Reviewerly/exHarmony huggingface dataset:
Evaluation of the influence of the properties of inhomogeneous media in the formation of the image in technical vision systems

A method for estimating light scattering for various types of inhomogeneous media is presented. The models of propagation and scattering of optical radiation in randomly inhomogeneous media are analyzed. An estimate is made of the applicability limits of methods for studying inhomogeneous media for various scattering models. The values characterizing the optical properties of inhomogeneous media are determined.
PENINGKATAN MOTIVASI BELAJAR TEMA 1 INDAHNYA KEBERSAMAAN MELALUI MODEL PEMBELAJARAN GROUP INVESTIGATION PADA SISWA KELAS 4 SDN 1 SUKOREJO

This study aims to increase the motivation to learn theme 1 through the application of the Group Investigation learning model for 4th grade students of SDN 1 Sukorejo. This research is a Classroom Action Research (CAR) carried out in two cycles consisting of 4 stages, namely the stages of planning, implementation, observation, and reflection. The subjects of this study were students in grade 4 at SDN 1 Sukorejo, totaling 30 students. The data collection technique used was non-test (observation, documentation) and then analyzed descriptively quantitatively. The results showed an increase in the average percentage of learning motivation from the first cycle totaling 7 students (23.33%) included in the classification highly motivated, 11 students (36.67%) entered the classification quite motivated while 12 students (40%) entered in classification is not motivated and cycle II as many as 26 students (86.67%) enter the classification highly motivated and 4 students (13.33%) in the classification are quite motivated. Based on the results of the study it can be concluded that the Group Investigation learning model can increase motivation to learn theme 1 grade 4 SDN 1 Sukorejo.
An Evaluation of Parameter Pruning Approaches for Software Estimation

Model-based estimation often uses impact factors and historical data to predict the effort of new projects. Estimation accuracy of this approach is highly dependent on how well impact factors are selected. This paper comparatively assesses six methods for prune parameters of effort estimation models, including Stepwise regression, Lasso, constrained regression, GRASP, Tabu search, and PCA. Four data sets were used for evaluation, showing that estimation accuracy varies among the methods but no method consistently outperforms the rest. Stepwise regression prunes estimation model parameters the most while it does not sacrifice much estimation performance. Our study provides further evidence to support the use of Stepwise regression for selecting factors in effort estimation.
Convolutional Equalizer - A Convolutional Approach to Equalize Input Features in Dimension

The dimension-equalized inputs are commonly required for tasks of machine learning but, due to certain physical constraints in acquisition of the source data, it might not be fortunate to have always the inputs equal in dimensions. Therefore an input-data equalizer is proposed accordingly in this study. Based on the equalized inputs, kinds of similarity measurement are easily applied in order to compute the loss which is a key to drive the learning machine to converge into an optimal model with the fashion of gradient descent. In this paper, input sequences, which are originally uneven in length, could be discretely resampled to be length-equalized with less discount of their inherit information through a convolutional transformation operation only and for this purpose a discrete convolution has been employed to the proposed equalizer. The equalization has been completed in one shot with a simple complementary calculation to count and regenerate the data points. Rational details of the operation with elaborated instances are included in this paper. The significance of this work is that unlike the other approaches, which are frequently used in the applications, the sequences resampled from the proposed approach displayed a robust tendency which is really advantageous to the consecutive machine learning procedure. The Hotelling's T2-test at 95% two-tailed confidence level showed no discrimination of the data before and after the equalization with a p-value 4×10-6.
New possibilities of teaching physics based on secondary school students’ projects

On the basis of the development in education, where the necessity of interconnection of teaching with practice or higher education institutions is increasingly emphasized, the subjects Student Project and High School Graduation Project were introduced within the framework of education at SSPS, Prague (Smíchov Secondary Industrial School). These courses lead students to work independently on a given topic with the support of a teacher, a university teacher or an expert from the industry. That is why the SSPS established close cooperation with the CTU FEL (Faculty of Electrical Engineering of the Czech Technical University in Prague), which led to the creation of several new teaching tools and aids in Physics. In this article we describe some examples of projects implemented at SSPS and will also pay attention to their contribution to development and improvement of physics laboratories at SSPS.
Research and Development of Network Security and Risk Management

