Reviewerly/exHarmony huggingface dataset:
Identification and Processing of Outliers in Data Based on Improved Quantile Method

At present, a variety of sensors were installed on various production equipments, and a large amount of data was collected by sensors during operation. Then, it is inevitable that there are a large number of outliers in the measured values. In view of the above situation, this article has studied common methods in data outlier filtering. Pauta Criterion, Chauvenet's Criterion and Quantile Method were analyzed. The shortcomings of these methods and the defects in the application were summarized, and according to the actual problem, Quantile Method has been improved in a targeted manner. The advantages and effectiveness of the proposed method were proved by simulation experiments. The experimental results shown that the improved Quantile Method has higher recognition accuracy of outliers than the other two methods, and the new method is suitable for data with large fluctuations. Compared with the other two methods, it has higher practical application value. After the outliers in the data were identified, the average method was used for further correction. The method proposed in this paper has fast calculation speed and high accuracy, can adapt to the data with large fluctuations, and can accurately correct the outliers according to the fluctuation trend of the data. In summary, the method is suitable for real-time filtering of data during data collection.
Prediction and Visualization of Flood Occurences in Tangerang using K-Medoids, DBScan, and X-Means Clustering Algorithms

The object of this research is the Tangerang area due to losses and damage due to floods in Tangerang in 2016, many people suffered due to flooding also many people were displaced. In the Tangerang area, there are no applications that can detect potential flooding and the geographical location of Tangerang is close to the Jakarta and Bogor areas. This research purpose will focus on observation, identification of research objects, collecting data, and conducting surveys. The visualization mapping of flood prediction in Tangerang City to display visualization information on river water levels in the Tangerang area by clustering using Power BI. This research objectives knowing the prediction of river water levels in the Tangerang area, including the Cisadane River, Angke River, and Pesanggrahan River using three clustering algorithms (K-Medoids, DBScan, X-Means).
An efficient hyper-graph based tie point matching method for aerial imagery

For the aerial images characterized by low contrast or containing repetitive patterns or homogeneous textures, robust tie point matching is still a challenging task. In this letter, an effective hypergraph-based tie point matching method is proposed. Firstly, feature points are divided into several point clusters in the overlapping area of images. Secondly, a two-stage matching pipeline, that is, candidate feature point matching and high-order graph matching, is performed. The candidate relationship is utilized to establish the high-order similarity tensor without any information loss. The experimental results show that our algorithm can significantly enhance the matching robustness and success rate compared with traditional tie point matching algorithm, and when compared with state-of-art hypergraph matching methods, our algorithm can obtain approximate equal matching success rate with a largely higher computational efficiency.
INTERACTIVE EDUCATIONAL TECHNOLOGIES IN THE TRAINING OF A FUTURE BIOLOGY TEACHER FOR SPECIALIZED SCHOOL

The relevance of the study is determined by the integration of Ukraine into the European educational system, reforms of secondary education, the transition to specialized education in high school, the growing requirements to vocational training of graduates of higher education institutions, the need to change from the traditional academic style of teaching disciplines to innovation.&#x0D; The aim is to generalize scientific, educational-methodical sources and personal practical experience of using interactive pedagogical techniques in forming the professional competence of the Biology teacher of the specialized school.&#x0D; Methods used in the research include theoretical (analysis of scientific and pedagogical sources, articles, abstracts, dissertations, Internet resources; synthesis and generalization of factual material; comparison and classification) and empirical methods (pedagogical observation, questioning, interviews with students).&#x0D; The results. The role of interactive techniques in teacher's training has been analyzed; it is noted that the organization of education on a competence approach is based on strengthening of practical professional orientation of education, development of students' pedagogical abilities and values. The author has implemented the preparation of the future Biology teacher of the specialized school at the second (master's) level of higher education while ... . Anonymous questionnaires of the master-graduates, the future teachers of Biology, Chemistry and the Fundamentals of Human Health have been conducted. The respondents have been asked to rate on a five-point scale the effectiveness of a method, a form, a teaching technique in forming their readiness to work at a specialized school. The analysis of the results of the study is carried out on the basis of ...&#x0D; Conclusions. Training the modern teacher of the specialized school should be carried out due to the strengthening of the practical component of educational programs. Special attention should be paid to on-the- job (pedagogical) practices in general secondary education institutions with specialized classes...
Design and Implementation of the News Practice Teaching Platform Based on JavaWeb

