Reviewerly/exHarmony huggingface dataset:
The Optimization of Feature Selection Based on Chaos Clustering Strategy and Niche Particle Swarm Optimization

With the rapid increase of the data size, there are increasing demands for feature selection which has been a powerful tool to handle high-dimensional data. In this paper, we propose a novel feature selection of niche particle swarm optimization based on the chaos group, which is used for evaluating the importance of feature selection algorithms. An iterative algorithm is proposed to optimize the new model. It has been proved that solving the new model is equivalent to solving a NP problem with a flexible and adaptable norm regularization. First, the whole population is divided into two groups: NPSO group and chaos group. The two groups are iterated, respectively, and the global optimization is updated. Secondly, the cross-iteration of NPSO group and chaos group avoids the particles falling into the local optimization. Finally, three representative algorithms are selected to be compared with each other in 10 UCI datasets. The experimental results show that the feature selection performance of the algorithm is better than that of the comparison algorithm, and the classification accuracy is significantly improved.
Correction to: Predicting Arabic word reading: A cross-classified generalized random-effects analysis showing the critical role of morphology

The original version of this article unfortunately contained the following errors.
An Improved Bacterial Foraging Optimization with Differential and Poisson Distribution Strategy and its Application to Nurse Scheduling Problem

Bacterial Foraging Optimization (BFO) has been predominately applied to some real-world problems, but this method has poor convergence speed over complex optimization problems. In this paper, an improved Bacterial Foraging Optimization with Differential and Poisson Distribution strategies (PDBFO) is proposed to promote the insufficiency of BFO. In PDBFO, the step size of bacteria is segmented and adjusted in accordance with fitness value to accelerate convergence and enhance the search capability. Moreover, the differential operator and the Poisson Distribution strategy are incorporated to enrich individual diversity, which prevents algorithm from being trapped in the local optimum. Experimental simulations on eleven benchmark functions demonstrate that the proposed PDBFO has better convergence behavior in comparison to other six algorithms. Additionally, to verify the effectiveness of the method in solving the real-world complex problems, the PDBFO is also applied to the Nurse Scheduling Problem (NSP). Results indicate that the proposed PDBFO is more effective in obtaining the optimal solutions by comparing with other algorithms.
An Enhanced Fall Detection Approach in Smart Homes Using Optical Flow and Residual Autoencoder

When focusing on the elderly people, falling is considered a major health problem that can lead to serious injuries; sometimes it can cause the death of them.So, fall detection is an important service for the elderly healthcare to improve the daily life of them and decrease the costs of monitoring these individuals.The fall detection is considered as a problem of detecting anomalies because falling is an abnormal activity.To deal with such issue, we present (OFSRAE) which is unsupervised fall detection framework based on deep learning techniques to detect the elderly people falls.Our proposed framework consists of two stages: the data preprocessing and the deep learning model.We applied the dense optical flow in the first stage to extract the information of the motion and direction of moving objects in the foreground.The deep learning model in the second stage is based on the convolutional long short term memory autoencoder (ConvLSTMAE) network, and the residual connections to extract spatial and temporal features of videos that are captured from thermal and depth cameras.The reconstruction error of an autoencoder is used to identify falls as anomalies recorded in such videos.We experimentally evaluated OFSRAE framework on the publicly available UR and thermal fall datasets which conserve the elderly privacy that is an important issue during monitoring.The experimental results show that the proposed framework detects falls with high performance compared to other deep learning models in the literature.
PERBANDINGAN METODE ARIMA BOX-JENKINS DENGAN ARIMA ENSEMBLE PADA PERAMALAN NILAI IMPOR PROVINSI JAWA TENGAH

Import is activities to enter goods into the territory of a country, both commercial and non-commercial include goods that will be processed domestically. Import is an important requirement for industry in Central Java. The increase in high import values can cause deficit in the trade balance. Appropriate information about the projected amount of imports is needed so that the government can anticipate a high increase in imports through several policies that can be done. The forecasting method that can be used is ARIMA Box-Jenkins. The development of modeling in the field of time series forecasting shows that forecasting accuracy increases if it results from the merging of several models called ensemble ARIMA. The ensemble method used is averaging and stacking. The data used are monthly import value data in Central Java from January 2010 to December 2018. Modeling time series with Box-Jenkins ARIMA produces two significant models, namely ARIMA (2,1,0) and ARIMA (0,1,1). Both models are combined using the ARIMA ensemble averaging and stacking method. The best model chosen from the ARIMA method and ensemble ARIMA based on the least RMSE value is the ARIMA model (2,1,0) with RMSE value of 185,8892 Keywords: Import, ARIMA, ARIMA Ensemble, Stacking, Averaging
Mobile Device-Based Authentic Learning in Educational Environments: A Systematic Review