Abstract With the advent of the 21st century, China is in a period of social transformation and is in the stage of deepening reform. Social contradictions are complex, hot events are frequent, and public opinion is active. Under the conditions of the Internet, especially in the context of the prosperous media, the public opinion pattern has undergone new changes. With the popularity of the Internet and the increasing number of Internet users, the Internet has become a large social place. After a hot event occurs, it often causes heated discussion, and overheated public opinion or embarrassing grievances will also form corresponding events. Improper handling often results in “secondary disasters”. In recent years, the concept of lyricism has been frequently used, and public opinion analysis has been promoted at the same time in both theoretical research and practical application. The related network risk management has also received increasing attention. Various situations have shown that lyric research is heating up. Based on this status quo, this paper sorts out and comments, summarizes the research status at home and abroad, and focuses on the rational situation of the situation of public opinion research.
Designing Modes in Disordered Photonic Structures

In this chapter, we introduce the inverse design of disordered photonicPhotonics structures with the target modal response. Starting from the concept of metadisorderMetadisorder , which is defined by orderOrder -random interactions in the multivariable parameter space, we discuss the realization of anomalous wave transport and localization in photonicPhotonics structures as an example of the independent control of wave quantities. As a generalization of this approach, we introduce the concept of Bohmian photonicsBohmian photonics inspired by the alternative interpretation of quantum mechanics, which enables independent control of the amplitude and phase information of light. Various practical applications of Bohmian photonicsBohmian photonics , including optical phase trapping, phase-conserved energy confinement and annihilation, and constant-intensityConstant-Intensity (CI) wave wave transport, are discussed.
THE COPING STRATEGIES APPLIED TOWARDS THE UNDESIRABLE BEHAVIORS OF EFL LEARNERS

The purpose of this study is to find out the strategies that EFL instructors apply to cope with undesirable behaviors of language learners at the tertiary level.The study was conducted with 20 EFL instructors working in the preparatory school at a university in 2017-2018 academic years.The data was gathered through a survey including 10 open-ended questions.Content analysis technique was used to assess the data.The results of the study reveal that EFL instructors apply 12 coping strategies towards the undesirable behaviors of language learners at tertiary level.The instructors use body language, verbal and facial expressions to warn their students.In addition, keeping silent, asking a question, finding out the reason of the undesirable behavior and the needs of the disruptive students, raising awareness, increasing motivation, talking in person, attracting attention, and ignoring the undesirable behavior are the other coping strategies.
Features Identification and Classification of Alphabet ر (ro) in Leaning (Al-Inhiraf) and Repetition (Al-Takrir) Characteristics

It is important for Muslim to recite the Quran properly with the correct Tajweed. which includes the use of correct characteristics (sifaat) and point of articulations (makhraj). To this date, there are limited researches done focusing on classifying the Quranic letters according to the characteristics. In this study, the focus is given to the classification of the characteristics of the Quranic letters for the purpose of developing an automated self-learning system for supporting the conventional method of Quranic teaching and learning. The characteristics of Quranic letters, which are the focus in this paper are Leaning and Repeating, where both consists of ? (ro) alphabet. Several methods of feature extractions and analysis were implemented such as Formant Analysis, Power Spectral Density (PSD), and Mel Frequency Cepstral Coefficient (MFCC) to come out with the suitable features that best represent the correct characteristics of the alphabet. Once the features had been identified, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) were used as the classifier. The results show that QDA with all 19 features trained achieved the highest percentage accuracy for both Leaning ( Al-Inhiraf) and Repetition (Al-Takrir) characteristics with of 82.1% and 95.8% of accuracy respectively.
The Hybrid Intelligent Information System for Poems Generation

Any generated text must have a “form” and “content” components. It is the “content” that is the main component of the generated text, but the “form” component is no less important. It may be necessary to generate texts in different linguistic styles among them the poetic linguistic style. The article proposes an approach for poems generation problem using hybrid intelligent information systems (HIIS). The HIIS consists of two main components: the subconsciousness module and the consciousness module. In the case of poems generation, the subconsciousness module consists of two submodules: the stress placement module and the rhyme and rhythm module. These modules use machine learning techniques. The consciousness module includes the poem synthesis module, which is rule-based. The stress placement module is based on the convolutional neural network. On the test dataset, the accuracy of the classifier is 97.66%. The rhyme and rhythm module based on neural networks with a depth of 5–7 layers. On the test dataset, the accuracy of the classifier is 91.63%.
Soma-Based Nonphysical Instrument Design in Electronic Music Performance