Journalism is a practical and applied subject. Besides the theoretical teaching in the university classrooms, practice teaching is also an organic part of it. Practical teaching of journalism basically organizes the teaching according to the professional teaching plans. Practical teaching links mainly include experiments, course practice and graduation practice. Experiments are mostly offered in combination with the theoretical courses, and the experimental courses are only conducted within the time specified in the teaching plans. Most of the experimental methods are taught by teachers first, and students complete the experimental operation according to the teacher’s arrangement or the steps in the instruction book. In this mode, although we emphasize the cultivation of the experimental ability, the experimental ability is regarded as a skill and imparted in the form of “knowledge”, which leads to the lack of the cultivation of students’ creative application abilities.
Role of Interaction Flow Modeling Language (IFML) in the Development of Ubiquitous Web Applications (UWAs)

During the revolution of webs, several modern web applications appeared. After developing electronic devices, Ubiquitous web applications (UWA) becomes an important part of web applications. UWAs are a new type of web applications which are accessed in various contexts, meaning through different devices, by users with various interests, at any time from any place around the globe. The problem is UWA features made challenge during the process development of web engineering methods. Interaction Flow Modeling Language (IFML) is the recent modeling language has many concepts for developing interactions in web applications compared with other web engineering methods. In this paper, we present the role of IFML method to develop UWA features through analyzing IFML and designing a case study. The result of this paper becomes a guide for developers and increase the usability of IFML.
Analysis of Diabetic Association Rules Based on Apriori Algorithms

In this paper, the association rule is elaborated on the basis of Apriori taking the diabetic patient’s disease record as a case. The core idea of association rule on the basis of Apriori algorithm for mining large item sets is discussed, furthermore the example shows the execution process of the algorithm.
Cryptanalysis of SP<sup>2</sup>DAS and 3PDA, Two Data Aggregation Schemes for Smart Grid

This paper analyses the security of two recently proposed privacy preserving data aggregation schemes, called SP2DAS and 3PDA. We show that, for both of these protocols, despite the designers' claims, there are efficient forgery attacks on the signature schemes used in. We present a selective forgery attack on the signature scheme of SP2DAS in the key-only attack model and a selective forgery attack on the 3PDA's signature scheme in the known-message attack model, requiring only two pairs of message-signature. Our results show that in both of these schemes, the attacker can inject any arbitrary faulty data into the data aggregated by the network, without being detected, which is a serious threat to the performance of the whole network.
Agile Scaled Steps of Doneness: A Standardized Procedure to Conceptualizing and Completing User Stories Across Scrum Teams and Industries

Agile software development (ASD) requires a shift in culture when compared to the traditional Waterfall software development. The traditional methods concentrate on project scope, using them to determine cost and time schedule. Agile concentrates on business values, using them to determine quality levels and possible technology constraints. Where waterfall methods are suitable for well-arranged and predictable environment. For an organization, one of the most important differences between agile and waterfall is the return of investment. Organizations are created to generate revenue and moreover profit for its stakeholders. Agile can help to produce earlier return on that investment. This enables an organization to get the maximum returns before their competitors start penetrating their market shares. Agile has scaling frameworks to assist organization transition to ASD. This paper aims to build on the original ENASE paper titled “Scaling A Standardized Procedure To Conceptualizing And Completing User Stories Across Scrum Teams And Industries” by extending the application of the Scaled Steps of Doneness procedure from four teams to six teams and analyze the results.
Management of Experimental Activity Concerning Moral Education of Preschoolers

The article describes the managing process in a Stavropol Krai preschool institution concerning the experimental activity of the pedagogues. The logic of experiment management is presented: training of in-service training of the pedagogues, their motivation and information awareness improvement. The author considers certain positive results of the innovative pedagogical experimentation and outlines prospects for development.
PVO-Based Multiple Message Segment Reversible Data Hiding

In this article, a reversible data hiding technique is proposed to embed multiple segments of a single message into a single cover image. This multiple message segment technique uses a pixel value ordering approach to embed the secret message. The splitting and randomization of the original secret message provides security from an attacker There are many digital formats for data hiding, like images, audio, and video, of which the digital image is the simplest format. Data hiding in image processing refers to inserting the secret message into digital images. Reversible data hiding (RDH) is a lossless technique, in which both the embedded secret message and the cover image is extracted by the receiver. The applications of RDH include medical and military imaging.
Testing of Owner Estimate Cost Model with Android-based Application