The rapid growth of mobile technology has opened new opportunities for learners to engage in authentic learning activities. The aim of this systematic review is to provide a synthesis of research on mobile device-based authentic learning in educational environments. This review has analyzed 72 studies published between 2010 and 2019 (until mid-November) in terms of major research purposes, methodologies, research outcomes, subject matter domains, educational levels, educational contexts and geographical distributions. Three research questions guided the review process. The findings showed the major research purpose of a majority of the studies was about investigating the effects of mobile device-based authentic learning on students. Researchers have mostly used mixed-methods research to examine the learning phenomena with a majority of the studies reporting positive learning outcomes. The analysis showed that science and literacy are the most represented content areas, while most of the studies were carried out at the elementary school level. More research was conducted in informal contexts than any other contexts, and the most predominantly used mobile device type was tablet computers. The findings revealed that there is a disproportionate distribution of studies in the literature in terms of geographical contributions as studies tend to concentrate on specific countries or regions. Keywords: Authentic learning, mobile learning, situatedness, contextualization, mobile augmented, ubiquitous learning
Color Image Encryption-Decryption using RANDOM Noise and PMT

RGB color images are among the most important types of digital data circulating in the Internet and through various social media.RGB color image may contains confidential information, or the signal may be of a personal nature, which requires preventing its understanding from unauthorized entities or persons and therefore this signal must be encrypted.In this paper, we will discuss a new method for encrypting the RGB color images based on adding a random noise and image rearrangement.The method will be run to measure its efficiency and the extent to which it achieves the level of security and protection.The experimental results will be analysed to prove the proposed method efficiency factors.
Penerapan Metode Field Trip untuk Meningkatkan Kemampuan Menulis Puisi pada Siswa Kelas V-B MIN Wonosari Gunungkidul

This study aims to determine the efforts to improve the poetry writing ability of VB graders in MIN Wonosari, Gunungkidul Regency by using the field trip method 2015/2016 academic year. This research is a classroom action research. The subjects of this study were all students of V-B MIN Wonosari Gunungkidul Regency, amounting to 21 students consisting of 8 male students and 13 female students. Classroom action research steps include the activities of planning, implementation, observation, testing, and documentation. The results of the study can be concluded that: (1) the use of field trip methods can improve students' poetry writing skills-average value in the V-B MIN Wonosari class in Kab. Gunungkidul in the pre-cycle was 49.52 with learning completeness 42.86% with sufficient criteria. After carrying out learning with the field trip method increased in the first cycle, the average value became 76.19, with learning completeness of 61.90%, including good criteria. In the second cycle, the average score became 76.72, with 80.95 learning completeness, including very good criteria. (2) There is an increase in the implementation of the learning process and students. From these data, it can be concluded that the application of field trip methods can improve the ability to write poetry in students of V-B MIN Wonosari class, Kab. Gunungkidul.
Monitoring the water supply system in a distributed production area using wireless technology

The article is devoted to the results of a study on the choice of a method for monitoring the water supply network in a distributed industrial area, in particular, methods for collecting data from measuring instruments for detecting water leaks. Monitoring over a large industrial area is complicated by many factors that impede the use of existing leak detection methods: complex topography of the water supply network, which makes it difficult to segment; deterioration of infrastructure, both due to aging and corrosion; heterogeneity of the material of the manufacture of pipes; large variation in pipe diameters in one pipeline system; a variety of construction, installation and repair work carried out at the production site of the enterprise; significant level of round-the-clock production noise; the presence of uncontrolled elements of the water supply network. In addition to complicating factors, it was necessary to take into account the specified limitations: a significant distance from the infrastructure; remoteness of control places from power supply networks; small amount of transmitted data; the need to install sensors in water wells, since it is impossible to guarantee the safety of elevated cabinets in a production environment. As a possible solution, we considered wireless technologies for transmitting small-volume data over long distances belonging to the class of LPWAN (Low-power wide-area network): NB-IoT (Narrow Band Internet of Things), LoRa (Long Range) and Strizh. The common advantages of the technology are high penetration and long range and low power consumption, which allows end devices to provide uninterrupted autonomous operation for up to 10 years without replacing the battery. Each technology also has additional advantages and limitations. The conclusion of the study is that to solve the problem in the given restrictions, you can use any of the technologies considered, while specifying the problem, you need to take into account additional parameters, such as the availability of network coverage in a given area, the number of sensors to be placed, the availability of the necessary devices, financial constraints.
Interesting Applications of Mobile Robotic Motion by using Control Algorithms