Abstract Attention to the role of the body and bodily awareness in human-computer interaction is increasing. Broader availability of cost-effective motion sensors in mobile and gaming applications has prompted a shift to body-centered design methods. This article examines the relevance of embodied sketching activities drawn from soma-based and sonic interaction design to digital musical instrument (DMI) development. It focuses specifically on the Telechord, a novel motion-controlled system that promotes methods for exploring connections between movement and sound. By emphasizing the felt aspects of movement-based design and performance, this approach places performer experience at the forefront, complementing technical efforts to enhance nuance and coherence in current DMI design research.
Linear algebra approach to phase shifting interferometry: numerical methods

We model a set of Phase-Shifting Interferogram (PSI) images in a new and versatile way that allows exploring interesting numerical approaches for the analysis of PSI. We show how this representation can be used to recover the modulating phase if the phase steps are known, and we also show how it can be used as the baseline for an iterative algorithm. For the case in which the phase steps are known we compare against the four step algorithm. For the case of unknown phase steps, we compare against the Principal Component Analysis (PCA) and the Advanced Iterative Algorithm (AIA).
Recognition of Antisocial Behavior in Online Discussions

The paper is focused on recognition of antisocial behavior in social media. User generated content in the form of discussions is essential to the success of many online platforms. While most users tend to civil accepting the social norms, others engage in antisocial behavior negatively affecting the rest of the community and its goals. Such behavior includes harassment, bullying, flaming, trolling, etc. The contribution is focused on classification of troll posts in online discussions to distinguish them from creditable posts. The work proposes a machine learning approach to build the classification model for toxic posts identification on an extensive dataset. The following machine learning methods were used: k Nearest Neighbors, Naïve Bayes Classifier, Decision Trees, Logistic regression and Support Vector Machine. These machine learning methods were used in combination with three different feature representations of texts of online discussions as a binary vector, a bag of words and the TF-IDF weighting scheme. The paper contains also the results of experiments with all learned models for toxic posts recognition.
Bayesian Confirmation and Justifications

We introduce a family of probabilistic justification logics that feature Bayesian confirmations. Our logics include new justification terms representing evidence that make a proposition firm in the sense of making it more probable. We present syntax and semantics of our logic and establish soundness and strong completeness. Moreover, we show how to formalize in our logic the screening-off condition for transitivity of Bayesian confirmations.
Determining Emotional Profile Based on Microblogging Analysis

In general, groups of people are formed because of the similarities and affinities that members have with each other. Musical preferences, soccer teams or even similar behaviours are examples of similarities and affinities that motivate group formation. In social media, identifying these affinities is a difficult task because personal information is not easily identified. In this paper we present an alternative to identifying similarities between authors and their most frequent audience in Twitter, using emotional and grammatical writing style analysis. Through this study it is possible to define the creation of an emotional profile entirely based on the interactions of people, thus allowing software like chatbots to “learn emotions” and provide emotionally acceptable responses.
Optimization of Network Security Using Website Filtering With Microtic Routerboard

The need for internet availability today is very important and greatly improved, people are now so dependent on the role of information technology that it has a very important impact. For that purpose there must be a good and efficient internet service provider system that should be economical. The Internet has had a huge effect on the dissemination of information, so that more and more people are accessing data over the Internet. These problems can be solved using MikroTik as a regulator of Internet data bandwidth and filtering sites that can interfere with computer network connectivity and interfere with job performance in accordance with established rules. This research was conducted using several steps, among others: process analysis to determine the traffic flow through the filtering process using Layer 7 Protocols, design to get the most effective and efficient way to implement router, implementation and testing by direct test to the web page or website with web browser. Based on research that has been done using MikroTik Routerboard whose result can fulfill system requirement specially in doing application filtering according to user requirement.
Qualitative Research of the Vocational Education for General High School Students Focusing on the Polytechnic School

