Reviewerly/exHarmony huggingface dataset:
Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

There is growing interest in graph pattern mining (GPM) problems such as motif counting. GPM systems have been developed to provide unified interfaces for programming algorithms for these problems and for running them on parallel systems. However, existing systems may take hours to mine even simple patterns in moderate-sized graphs, which significantly limits their real-world usability.We present Pangolin, an efficient and flexible in-memory GPM framework targeting shared-memory CPUs and GPUs. Pangolin is the first GPM system that provides high-level abstractions for GPU processing. It provides a simple programming interface based on the extend-reduce-filter model, which allows users to specify application specific knowledge for search space pruning and isomorphism test elimination. We describe novel optimizations that exploit locality, reduce memory consumption, and mitigate the overheads of dynamic memory allocation and synchronization.Evaluation on a 28-core CPU demonstrates that Pangolin outperforms existing GPM frameworks Arabesque, RStream, and Fractal by 49×, 88×, and 80× on average, respectively. Acceleration on a V100 GPU further improves performance of Pangolin by 15× on average. Compared to state-of-the-art hand-optimized GPM applications, Pangolin provides competitive performance with less programming effort.
The Impact of Vocabulary Depth and Breadth to the TOEFL Reading Subtest in IAIN Kediri

TOEFL has been provided as a standardized test to measure the English proficiency of non-native speakers. However, one thing should be considered that TOEFL is still a big challenge for non-native English learners, especially for EFL learners. This is one of the factors, related to how proficient the vocabulary knowledge which students have. Therefore, this study aimed to investigate to what extent the relation between vocabulary knowledge, breadth and depth, and TOEFL test, especially reading subtest session. Participants of this study were undergraduate English students who never took a TOEFL preparation course. To help analyze data, IBM SPSS 24 version with Spearman rank correlation analysis was used to make it more precise. The result of Spearman rank correlation analysis revealed that there was a significant correlation among the variables. This result supports some previous studies focusing on the same topic.
Teachers’ Professional Development in the Process of Workers’ Training for Worldskills Championships’ Participation

The comparative-pedagogical study of the systems of professional-pedagogical education both in Russia and abroad can be considered very actual, while the processes of globalization and integration influence greatly upon the transformation of the existing educational paradigm. It manifests itself both in the estimation of essential characteristics of professional-pedagogical education, as well as in the attitude towards vocational education and training (VET): thus, it is becoming more attractive for students – future specialists of the labour market in accordance with the opportunity to participate in the international movement "Worldskills". Thus the problem of professional development of teachers, able to form a completely new type of a working person ‒ creative, flexible to the changes in the globalizing world, responsible and active is of a particular importance at the present moment. The demand for results of conducted research is determined by the ability to re-think and over-estimate critically the conceptual basis of the system of professional development of teachers, to define vectors of its development in accordance with major educational trends and tendencies, to enlarge pedagogical resource of teaching and learning in the system of higher pedagogical education.
A novel method for culminating the consumption of fast food using PCA Reduction and K-means Clustering Algorithm

Nowadays peoples are going fast. Everyone used to do all things fast. They spend most of the time to sleep in the morning from the last half an hour they get ready to go for their jobs. In this hurry burry situation they have no time to cook and eat. So they are eating outside food which is unhealthy fast food. Fast food is good in taste but not good in ingredients. Fast food has many types and continuous consumption of fast food may lead to various serious health issues. In this paper, presented preprocessing and clustering technique to find the impacts of fast food using Data Mining. Data Mining is a process of mining/finding hidden or unknown information/pattern from the data using data mining techniques.
Sparse Bayesian approach to fast learning network for multiclassification