Robots are widely used in various fields of production.These complex technical devices are able to facilitate the work of man.Among the varieties of robots, it is necessary to highlight mobile robots.The ability to move expands the ability of robots to perform more complex tasks.The basis of mobile robots is their navigation system.Such a system consists of various algorithms that ensure the fulfillment of tasks.Among these algorithms, it is worth highlighting: robot control when avoiding obstacles, finding a directional path, general robot motion control.These issues are considered in this paper.
A Framework for Capturing and Analyzing Unstructured and Semi-Structured Data for a Knowledge Management System

Mainstream knowledge management researchers generally agree that knowledge extracted from unstructured data and semi-structured data has become imperative for organizational strategic decision making.In this research, we develop a framework that captures and analyses unstructured data using machine learning techniques and integrates knowledge and insight gained from the data into traditional knowledge management systems.Unlike most frameworks published in the literature that focuses on a specific type of unstructured data, our frameworks cut across the varieties of unstructured data ranging from textual data from social network sites, online forums, discussion boards, reviews to audio data, image data and video data.We highlight some preprocessing and processing techniques for these data and also highlight some standard output.We evaluate the framework by developing a textual data application programming interface (API) using python and beautiful soup and we perform sentiment analysis on the students' review data collected through the API.
Direct Least Absolute Deviation Fitting of Ellipses

Scattered data from edge detection usually involve undesired noise which seriously affects the accuracy of ellipse fitting. In order to alleviate this kind of degradation, a method of direct least absolute deviation ellipse fitting by minimizing the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math> algebraic distance is presented. Unlike the conventional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> estimators which tend to produce a satisfied performance on ideal and Gaussian noise data, while do a poor job for non-Gaussian outliers, the proposed method shows very competitive results for non-Gaussian noise. In addition, an efficient numerical algorithm based on the split Bregman iteration is developed to solve the resulting <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math> optimization problem, according to which the computational burden is significantly reduced. Furthermore, two classes of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> solutions are introduced as the initial guess, and the selection of algorithm parameters is studied in detail; thus, it does not suffer from the convergence issues due to poor initialization which is a common drawback existing in iterative-based approaches. Numerical experiments reveal that the proposed method is superior to its <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> counterpart and outperforms some of the state-of-the-art algorithms for both Gaussian and non-Gaussian artifacts.
Combination of Hill Cipher Algorithm and Caesar Cipher Algorithm for Exam Data Security

The progress of communication technology has had a positive impact on human life, including in the field of education. The education office is currently implementing computer-based exams starting from the State Higher Education Entrance Joint Selection (SBMPTN) exam to the School Final Examination. But with the implementation of computer-based exams this is of course the less secure the level, therefore the authors make this research with the aim of securing the exam data that will be tested with Hill cipher cryptography and Caesar cipher. Cryptography is a technique of hiding data that is done to secure data, in this case cryptography aims to secure data on exam questions.
Under which King, Bezonian? Literary Studies between Hermeneutics and Quantification

What is the relationship between the quantitative literary history of the past twenty years and the older hermeneutic tradition? Answers have typically been of two kinds: for many in the interpretive camp, the two approaches are incompatible, and the newer one has little or no critical value; for most quantitative researchers, they are instead perfectly compatible, and in fact complementary. Here, I will propose a thirdpossibility, that will emerge step by step from a comparison of how the two strategies work. How they work, literally; in the conviction that practices – what we learn to do by doing, by professional habit, and often without being fully aware of what we are doing – have frequently larger theoretical implications than theoretical statements themselves. In other words: understanding what a research paradigm does, ratherthan what it declares it wants to do. This is the plan.
Simple Face Thermal Features for Gender Discrimination

