Reviewerly/exHarmony huggingface dataset:
The influence of animal name introduction learning application in English for Kindergarten on Android phones

Abstract Preschool or Kindergarten education is a formal of education for children between the ages of 4-6 years. The main task as a preschool educational institution is to prepare children by introducing knowledge, attitudes, behavior, skills and intellectuals so they can adapt to real learning activities in elementary schools. At present most of the learning in schools is still used teaching methods using media guidebooks, as well as learning carried out education of preschools. In preschool children learning there are basic material with animal themes such as the introduction of the names of animals in the vicinity in accordance with basic competencies to know the types and names of animals. Whereas at present most of the learning in schools is still used teaching methods using guidebook media. Adobe flash or is a multifunctional software. Apart from its initial function, which made it easy to create web animations, it turned out that Flash was developing so rapidly that it could be utilized as an extraordinary multi-media software. Adobe Flash be utilized as an effective game creation program. It is hoped that this research children’s educational game can be useful for children in Indonesia of preschool age.
Curriculum spaces and mathematical models for curriculum design

The area of mathematical psychology concerning knowledge spaces is complemented by the theoretical notion of a “curriculum space,” which is developed as a mathematical model for curriculum design in order to illuminate the interplay between learners and the curricula in which they enroll. The analysis is carried out in terms of concepts from graph theory and order theory, yielding a precise language in which to communicate aspects of curricular influences on student-achievement. Emphasis is placed on the structure of tasks that are addressed in a curriculum, the structure of prerequisite-course assignments within the curriculum, and the compatibility between these structures. Among the main results, conditions are given under which the tasks in a curriculum induce a partially ordered set, curricula that admit “compatible” prerequisite-structures are completely characterized, and a greedy algorithm is presented for categorizing courses into “curriculum maps” that respect the compatibility condition.
Deep Learning-Based Resolution Prediction of Software Enhancement Reports

The automatic resolution prediction of newly submitted enhancement reports is an important task during the bug triage process. It can help developers automatically predict the resolution status of enhancement reports. The resolution prediction is still a manual process which is very time-consuming, costly, and laborious. To help software applications for the timely implementation of enhancement reports, we introduce a deep learning-based technique to predict the resolution of newly submitted enhancement reports automatically by using a summary and description of enhancement reports. We use Word2Vec and a deep-learning-based classifier that can learn the deep syntactical and semantical relationship between the words of enhancement reports. We use additional novel features from enhancement reports and customized tokenizer to save useful features. Experimental results show the proposed approach enhances the performance as compared to state-of-the-art approaches in resolution prediction and has an effective ability to predict the resolution status of enhancement reports.
Improving Participation and Learning of Compiler Theory Using Educational Simulators

Due to the fact that compiler theory is pretty abstract, students' interest in learning the topics of this course decreases. In order to better prepare students for this challenge, a new approach to teaching and practicing compilers is necessary. The new approach should be complementary to existing curricula. This paper presents two educational interactive tools for visual representation of lexical and syntax analysis. It describes the way of applying simulation tools in order to improve the teaching process of the compiler construction course. These software systems evaluations were performed by checking the correctness of the implemented functionalities and examining the user experience by a quantitative student survey.
Combining Meta-path Instances into Layer-Wise Graphs for Recommendation

In the recommendation area, the concept of meta-path is famous for inferring explicit and effective relationships between nodes such as users and items. To extract useful information from the instances of meta-paths, existing methods embed meta-path instances separately. However, they ignore the complicated semantics presented by multiple instances. These complicated semantics not only provide additional information but also affect the semantics of single instances. Without considering the complicated semantics, the information extracted from the instances may be incomplete and less effective. To solve the problem, we propose to learn the complicated semantics by combining meta-path instances into layer-wise graphs (instance-graphs) for recommendation. Following the idea, we develop an Instance-Graph based Recommendation method (IGR). IGR combines meta-path instances into layer-wise instance-graphs. Then, the instance-graphs are investigated layer by layer to generate effective embeddings. Finally, these embeddings are discriminatively merged into user/item embeddings to make predictions. Extensive experimental results show that IGR outperforms various state-of-the-arts recommendation methods.
Edge Differential Privacy for Algebraic Connectivity of Graphs

