Reviewerly/exHarmony huggingface dataset:
Exposing generational and gender gap in phishing awareness among young adults: A survey experiment

Phishing is one of the most common cybersecurity attacks that target end users. Increasing user awareness of such a social engineering approach is a good, if not the best, countermeasure of phishing. While many have tried to educate users on this matter, such interventions may not work equally good for all demographic groups. In this paper, we conducted an experimental study involving 247 participants to see if a small intervention in the form of educational infographics can improve phishing awareness among young adults and if the effects were comparable for both males and females. It is apparent from the results of this study that men and women differ significantly in their knowledge of cybersecurity, particularly in regard to phishing, and that some cybersecurity education might not be effective for women. In addition, we found that the older millennials in their 30s had a much better phishing awareness than the younger generations (i.e., the 20s and the teenagers), with or without any interventions. Further research is required to figure out the factors leading to these differences in order to prevent cybercriminals from preying on one gender over the other, as well as to develop a good cybersecurity education approach that works to raise cybersecurity awareness of users regardless of their age or gender.
Application of evolutionary algorithms for the identification of the first-order differential equations systems

The paper considers a new approach to the identification of dynamic objects in the form of the first-order differential equations system. The peculiarities of the proposed approach are the absence of restrictions on the structure of the differential equations included in the system and the symbolic representation of the obtained solution. The proposed approach includes a self-configuring genetic programming algorithm for selecting the structure of differential equations. The number of algorithms corresponds to the number of equations in the system. The method of differential evolution is used to optimize numeric constants. The authors apply a self-configuring procedure for the indicated evolutionary algorithms. The proposed approach has been tested on a variety of problems. The accuracy of the obtained solution has been investigated depending on the presence of noise in the sample data.
Online clustering algorithm with a greedy agglomerative heuristic procedure and special distance measures

Automatic grouping (clustering) involves dividing a set of objects into subsets (groups) so that objects from one subset are more similar to each other than to objects from other subsets according to some criterion. There are industries where data come in the form of a stream, i.e. a sequence of potentially infinite, non-stationary data arriving continuously. In our study, we propose a greedy agglomerative heuristic algorithm with distance measure variations for online products clustering. Computational experiments demonstrate the comparative efficiency and accuracy of using the greedy agglomerative heuristic in online clustering of industrial products into homogeneous production batches.
Requirement validation approach using model and prototyping on agriculture information system case study: Dutatani agriculture information system

Information system technology has developed from time to time. Not only in terms of hardware and software, but the coding technique has also changed. This change certainly affects the existing and future business processes. Various fields of science, including agriculture, also utilize information systems to improve quality and services in the agricultural sector. The Indonesian government strongly supports the activities of improving information system technology in agriculture. Dutatani's research team has also developed an agricultural information system (SIP), which has been consistently developed since 2016 towards precision agriculture. Along with the development of SIP technology, it requires re-validation of business process needs compared to changes in information system technology. As evidenced by previous research, many technologies have been tried to apply to SIP development and generate code smells. Therefore, this study aims to apply the Requirement Validation Approach using Model and Prototyping (ReVAMP) which is suitable for Agile software development conditions. Through several iterations and following the code smell instructions from previous studies, this study succeeded in making business process changes by 37% and simplifying data attributes by 56%. This research also reveals the fact that ReVAMP can transform business processes as well as simplify them.
Biology-inspired optimization algorithms applied to intelligent input weights selection of an extreme learning machine in regression problems

Modern artificial neural network architectures and training algorithms are able to achieve high accuracy in a wide range of problems. However, training multilayer neural networks using backpropagation or evolutionary algorithms might take a large amount of time. Extreme learning machines (ELMs) are aimed to resolve this problem by excluding training from the neural network model creation process by randomly initializing weights between input and hidden layers and computing weights between hidden and output layers. However, random weights initialization might lead to suboptimal results produced by the network. In this paper, we apply biology-inspired algorithms, including genetic algorithm with tournament selection, particle swarm optimization, and chaotic fish school search with exponential step decay, to the selection of input weights in ELM, the obtained ELM configurations are applied to solve regression problems. The results of the study show, that population-based algorithms can improve ELM accuracy.
Improving automotive student's creativity and online learning motivation through project-based learning in entrepreneurship creative products subjects