This paper proposes a novel artificial neural network called sparse-Bayesian--based fast learning network (SBFLN). In SBFLN, sparse Bayesian regression is used to train the fast learning network (FLN), which is an improved extreme learning machine (ELM). The training process of SBFLN is to randomly generate the input weights and the hidden layer biases, and then find the probability distribution of other weights by the sparse Bayesian approach. SBFLN calculates the predicted output through Bayes estimator, so it can provide a natural marginal possibility for classification problems and can solve the overfitting problem caused by the least-squares estimation in FLN. In addition, the sparse Bayesian approach can automatically trim most redundant neurons in hidden layer, which makes the network more compact and accurate. To verify the effectiveness of the improvements in this paper, the results of SBFLN are evaluated in 15 benchmark classification problems. The experimental results show that SBFLN is not sensitive to the number of neurons in the hidden layer, and the performance of SBFLN is competitive or superior to some other state-of-the-art algorithms.
Performance Analysis of ARM Architecture Based Mobile Processor with Multi-thread Image Compression

The increasing use of social media in recent years has brought the problem of transmitting picture and audio data on the internet. In order to reduce the burden on the internet during data transfer, it is necessary to either increase the bandwidth of the internet or reduce the size of the data by compressing it. In this study, the performance change due to multi-core usage of an ARM architectured mobile processor is analyzed on the image compression process with Haar Wavelet. The results showed that the use of multi-cores in the compression process reduces runtime but leads to an increase in processor temperature. In addition, it is observed that the working time of the scenario where the number of threads and the number of cores is equal works faster than the other scenarios.
Efficient virtual machine placement algorithms for consolidation in cloud data centers

Dynamic Virtual Machine (VM) consolidation is a successful approach to improve the energy efficiency and the resource utilization in cloud environments. Consequently, optimizing the online energy-performance tradeoff directly influences quality of service. In this study, algorithms named as CPU Priority based Best-Fit Decreasing (CPBFD) and Dynamic CPU Priority based Best-Fit Decreasing (DCPBFD) are proposed for VM placement. A number of VM placement algorithms are implemented and compared with the proposed algorithms. The algorithms are evaluated through simulations with real-world workload traces and it is shown that the proposed algorithms outperform the known algorithms. The simulation results clearly show that CPBFD and DCPBFD provide the least service level agreement violations, least VM migrations, and efficient energy consumption.
Lighting technique for attaching invisible information onto real objects using temporally and spatially color-intensity modulated light

This paper describes a lighting technique which enables us to invisibly attach information onto a real object by using illumination light and to extract the information from a video image of the object captured with a video camera. A temporally and spatially color-intensity modulation embeds information into a color component of the light beam. First, we assessed the invisibility to the human eye of the information attached onto a real object visible to the human eye. The experiments showed an upper limit value of modulation amplitude that satisfied invisibility for each color component. The experiment results revealed that the blue color component had a much higher degree of invisibility than the red and green components. We also examined the readability of the information attached to the object with a video camera when the information was embedded in the blue color component of the light beam. The experimental results showed a lower limit value of modulation amplitude that satisfied readability. The experimental results showed the range of modulation amplitude of intensity in the blue color that satisfied the invisibility and readability of the information attached to the object. The results of our experiments demonstrated the effectiveness of the proposed technique.
IMPLEMENTASI ALGORITMA KNUTH MORRIS PRATT PADA APLIKASI SINOPSIS FILM BIOSKOP BERBASIS WEB

Synopsis of cinema films is a description that illustrates the storyline of a film that will be screened on a bisocope with a brief study but can convey information related to the outline of a cinema film story that will be played on a bisocope. The number of synopsis of cinema films that are stored in the database of a system affects the use of time and space needed in the process of searching synopsis data. Synopsis search for cinema films can not be separated from the existence of string matching to match keywords searched with cinema film data stored in the database system, the better the way the string matching algorithm is applied to the search feature in a system, the easier it is for users to find data synopsis of cinema films sought. Knuth Morris Pratt uses the stage of shifting text patterns from left to right in the process of matching strings in text if there is a mismatch between the character pattern and the text character. In this study the Knuth Morris Pratt algorithm is used to make it easier for users to find synopsis data of cinema films that are searched according to the keyword searches performed on the search feature in the system.Keywords: synopsis, movie theaters, String Matching, Knuth Morris Pratt
Hybrid methods of brandt’s generalised likelihood ratio and short-term energy for malay word speech segmentation