Graphs are the dominant formalism for modeling multi-agent systems. The algebraic connectivity of a graph is particularly important because it provides the convergence rates of consensus algorithms that underlie many multi-agent control and optimization techniques. However, sharing the value of algebraic connectivity can inadvertently reveal sensitive information about the topology of a graph, such as connections in social networks. Therefore, in this work we present a method to release a graph’s algebraic connectivity under a graph-theoretic form of differential privacy, called edge differential privacy. Edge differential privacy obfuscates differences among graphs’ edge sets and thus conceals the absence or presence of sensitive connections therein. We provide privacy with bounded Laplace noise, which improves accuracy relative to conventional unbounded noise. The private algebraic connectivity values are analytically shown to provide accurate estimates of consensus convergence rates, as well as accurate bounds on the diameter of a graph and the mean distance between its nodes. Simulation results confirm the utility of private algebraic connectivity in these contexts.
Heritage language maintenance through digital tools in young Macedonian children - an exploratory study

Heritage language maintenance can be very challenging for diaspora communities worldwide. The role of technology on heritage language maintenance has recently started to spark the interest of researchers as a new emerging field but it still needs a lot of exploration. This exploratory study aims to provide an insight into Macedonian diaspora parents’ experiences with digital tools and heritage language maintenance for younger children using a phenomenological approach. Data collected from 23 participants examined the parents’ experience as members of these communities through surveys and follow-up interviews. Results reveal a strong interest in language maintenance, along with challenges such as lack of time and exposure and lack of digital resources for heritage language learning. Participants show a primary interest in using educational apps, emphasizing the need for interactional and engaging resources. Based on these findings, we provide recommendations for developing technology-mediated heritage language maintenance tools.
An Improved Approach for QoS Based Web Services Selection Using Clustering

With the rising number of web services created to build complex business processes, selecting the appropriate web service from a large number of web services respond to the same client request with the same functionality are developed independently but with different quality of service (QoS) attributes.From this point, there are many approaches to web service selection.Nevertheless, this is still deficient due to a considerable number of discovered web services.The prefiltering is a solution to reduce the number of web services candidates.In this paper, the K-means clustering is applied to determine similar services based on QoS information.The results of this prefiltering are considered at the selection task using the Branch and Bound Skyline (BBS) algorithm.The experimental evaluation performed on real Dataset proves that our approach presents efficient results for web service selection.
Path-Based Sensors: Paths as Sensors, Bayesian Updates, and Shannon Information Gathering

Consider a sensor that reports whether or not an event has occurred <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">somewhere</i> along a path, but that has no conception of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">where</i> along the path that event has occurred. We name this type of sensor a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path-based sensor</i> and describe the recursive Bayesian update that can be used to calculate posterior beliefs about the presence of a sensor triggering phenomenon given a path-based sensor observation. We show how the Bayesian update can be leveraged to calculate the expected Shannon information that will be gained along a particular path. We formalize two iterative information-gathering problems that result from this scenario and present path-planning algorithms to solve them. These include: 1) gathering information about the path-based sensor triggering phenomena and 2) assuming the path-based sensor triggering event is “robot destruction,” simultaneously gather information about: 1) hazards using a path-based sensor and 2) information about another environmental phenomenon using a standard sensor, such as the locations of search and rescue targets with a camera. We evaluate our methods using Monte Carlo simulations and observe that they outperform other techniques with respect to the new problems that we consider. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the problem of searching for robot-destroying hazards that are otherwise invisible to the robots. That is, we can observe whether or not a robot survives a path, but, if a robot is destroyed, then we have no idea where, along the path, its destruction has occurred. A mathematically equivalent problem happens in any scenario, in which an agent is equipped with an event sensor that can only be set/triggered once, but that requires postprocessing to figure out if the sensor has been triggered or not. For example, postprocessing is needed if the determination of whether or not a biological specimen was obtained requires a manual laboratory inspection. We also consider an extension of the hazard detection problem, in which we simultaneously collect information about search-and-rescue victims using a “victim sensor,” such as a camera. In this problem, hazards indirectly affect information gathered about victims because new information about victims is lost whenever a robot is destroyed. We provide algorithms to solve these types of problems. The algorithms work even in cases with noise such that false positives and false negatives are possible. This work is useful in any application where observations take the form of a cumulative “yes” or “no” along a path.
THE ELECTRONIC NOTE-TAKING IN INSTITUTIONS OF HIGHER EDUCATION OF RUSSIA