This research continues previous research by testing the owner estimate cost (OEC) model in Android-based application development projects. Previously, the model of OEC had been tested with nine general software project data which produced acceptable accuracy. In this study, the same model was tested with 5 android-based software projects to find out the consistency of the model with test data that was different from before. To test the accuracy of the results using the magnitude of relative error (MRE) for each software's owner estimate cost and mean of MRE (MMRE). The test results with 5 android-based applications show that this model is also consistent and has an acceptable level of accuracy.
MCFS: Min-cut-based feature-selection

In this paper, MCFS (Min-Cut-based feature-selection) is presented, which is a feature-selection algorithm based on the representation of the features in a dataset by means of a directed graph. The main contribution of our work is to show the usefulness of a general graph-processing technique in the feature-selection problem for classification datasets. The vertices of the graphs used herein are the features together with two special-purpose vertices (one of which denotes high correlation to the feature class of the dataset, and the other denotes a low correlation to the feature class). The edges are functions of the correlations among the features and also between the features and the classes. A classic max-flow min-cut algorithm is applied to this graph. The cut returned by this algorithm provides the selected features. We have compared the results of our proposal with well-known feature-selection techniques. Our algorithm obtains results statistically similar to those achieved by the other techniques in terms of number of features selected, while additionally significantly improving the accuracy.
Designing of Postgraduate Medical English Class in Smart Learning Environment

Based on the smart education environment, this paper employs the combination between the formative evaluation and the summative evaluation in order to conduct a teaching experiment in the second semester of the first academic year for postgraduates, which aims to promote the teaching quality of graduate medical English through various learning resources in the context of smart education.
Hyper-parameter Determination of CNN Classifier for Head Pose Estimation of Three Dimensional Degraded Face Images

This paper presents the evaluation of parameters for head pose estimation using Convolutional Neural Network (CNN) towards the degraded images. Head pose estimation is one of the important factor for three dimensional face recognition system. Due to its superiority, Convolutional Neural Network (CNN) has been used as a head pose estimator, however, its performance is significantly dropped when the input face images is exposed to noises. As the CNN comes with different choices of pooling layer, two different experimental setups are created with similar architecture and training condition but using a different type of pooling layer. After learning, the CNN are tested with another five different testing datasets to monitor the effects of various particular noises, such as: Gaussian noise, Salt-Pepper, and Speckle. Result of the experiments shows that the usage of max pooling significantly lowering the performance of the CNN, compared to the system with average pooling layer.
Non Relativistic Collisions

In this chapter we shall study both elastic and inelastic frontal (non-relativistic) collisions of point-like particles. We shall also introduce the notion of particle scattering by a hard (impenetrable sphere) and by a repulsive potential. As already usual in this book, the most generic aspects will be presented, and the reader will be invited to deduce additional theoretical aspects and apply them to some practical examples.
Multi-robot On-line Sampling Scheduler for Persistent Monitoring

The employment of autonomous agents for persistent monitoring tasks has significantly increased in recent years. In this sense, the data collection process must take into account limited resources, such as time and energy, whilst acquiring a sufficient amount of data to generate accurate models of underlying phenomena. Many different schedulers in the literature act in an off-line manner, which means they define the sequence of visit and generate a set of paths before any observations are made. However, on-line approaches can adapt their behavior based on previously collected data, allowing to obtain more precise models. In this paper, we propose an on-line scheduler which evaluates the sampling rate of the signals being measured to assign different priorities to different Points-of-Interest (PoIs). Next, according to this priority, it is determined if a region must be visited more or less frequently to increase our knowledge of the phenomenon. Our methodology was evaluated through several experiments in a simulated environment.
Learning-Based Video Game Development in MLP@UoM: An Overview

In general, video games not only prevail in entertainment but also have become an alternative methodology for knowledge learning, skill acquisition and assistance for medical treatment as well as health care in education, vocational/military training and medicine. On the other hand, video games also provide an ideal test bed for AI researches. To a large extent, however, video game development is still a laborious yet costly process, and there are many technical challenges ranging from game generation to intelligent agent creation. Unlike traditional methodologies, in Machine Learning and Perception Lab at the University of Manchester (MLP@UoM), we advocate applying machine learning to different tasks in video game development to address several challenges systematically. In this paper, we overview the main progress made in MLP@UoM recently and have an outlook on the future research directions in learning-based video game development arising from our works.
The Evaluation Algorithms of the Computer Aided Instruction System Based on the New Media