&lt;p&gt;Speech segmentation is an important part for speech recognition, synthesizing and coding. Statistical based approach detects segmentation points via computing spectral distortion of the signal without prior knowledge of the acoustic information proved to be able to give good match, less omission but lot of insertion. In this study the segmentation is done both manually and automatically using Malay words in traditional Malay poetry. This study proposed a hybrid method of Brandt’s generalized likelihood ratio (GLR) and short-term energy algorithm. The Brandt’s algorithm tries to estimate the abrupt change in energy to determine the segmentation points. A total of five Pantun are used in read mode and spoken by one male student in a noise free room. Experiments are conducted to see the the accuracy, insertion, and omission of the segmentation points. Experimental results show on average 80% accuracy with 0.2 second time tolerance for automatic segmentation with the algorithm having no knowledge of the acoustic characteristics&lt;em&gt;. &lt;/em&gt;&lt;/p&gt;
Optimal PAC-Bayesian Posteriors for Stochastic Classifiers and their use for Choice of SVM Regularization Parameter

PAC-Bayesian set up involves a stochastic classifier characterized by a posterior distribution on a classifier set, offers a high probability bound on its averaged true risk and is robust to the training sample used. For a given posterior, this bound captures the trade off between averaged empirical risk and KL-divergence based model complexity term. Our goal is to identify an optimal posterior with the least PAC-Bayesian bound. We consider a finite classifier set and 5 distance functions: KL-divergence, its Pinsker's and a sixth degree polynomial approximations; linear and squared distances. Linear distance based model results in a convex optimization problem. We obtain closed form expression for its optimal posterior. For uniform prior, this posterior has full support with weights negative-exponentially proportional to number of misclassifications. Squared distance and Pinsker's approximation bounds are possibly quasi-convex and are observed to have single local minimum. We derive fixed point equations (FPEs) using partial KKT system with strict positivity constraints. This obviates the combinatorial search for subset support of the optimal posterior. For uniform prior, exponential search on a full-dimensional simplex can be limited to an ordered subset of classifiers with increasing empirical risk values. These FPEs converge rapidly to a stationary point, even for a large classifier set when a solver fails. We apply these approaches to SVMs generated using a finite set of SVM regularization parameter values on 9 UCI datasets. These posteriors yield stochastic SVM classifiers with tight bounds. KL-divergence based bound is the tightest, but is computationally expensive due to non-convexity and multiple calls to a root finding algorithm. Optimal posteriors for all 5 distance functions have lowest 10% test error values on most datasets, with linear distance being the easiest to obtain.
The Values of Character Education in Indonesia Children Literature and Translated Children Literature

This research is aimed at exploring the values of character education in Indonesia children literature and translated children literature.The research employs qualitative research by using a content analysis approach.The citations which contain the values of character education found in Indonesia children literature and translated children literature are the data of the research.The research findings indicate: First, the values of religious character education contained in Indonesia children literature are 20.6%, and translated children literature are 5.4%.Second, the values of self-potential character education in Indonesia children literature are 75.7%, and while in translated children literature are 88.3%.Third, family character education is 1.21% for Indonesia children literature and 0.9% for translated children literature.Fourth, the cultural education values in Indonesia children literature are 1.21% and 3.6% are in translated children literature.Fifth, environmental education in Indonesia children literature and translated children literature get 1.81%.It is concluded that Indonesia children literature and translated children literature contain the values of character education with certain proportion.Both Indonesia children literature and translated children literature are worthy of being used as a vehicle for character education for children.
Face Image Set Recognition Based On Improved HOG-NMF and Convolutional Neural Networks