Based on the latest research, scientific publications and publicly available on official websites of universities teaching materials in the article the analysis of the current practice of foreign and Russian higher schools the two directions use note-taking in the educational process - preparation of abstract classes by the teacher and note-taking teaching materials by students. The article considers the methods of taking notes used in Russia and abroad and substantiates the advantages of the emerging practice of electronic taking notes. The author justifies the absence of the term "excerpt" characteristic of German-language scientific literature in the scientific circulation of Russian researchers and suggests introducing a new term - "electronic abstract", developing the characteristics and types of this concept in the article. Based on the author's many years of personal experience in preparing lecture notes, organizing electronic notes of their lectures and other educational material by students of a number of universities in Krasnodar, the article classifies electronic notes, describes the parameters of electronic notes, describes the technical techniques of taking notes, and ways to save notes. The article presents the results of a comparative analysis of various software processes for creating electronic notes of various types by students and teachers of Russian universities.
Accounting Fraud Detection Using K-Means Clustering Technique

Background: Accounting fraud has become quite prevalent in current times. This kind of frauds or scandals comprise any act of manipulation of financial statements as a means for hiding financial misdeeds. Such kind of fraudulent practices can be brought to the fore with data mining techniques. One can recognize patterns for fraud in an accounting data set with data mining. Methodology: One such data mining technique is K-means clustering. This divides the data set into k distinct non-overlapping partitioned clusters. Though a number of statistical models have been used at length for revealing patterns in such scams, they have been found to be complicated and highly time-consuming. In such scenario, K-means may form the simplest preliminary basis of fraud detection by segregating the data set into fraudulent and non-fraudulent sets which may be further followed by more complex practices for establishing the same. This work has been carried out on an accounting data set, and the factors considered are bank statements (credit and debit in the cashbook), asset value and net profit, on which K-means clustering has been simulated. Conclusion: This work is an effort toward showing a simple application of K-means. The experimental results indicate high accuracy and significant relation between misrepresented values of these factors and fraud.
Analyzing network traffic for protocol identification: an ensemble online active learning method

Analyzing network traffic to identify protocols is the basis of cybersecurity detection and management. However, network protocol analysis is challenged by the problem of concept drift and class imbalance. Moreover, as the network traffic are arriving at high speed, it is also costly to label the traffic data in real time. In this paper, an ensemble online active learning method is proposed. To select the most representative samples and the minority class samples in the network traffic streams, the novel hybrid active learning labeling strategy based on predicted uncertainty and class imbalance ratio is proposed, which uses margin value as criterion and decision threshold based on the class imbalance ratio. To make full use of the labeled samples, a samples training weight adjustment mechanism is designed to enhance the performance of the ensemble classifier. Moreover, the drift detection mechanism is also integrated into the ensemble classifier to detect possible drift in the traffic stream and evolve the classifier. Experimental results show that the proposed method has a good protocol identification ability especially on the minority class protocols.
Research of Frequency Synthesizer Based on SIP Technology with Novel Structure

A frequency synthesizer based on SIP Technology with novel structure is presented in this paper, which combines the advantages of miniaturization and easy to maintain. The system is designed on PLL (Phase-Locked Loop)-DDS (Direct Digital Synthesis)-MIX (Mixer), implemented in LTCC and Fuzz-Button. According to that, an excellent performance Ku-Band (14.6GHz~14.8GHz) frequency synthesizer SIP with low phase noise (<; -95dBc/Hz@1kHz) and low spurious in narrow band (<; -75dBc) is developed. Using this structure, the size of frequency synthesizer is reduced to 31.8x40.6x6mm.
Librarians’ utilization of innovative information dissemination tools in reaching out to virtual users