The traditional CAI system runs in a single computer environment. It lacks convenience in updating knowledge. The teaching arrangement is formulated beforehand, which cannot fully meet the different requirements of different students. The means of checking students’ learning effects are also imperfect. The evaluation and test paper generation algorithm of the new media CAI system has always been a research hotspot in CAI. In view of the shortcomings of the current algorithms for generating test papers, a two-way automatic algorithm for generating test papers is implemented. The algorithm has the advantages of fast speed, high success rate and good avoidance of duplication of continuous test papers.
Word Recognition for Color Classification Using Convolutional Neural Network

Word recognition using deep learning is a simple approach to speech recognition in general. This work using color classes for the data set, to distinguish several colors using word spoken by human speakers. This research conducted by using convolutional neural network for deep learning classifier and MFCC as the feature extraction method from the audio data. This research yields good results, approximately about 98% for training accuracy and testing accuracy.
An Intelligent Resource Selection Framework for Cloud Computing Environments

Cloud computing provides computing resources as utility services. It offers clients with several usage scenarios. These scenarios fulfill the services requested by the clients with a lower cost as compared with deploying them locally. This paper introduces an intelligent resource selection framework that provides the client with the best set of resources in terms of computing cost and the fulfillment of client's requests. It is assumed that the requested resources can be provided to the client from multiple data centers. Additionally, cloud resources are allocated in units determined according to client's request. The client submits his resource requirements in terms of units of computing power, storage, networking, software ... etc. Then, the proposed framework finds the best set of resources that satisfies the client's request. Moreover, it takes into consideration the inter-dependencies among resources that lead to mutually allocating certain resources together. Additionally, it considers the overhead incurred in acquiring and using the requested resources. This problem is formulated as a constrained optimization problem and has been tackled in this paper using genetic algorithms. Simulation studies show that the proposed framework provides a near-optimal solution for the studied cases.
Relation chaining in binary positive-only recommender systems

Recommender systems typically work on user-item preference relation data. Recommendations can be improved by including side relations that are indirectly linked to the target relation. Data fusion by matrix co-factorization is one such method that can integrate heterogeneous representations of objects of different types. A shared latent matrix factor model is inferred, which is then used to approximate and thus predict multiple data matrices at the same time. The factor model can also be used to infer indirect relations among objects, by multiplying the corresponding factor matrices on a chain of relations that link those objects (i.e., relation chaining). We show that recommendation in binary positive-only data can be improved by relation chaining. We can filter out less reliable relations by using the number of supporting intermediate paths as an additional parameter in a multi-objective Pareto optimization. Our method outperforms other state-of-the-art chaining methods. To speed-up the computation of relation chaining, we propose a chain matrix multiplication-based approach for chaining. To evaluate our method, we have created synthetic data on transitive relations among objects, for a varying degree of noise. Results on synthetic data show that chaining indeed works on chains containing transitive relations. Results on three real datasets show that the inclusion of the number of intermediate paths improves relation chaining predictions. Compared to the full data matrix multiplication approach, the proposed relation chaining method achieved two-fold speed-up.
The Affordances of Augmented Reality in Delivering the Science Curriculum to Elementary Grades

The current science books for Palestinian elementary schools essentially consist of picture-based activities. Each lesson features images which students are asked to reflect on, and provide oral and written answers to questions linked to each image. This rich curriculum is being delivered in traditional methods, and few teachers have to date ventured into utilizing modern technologies that can be easily accessible. The present research effort measures the affordances of augmented reality in delivering the science curriculum to elementary grades in the West Bank. An interaction analysis study was used, in which activities from the third grade science textbook were demonstrated using AR technologies to identify their role in enhancing learners’ interaction with pictures in the science school books. A sample of 50 third grade female students, from a West Bank’s basic school for girls, was drawn from the study population (all third grade students in the West Bank). The 50 students were divided evenly into homogeneous control and intervention groups. Seven activities were delivered conventionally for the former and with augmented reality technologies for the latter. The findings suggest that students taught with augmented reality-enhanced procedures were particularly engaged and effectively responsive, both orally and in written tasks.