A basic problem of learning through online media during the COVID-19 is a lack of attractive learning models, so that it can reduce students' creativity and learning motivation. The application of the project-based learning model is one of the models which can overcome problems of creativity and student learning motivation in online learning. This study aimed to examine the effect of implementing a project-based learning model to increase students' creativity and online learning motivation in the creative products and entrepreneurship subject in the automotive vocational major. One of the Quasi-experimental research, Pretest-Posttest Control Group design was applied. A total of 146 vocational education students in the automotive department were selected to be involved as research samples. The observation method was used to collect data on creativity and learning motivation at the pretest and posttest using an instrument that was arranged based on the indicators in the dependent variable. The collected data were analyzed through paired sample and independent sample t-test, which were first tested for analysing prerequisites then finding mean difference. The results of data analysis showed that the project-based learning model application was proven to be significant in increasing students' creativity and online learning motivation in the creative products and entrepreneurship subjects. The learning model is significantly superior to the previous conventional model. The project-based learning model is very important to be applied in online learning in the automotive department, particularly in the creative products and entrepreneurship subject.
Go-dify: A transpiler for performance improvement in game development

Today's game enthusiasts wanted a full game immersion experience; thus, the game development industry is banking on the state-of-the-art tools and techniques to optimize the performance of game development and its execution. This research developed a transpiler that was used in compiling JavaScript-based game projects into Go Language (Golang). The objective is to optimize the game performance of at least 30 frames per second for standard games and 60 frames per second for high-definition games. Technical analysis, design and development, and testing the research output were executed to see the extent of performance improvement when HTML5 games in JavaScript are transpiled to Golang in terms of time complexity of the procedures in the program. Transpiler was designed based on generator design pattern with the parser producing an abstract syntax tree (AST) and code generator reading AST, unmarshal the data within the data set and transpile the JavaScript code to Go Language. It was evident in the results that the transpiler was to transform and improve the process and generate an output that runs similarly as its input source. There is an apparent increase in performance on the output of the transpiler compared to the performance it can do when the output was not yet processed.
Multicriterial genetic algorithm for pattern generation as a stage of logical analysis of data

The main stage of logical analysis of data is the generation of patterns that have the maximum coverage of sample observations and also fulfill the constraints on the homogeneity of the coverage. To reduce the effect of overfitting, these constraints should be weakened and the heterogeneity of the pattern should be allowed. However, it is a challenge to determine in advance what kind of heterogeneity is acceptable for each data set. In this paper, we consider the problem of pattern generation as a multicriteria optimization problem where the criteria are the coverage of objects of the target class and coverage of objects that are not included in the target class. To solve this problem, a modified evolutionary algorithm based on the NSGA-II algorithm has been developed. The new algorithm takes into account the representation of the solution (coding patterns) in logical analysis of data and the informativeness of patterns.
A new neuro-fuzzy approach based on modified AGSK algorithm for classification problems

This paper introduces a new neuro-fuzzy classifiers trained by recently developed modification of the Adaptive Gaining Sharing Knowledge (AGSK) optimization algorithm. Mentioned modification is based on applying the success-history based position adaption technique to improve the AGSK approach's ability to explore and exploit the search space. Proposed neuro-fuzzy classifiers were used to solve the opinion mining problems, to be more specific, they were applied to determine the customers’ evaluation of coffee products based on various parameters. Besides, number of these parameters also varied with aim to define which of them are the most significant. It should be noted that these problems were distinguished due to different types of coffee beans used to prepare final products. Obtained results demonstrated the workability and usefulness of the neuro-fuzzy classifiers trained by the AGSK modification. Comparison to alternative approaches showed that the proposed technique outperforms them and, therefore, can be used instead of them for other classification problems.
Imbalanced Data Classification Approach Based on Clustered Training Set

Fraud detection is a system that prevents criminals from obtaining financial assets. The research aims to increase machine learning prediction quality on fraudulent cases as well as decrease false positive and false negative cases in prediction. Fraudulent data like credit card transactions are usually imbalanced data, and standard machine learning techniques cannot achieve the desired quality levels in this scenario. This paper proposes a clustering-based classification method to improve the recall. For the experimental evaluation, we use a credit card transaction database. Firstly we suggest finding the optimal features and number of clusters to create smaller, more homogeneous training sets, which we train on separate machine learning models. The second step is to find relevant percentages to undersample each cluster to compensate for sharply imbalanced data. Our baseline recall is 0.845. By applying the proposed method, we improved the recall to 0.867. Moreover, classification of fraudulent cases that were labeled as regular decreased from 323 to 278, i.e. by 13.9%. The statistical test has shown that decrease is significant.
Improved access speed of the Codeigniter framework and REST APIs for the implementation of SIAKAD: Academic information system in Balikpapan schools