Purpose The purpose of this paper is to reflect on innovative tools used by librarians in enhancing information dissemination. Design/methodology/approach This is a general review where previous research papers on the phenomenon of the study have been consulted. Findings The findings indicate that there are various tools available for use by librarians in reaching out to virtual users as far as information dissemination is concerned. Originality/value This paper sheds light on current and future tools for enhancing information dissemination in libraries. This can also be used to initiate further research.
A Fog Covered Object Recognition Algorithm Based On Space And Frequency Network

It is difficult to recognize a target object from foggy images. Aiming at solving this problem, a new algorithm is thereby proposed. Fog concentration estimation is the prerequisite for image defogging. Due to the uncertainty... | Find, read and cite all the research you need on Tech Science Press
Developing a legal assistant website “Notoaturan” using Waterfall method

The development of increasingly advanced technology brings great benefits to people's lives. This is evidenced by the many uses of technology in various fields of life. The development of information technology also affects people's behavior which shows a new phenomenon where several activities can be carried out online to make it more effective and efficient. This also happens in the field of law, with the term e-legal services. The process of digitizing legal services can be realized if there is good support from the government, legal service providers, and the community. The government itself has provided laws and regulations that discuss legal digitization. Data and information are collected in two ways, interviews and literature study. By using the waterfall software development method, the Notoaturan website is built using the PHP programming language, with the Codeigniter 3 framework with MySQL database. The results obtained are in the form of a notoaturan website that provides services related to notaries and lawyers. Notary provides a process for ordering notary services such as creating a business entity, creating a CV, making a PT, and others, as well as providing services for placing an order for a lawyer starting with prior consultation. The website is also equipped with several additional services such as complaints and assistance for using the website.
Filter-Based Feature Selection and Machine-Learning Classification of Cancer Data

Microarray cancer data poses many challenges for machine-learning (ML) classification including noisy data, small sample size, high dimensionality, and imbalanced class labels. In this paper, we propose a framework to address these problems by properly utilizing feature-selection techniques. The most important features of the cancer datasets were extracted with Logistic Regression (LR), Chi-2, Random Forest (RF), and LightGBM. These extracted features served as input columns in an applied classification task. This framework’s main advantages are reducing time complexity and the number of irrelevant features for the dataset. For evaluation, the proposed method was compared to models using Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Decision Tree (DT), LR, and RF. To prove the proposed framework’s efficiency, all the experiments were performed on four standard datasets, encompassing two binary and two multiclass imbalanced-microarray cancer datasets: Lung (5-class dataset), Small Round Blue Cell Tumors (SRBCT; 4-class dataset), and Ovarian and Breast Cancer 2-class datasets). The experimental results of our comparison showed that the proposed framework achieved the highest predictive performance. A comparative study of our framework, using accuracy and F1 as metrics, was performed against state-of-the-art approacheswhich illustrated that the proposed method presented a better result for two of the selected datasets.
Fabrication of an energy dual-axis solar tracking system and performance analyses through optimal values of solar panel parameters

This work presents a hardware development of a solar tracking system using arduino pro mini and further analyzing the performance of dual axis solar tracking systems. The objective is to examine the performance of the parameters such as voltage, power, all day efficiency, cost; through different settings of solar tracker. The proposed work has been established on hardware and analyzed using software mode. The hardware has been set up using two servo motors, along with a light dependent resistor (LDRs), which senses the solar rays. Further the motions of the motors are performed by moving the solar panel towards point or location corresponding to the source of maximum light. The control portion was developed by using C programming as a software mode of operation. The control mechanism was targeted to the Arduino UNO for functioning of the hardware system. The result showed that the solar tracking system produces maximum all day power corresponding to dual axis tracker. It resulted in 25% more than that of a single axis tracker and 51% more than that of a fixed tilt angle. Moreover, a Therefore, a solar tracker can effectively capture the maximum sunlight source and can be used for any small applications in order to achieve more electrical power.