, gun type prediction and gun source (shooter) detection. Our task is gun source detection and muzzle head detection, where the muzzle head is the round opening of the firing end of the gun. We would like to locate the muzzle head of the gun in the video visually, and identify who has fired the shot. In our formulation, we turn the problem of muzzle head detection into two sub-problems of human object detection and gun smoke detection. Our assumption is that the muzzle head typically lies between the gun smoke caused by the shot and the shooter. We have interesting results both in bounding the shooter as well as detecting the gun smoke. In our experiments, we are successful in detecting the muzzle head by detecting the gun smoke and the shooter.
An Auto-tuning Method of the Population Size in Differential Evolution for Engineering Optimization Problems

Differential Evolution (DE) is a widely used evolutionary algorithm among many metaheuristic algorithms to solve global optimization problems. In this study, an auto-tuning method of the population size in DE was proposed and adopted the previous methods for the crossover rate and the scale factor based on DE to solve engineering optimization problems with constraints. Five engineering optimization problems were selected from literature and the proposed algorithm was evaluated while comparing with the fixed population size. The proposed method shows both comparable convergence performance and possibility of generalization of DE method to handle various problems with ease.
On the Formation of Critical Thinking of Students of a Higher Educational Institution

Objective : The paper examined for the first time the features of the formation of systemic critical thinking of students of a higher educational institution, using systemic methods specially developed for this. Background : Unfortunately, there is practically no specific technology or even a method that specifically solves the problem of forming critical thinking. Nevertheless, each person who did not even receive the skills of critical thinking in the learning process possesses this type of thinking to some extent. Method : The method of systemic structuring and studying information about the objects in question solves important tasks of developing ways of thinking that are very relevant for future specialists. Implementing the methods of systemic cognition of the studied objects and the surrounding reality ensured the development of the systematic knowledge, skills, and systemic and other relevant ways of thinking and a systematic approach in cognitive and other activities. Results : The peculiarity of what has been said is that students in the process of implementing this method learn and develop simultaneously the proposed types of activities (actions) and the corresponding ways of thinking, including critical ones, determined by them. The particularly important result of the implementation of a systematic approach and systematic methods developed on its basis is the formation of systematic-critical thinking. Conclusion : The concept of systematic-critical thinking has been developed. Using a systematic approach as a scientific method allowed to significantly transform not only theory and practice but also existing traditional technologies of the formation of critical thinking.
A simulation of autonomous robot movement directed by reinforcement learning

As companies embrace Industry 4.0 and embed intelligent robots and other intelligent facilities in their factories, decision making can be derived from machine learning algorithms and so if we are to simulate these systems we need to model these algorithms too. This article presents a discrete-event simulation (DES) that incorporates the use of a reinforcement learning (RL) algorithm which determines an approximate best route for robots in a factory moving from one physical location to another whilst avoiding collisions with fixed barriers. The study shows how the object oriented and graphical facilities of an industry ready commercial off-the-shelf (COTS) DES software package enables an RL capability without the need to use program code or require an interface with external RL software. Thus the article aims to contribute to the methodology of simulation practitioners who wish to implement AI techniques as a supplement to their input modelling approaches.
Bumps in the Code: Error Handling During Software Development

Problems come up during software development all the time. When developers hit these bumps, they must figure out what has gone wrong. Findings from three studies suggest that the way developers handle errors contributes to professional growth.
A Step-Down Test Procedure for Wavelet Shrinkage Using Bootstrapping

Wavelet thresholding (or shrinkage) attempts to remove the noises existing in the signals while preserving inherent pattern characteristics in the reconstruction of true signals. For data-denoising purpose, we present a new wavelet thresholding procedure which employs the step-down testing idea of identifying active contrasts in unreplicated fractional factorial experiments. The proposed method employs bootstrapping methods to a step-down test for thresholding wavelet coefficients. By introducing the concept of a false discovery error rate in testing wavelet coefficients, we shrink the wavelet coefficients with p-values higher than the error rate. The error rate controls the expected proportion of wrongly accepted coefficients among chosen wavelet coefficients. Bootstrap samples are used to approximate the p-value for computational efficiency. We also present some guidelines for selecting the values of hyper-parameters which affect the performance in the step-down thresholding procedure. Based on some common testing signals and an air-conditioner sounds example, the comparison of our proposed procedure with other thresholding methods in the literature is performed. The analytical results show that the proposed procedure has a potential in data-denoising and data-reduction in a variety of signal reconstruction applications.