The development of smartphone users in Indonesia is now reaching a high number, especially in Balikpapan City. This condition can be used as a media to support smartphone activities, mainly Android on the Academic Information System (SIAKAD) at school. The results of a study at the Al-Azhar 58 Balikpapan school use Microsoft Word and Excel applications as data storage. However, these applications do not yet have a better system like SIAKAD. CodeIgniter framework with RESTful API often uses on a small and medium scale in web-based SIAKAD. However, it is also often used in the development of large-scale information systems. One of the problems that arise when accessing websites via mobile is the large amount of data that must be processed, where the specifications of mobile devices are sometimes limited. Therefore, it is necessary to develop an application to access SIAKAD with android-based smartphone devices. RESTful web service is one of the technologies used in this method. Therefore, the website and android-based SIAKAD can increase effectiveness and efficiency both in terms of cost, effort, and time. This study shows that RESTful averages have less throughput than SOAP averages. RESTful access speeds to be faster than SOAP access speeds, which cause the throughput to generate at RESTful 1736.8596 bps and SOAP 3694.62 bps.
Methods of protection in speaker verification systems

The article reviews the existing approaches used in speaker verification systems and explains why rapid development of voice authentication is taking place at the moment. The peculiarities of voice authentication systems in comparison with other biometric methods are described. The general scheme of the voice authentication system is provided. Metrics for assessing the effectiveness of voice authentication are presented. The characteristics of the most commonly used discriminative voice features are described. The classification of the most popular algorithms for automatic speaker verification is presented. The most popular spoofing methods and their essential features are characterized. The varieties of antispoofing countermeasures are described and their efficiency at protecting from different kinds of spoofing is specified. A modified classification of antispoofing countermeasures is proposed. The research indicates upcoming study directions in the speaker authentication sphere. Modern speaker verification methods are widely used and reputed. One of their major advantages is that they can be adapted not only for one-time, but also for continuous authentication. In spite of that, voice authentication systems are not completely reliable. Low price and high quality audio processing tools make them vulnerable to spoofing. Therefore, new measures aimed at preventing, detecting and protecting from spoofing should be invented to assure information security.
Analysis of variable modification in the Keetch-Byram drought index using artificial neural network

The variables in the original Keetch-Byram drought index calculation method (without modification) consisted of the average annual rainfall, the difference in humidity, and the maximum temperature. In Indonesia, drought is not only caused by reduced rainfall and high maximum temperatures. Several other variables also have the potential to strengthen the accuracy of the drought index calculation, namely wind speed, atmospheric pressure, and duration of solar irradiation. The problem is whether the addition of a new variable will change the accuracy of the calculation? If the new variable has a positive impact, which new variable has a significant effect on the calculation of the drought index. The analysis of the addition of variables in this study uses the method of training and testing artificial neural networks, namely the backpropagation algorithm. The training was carried out on many original variable data, then compared with testing using new variables. The results showed that the addition of a wind speed variable and replacing one of the variables with a wind speed variable showed an increase in prediction accuracy. Prediction accuracy using the three original variables only reached 86.67%, while with the addition of one wind speed variable the accuracy increased to 91.67%. This proves that the wind speed variable is a new candidate variable that has the potential to be one of the variables that play a role in the Keetch-Byram drought index calculation model in the future.
Using Pabami Media to Improve Early Reading of Cerebral Palsy Students in the 1st Class of the Elementary School for Children with Special Needs