A very simple approach is proposed for gender discrimination using thermal infrared images of the persons' face.The selected features are actually based on the mean value of the pixels of specific locations on the face.It is proved that the discrimination is feasible either by simple visualization in the feature space or by using a relatively simple neural network for this purpose.All cases of persons from the used database, males and females, are correctly distinguished based on the mean value of the employed locations of the face.Classification results are verified using two conventional approaches, namely: a. the simplest possible neural network so that generalization is achieved along with successful discrimination between all persons and b. the leave-one-out approach to demonstrate the classification performance on unknown persons using the simplest classifiers possible.
Designing a Dashboard Visualization Tool for Urban Planners to Assess the Completeness of Streets

This paper presents a design study for a novel interactive web-based visualization tool that utilizes a “Complete Streets” model to support urban planners and engineers to design streets in urban areas more effectively. The proposed tool integrates a map and a dashboard view, where streets are analyzed and scored based on their overall completeness through six parameters of service; automobile, public transit, goods vehicles, environment, pedestrians and cyclists. In the map view, planners can assess streets based on their location, type and form and perform comparisons between multiple streets. In the dashboard view, planners can tweak a comprehensive set of instructions within each of the six parameters and view the effects of their changes in real time for both overall completeness and across parameters. Planners can also save versions to revisit and tweak them whenever necessary and may also download their dashboard data in different formats. We proposed that planners will be able to assess the completeness of existing streets in their current state and create multiple street prototypes exploring different scenarios and combinations virtually, instantaneously, minimizing costly pilots and prototypes. Future iterations will also promote collaboration and sharing across dashboards.
Computerization Method to classifying of Red Blood Cells using Boosting Technique

Red blood cells are the rounded and flexible, keep oxygenation to the human body.Along with red blood cells other abnormal cells like sickle cells, elongated cells and Echinocytes present in the body.In present microscopic image are containing touching and overlapping cells, which have to segment properly without any error then proposed automated methodology is established.To identify shape of blood cells and classify in different class according to shape of cells using image processing technique and machine learning approach.XG-boost is the classifier used for classification of the cells in multiclass.Firstly, performed binary classification of cells and multiclass classification of abnormal cells are executed.It is performed in both analysis single as well as multilevel analysis.So testing accuracy for Extreme Gradient Boosting 98%achieved.
Relationship between Teachers’ Demographic Characteristics and Levels of Utilization of Instructional Materials in Pre-Primary Schools in Kenya

Research indicates that children who obtain quality pre-primary education have a head start in life. It is also evident that early exposure and good use of instructional materials (IM) prepare children to be well adjusted later in life. Children are also well equipped with problem-solving skills that are important in Early Childhood Development and Education (ECDE). Instructional materials are core to effective teaching and learning in the formative years. The purpose of this research was to establish whether there was a significant relationship between teachers’ demographic characteristics and levels of utilization of instructional materials in pre-primary schools in Kenya. The study adopted the Cognitive Theory of Multimedia Learning Framework (CTML) and Ecological Systems Theory by Urie Bronfenbrenner. Besides, the study applied a cross-sectional survey research design. A random sample of 168 teachers comprising of 76.2% females and 23.8% males participated in the study. The Chi-Square test results indicated that gender (x2= 1.335; d.f. =1; p= 0.030), level of education (x2=8.165; d.f. =5; p=0.024) and professional training status (x2= 13.620; d.f. =5; p= 0.020) had a statistically significant linear relationship with the level of utilization of instructional materials. These results implied that female teachers were more likely to use instructional materials than their male counterparts and teachers who were more educated and highly trained were more likely to use instructional materials than their less trained counterparts. However, age, marital status, and duration of teaching experience had no statistically significant relationship with the utilization of instructional materials in pre-primary schools. It was concluded that professional training of pre-primary school teachers is critical for effective teaching and learning at the foundational level. It was recommended that teachers should promote the utilization of instructional materials in their classrooms for interactive and experiential learning of pupils.
Ontology Reasoning with Deep Neural Networks (Extended Abstract)

The ability to conduct logical reasoning is a fundamental aspect of intelligent human behavior, and thus an important problem along the way to human-level artificial intelligence. Traditionally, logic-based symbolic methods from the field of knowledge representation and reasoning have been used to equip agents with capabilities that resemble human logical reasoning qualities. More recently, however, there has been an increasing interest in using machine learning rather than logic-based symbolic formalisms to tackle these tasks. In this paper, we employ state-of-the-art methods for training deep neural networks to devise a novel model that is able to learn how to effectively perform logical reasoning in the form of basic ontology reasoning.