This study was aimed at suggesting meaningful implications for enhancing the effectiveness of vocational education for general high school students through in-depth interviews with teachers, students, and officials of the metropolitan &amp; provincial office of education. From the in-depth interviews, the following conclusions were drawn with regard to the improvement of vocational education for the general high school students: First. The aim of the vocational education for general high school students should be clarified. Second, the promotion and providing information regarding the vocational education needs to be done much earlier than in the end of the second year. Third, in vocational education, it is necessary to open curriculum that reflects future industry rather than popular-oriented curriculum. Although it is important to study specialized subjects, practical studies of math and English is also important. Fourth, Extra-curricular activities for third-year students involved in the vocational education need to be improved. Fifth, training needs to be carried out to improve the awareness of general high school teachers on the vocational education.
A new framework for subimage analysis

We develop new methods for several subimage analysis problems. Images in two dimensions are represented by matrixes (higher dimensional images are represented by higher dimensional arrays). This representation yields efficient algorithms for image search and comparison problems applicable not only to images, but also to array search and comparison in any dimension two or larger. Subimage analysis based on our array-representation has limitations. For example, our methods are not rotation and scale invariant. However, they yield efficient algorithms that have practical applications. Videos and snapshots taken from videos are continuously added to the digital world where they are permanently accessible. From a snapshot, one may want to locate and watch the part of the video that contains this snapshot (e.g. moments in news-footage, soccer or olympic games). One may also want to find common parts (sequences of frames) in multiple videos for various reasons (e.g. copyright-check, false-news detection). Our algorithms are applicable in these cases. For another application, we propose representing RNA secondary structures by matrixes. Since our methods are applicable to submatrix analysis, RNA substructure search and multiple RNA structure comparison problems can be solved by using our algorithms (for exact matches).
A methodology for designing natural language interfaces for procedural content generation

Procedural Content Generation (PCG) uses algorithmic techniques to create a wide variety of content for games. These generators often have a large number of parameters, making it difficult for non-technical designers to explore the design space of generated artifacts. Natural language interfaces for generators can map natural language keywords to parameter space changes spanning multiple simultaneous parameters and afford use of expressive language. This way, designers can navigate to interesting points in the design space of a generator by describing desired properties of the artifact using a series of natural language descriptors. We present a design methodology that designers can use to develop natural language interfaces for procedural content generation systems. This design methodology begins by defining a design vocabulary that can describe the output of a generator, mapping the vocabulary to a series of parameters, and translating natural language queries to movements in the generator's design space. We further address issues around designer intent understanding, design space exploration and workflows using natural language interfaces in PCG. An example and implementation of our methodology is provided demonstrating its application to existing plug-ins for content creation in the Unity3D engine
The Use of Multiple MP3 Audio Files for Additional Steganography Capacity

Abstract Steganography is the science and art of hiding secret messages in such a way so that the existence of messages is undetected by human senses. In this paper, multiple MP3 files are used as cover with the aim of increasing steganographic capacity without limiting the number of used files. Concealing secret messages in the form of files on the MP3 file cover can be performed by using homogenous frame. Homogeneous frames in the MP3 files store the bits of the inserted secret files. Before being inserted into some MP3 cover files, secret files are first encrypted employing AES algorithm with passwords implicated by MD5 processes. Audio quality changes between steganography MP3 files and original MP3 files are measured by using stereo signal strength and Mean Opinion Score (MOS). The test results show that the distribution of steganography load on multiple MP3 files can fulfill the user’s needs for the capacity of steganography without reducing the quality of used MP3 files. This paper proposed a method about the increase of steganography capacity which is capable to insert and retrieve secret files on multiple MP3 audio files using homogenous frame utilization methods without resizing and reducing the quality of used MP3 files.
A Fraudulent Data Simulation Method Based on Generative Adversarial Networks

Abstract In view of the positive and negative sample imbalance in the process of establishing anti-fraud rules and models of the third-party payment, this paper simulated the fraudulent transactions by means of generative adversarial networks. In the design of the model, the output of the discriminator is optimized where classification results are set to three categories; the loss function of the GAN model is changed to include two parts: the source loss function and the category loss function. The generated data and the real business data are mixed according to a certain ratio, to train a fraud detection model. Verify the generation effect of this simulation method by comparing the detection effects of different detection models.