The ability to read is the most basic competency to master competencies in other subjects. Learning to read which was carried out at Elementary School for Children with Special Needs (SDLB) YPAC Malang City found that one out of three students of Cerebral Palsy in 1st class had problems with early reading. One of the causes of not optimal students' reading ability is because teachers tend to only use printed books. Appropriate and interesting media is needed so that students can have reading skills as expected. The purpose of this study is to describe the use of pabami media to improve early reading skills in students of Cerebral Palsy in the 1st class at SDLB YPAC Malang. This research is a classroom action research designed in 2 cycles, each cycle consists of 3 meetings. The learning media used is pabami which is equipped with cards of letter, syllable, and word. Data were collected by observation and test techniques. Pabami media is declared effective if the posttest results in the second cycle are higher than the posttest scores in the first cycle, with learning mastery indicators reaching a value of 75 or more. The results of the study concluded that pabami media was effective for improving early reading skills of students with cerebral palsy.
Improved UCAV Attack Trajectory Planning Algorithm Based on GWO and Dubins Curve

Around the key technology of path planning and attack for unmanned combat air vehicle, this paper sorts out the relevant planning algorithms. Combining the existing planning method based on gray wolf optimizer (GWO) and Dubins theory, we improve it in terms of model and algorithm. In terms of model construction, a new terminal constraint using the concept of the launch acceptability regions (LAR) of weapons is designed to introduce attack and weapon elements into the attack trajectory planning problem; at the algorithm level, redesign the coding of GWO to make it more suitable for current operational decision. The simulation and comparison results show the improved algorithm outperforms the traditional algorithm in terms of solution speed and other aspects in the same battlefield environment.
Improved JPS+ Path Planning Algorithm Based on Hexagonal Grid Map

Mobile robots have been used in a wide variety of applications, and path planning algorithm is one of the key technologies to realize autonomous navigation. The existing JPS+ algorithm usually operates on the square grid map, which has some disadvantages that are not suitable for robot control. Aiming at the above problems, this paper studies the path planning algorithm based on the hexagonal grid map instead of the square grid map. Based on JPS+ algorithm, this paper designs the preprocessing process for hexagonal grid map, improves the evaluation function and smooths the obtained path, and proposed the improved JPS+ algorithm based on hexagonal grid map. Simulation experiments verify the effectiveness of the proposed JPS+ algorithm.
A SVM Based Method for Stable Target Tracking in the Clutter Areas

When the target tracked by the radar enters the strong clutter area, if the correlation between the clutter plot and the target trajectory is better than the correlation between the target plot and the target trajectory, the target trajectory will be tracked to the clutter incorrectly, resulting in the loss of the target, thus affecting the trajectory tracking correctness and continuity. This paper proposes a stable target tracking method in strong clutter based on SVM. Firstly, the target plot and the clutter plot are distinguished from multi-dimensional features to realize the rough classification of plot, then the plot set and cluster set are regressively associated, and finally all the cluster state are updated according to the regressively association results. The results of simulation and measured data processing show that this method can improve the association accuracy and tracking stability of trajectory in the strong clutter, and improve the quality of the output trajectory.
Autonomous Recovery System of Aerial Child-Mother Unmanned Systems Based on Visual Positioning

The aerial child and mother UAV systems can effectively improve the operational range and task flexibility of UAV systems, and their recovery method is of great significance for the cooperative operation between the child and mother UAVs. In this paper, a separated lift and thrust vertical takeoff and landing mother UAV and a multi-rotor child UAV are proposed for the child-mother UAV systems. The solutions are proposed for the navigation and control problem of the recovery mission. The recovery mission is divided into an approaching stage, and a landing stage, using GPS and visual positioning to provide navigation information for the child UAV, respectively. In addition, the proposed multi-level visual marker effectively avoids the loss of navigation information during the landing process. Moreover, the recovery control problem of the child UAV is transformed into a position tracking problem, and the designed controller effectively ensures the accurate recovery of the child UAV. Finally, the proposed child-mother UAV systems are tested in a simulated environment (Gazebo) and verified that the proposed navigation and control method can effectively perform the recovery mission of the child UAV in static and moving states of the mother UAV.
UAV Path Planning Based on Improved Artificial Potential Field Method

An improved artificial potential field (IAPF) is proposed in view of the deficiencies of the traditional artificial potential field (TAPF) method in the 3D path planning of unmanned aerial vehicles (UAVs). First, a calculation method of repulsive potential field based on different obstacles is introduced. Then, an adaptive speed adjustment strategy is designed considering the physical constraints of the UAV. Meanwhile, a two-step method is proposed for the oscillation of local paths. Finally, the simulation results show that the IAPF effectively surmounts the weaknesses of the TAPF and is more consistent with the actual flight situation of the UAV.