Objective Face recognition can be affected by unfavorable factors such as illumination, posture and expression, but the face image set is a collection of people’s various angles, different illuminations and even different expressions, which can effectively reduce these adverse effects and get higher face recognition rate. In order to make the face image set have higher recognition rate, a new method of combining face image set recognition is proposed, which combines an improved Histogram of Oriented Gradient (HOG) feature and Convolutional Neural Network (CNN). Method The method firstly segments the face images to be identified and performs HOG to extract features of the segmented images. Secondly, calculate the information entropy contained in each block as a weight coefficient of each block to form a new HOG features, and non-negative matrix factorization (NMF) is applied to reduce HOG features. Then the reduced-dimensional HOG features are modeled as image sets which keep your face details as much as possible. Finally, the modeled image sets are classified by using a convolutional neural network. Result The experimental results show that compared with the simple CNN method and the HOG-CNN method, the recognition rate of the method on the CMU PIE face set is increased by about 4%~10%. Conclusion The method proposed in this paper has more details of the face, overcomes the adverse effects, and improves the accuracy.
Innovative Teaching Pedagogy in Nursing Education

There are a variety of teaching strategies that instructors can use to improve student learning. It is of great importance to select appropriate teaching strategies in nurse education to make the training more appealing and more effective. In this article, few teaching strategies will be introduced to help instructors learn how to involve the teaching strategy in the nurse education, Faculty and students work together to create environments that promote leadership, Interprofessional education, transformative research and cultural inclusiveness. Through innovative teaching and learning approaches that address a variety of learning styles, professors actively engage students. Whether it’s future researchers and nursing faculty looking to advance their careers or nurses, nurse practitioners and physician assistants seeking greater clinical knowledge, all students at the School of Nursing benefit from professors with diverse interests who create learning environments that empower them to be agents of health care change. Technology allows students to maximize learning time by completing coursework before coming to class. The elimination of lecture-style instruction engages students in a twoway flow of information. Faculty recognizes adults learn differently and instruction must accommodate multiple learning styles. New research validates that student-driven education promotes retention far better than lecturing. Ultimately, School of Nursing graduates effectively function to the highest standards of care
An overview of unmanned vehicle path planning algorithms

Abstract This paper summarizes the methods of global path planning and local path planning. By analyzing the recent improvements of RRT and VFH algorithms made by scholars, this paper gives answers to some existing problems in path planning, and then illustrates the effectiveness of these improved algorithms.
Flexible Assessment in Digital Teaching-Learning Processes: Case Studies via Computational Thinking

This proposal presents three applications: (i) the F-ATL methodology expressing the uncertainty inherent in assessment of teaching-learning (ATL) processes; (ii) two case studies validating the F-ATL methodology via activities of Computational Thinking (CT) applying fuzzy logic and the interval-valued fuzzy logic; (iii) the impact analysis related to the validation of both case studies. This proposal also focuses on modelling uncertainty in the assessment of digital educational resources and technologies in ATL processes, regarding the development of relevant thinking skills via CT. The case studies classify the studentsmarks in elementary school, evaluating their performance and considering CT skills as algorithm, generalization, abstraction, decomposition and evaluation.
The Relational Parts of Speech in Text Analysis for Definition Detection, for Romanian Language

This article presents a method for detecting definitions in text, as well as identifying their formal parts, using the predictive capacity of certain relational parts of speech, mainly prepositions but also including words from other lexical categories such as adverbs, pronouns and conjunctions. The method is specifically tailored for Romanian language. Based on their predicting capacity, the relational words can be distributed into four categories that influence the way the definitions are detected. Using an annotated corpus of definitions, a series of statistics is extracted and used to formulate measures describing the impact and usage patterns of these relational words.
Finding better learning algorithms for self-driving cars: An overview of the LAOP platform

Cars are becoming more and more intelligent, embedded with a range of sensors to give them local perception of their environment (LIDARs, cameras, etc.). Trendy companies like Google and Tesla are actively testing cars on American roads that can drive without any human interaction [1]. Neural networks are the modern approach for autonomous cars. However, an inefficient neural network algorithm will make the learning process slower and will result in a less reliable autonomous vehicle. In this paper, we will introduce a platform built in JAVA named LAOP (Learning Algorithm Optimization Platform) [2] while explaining the solutions we found to make it easy for researchers to test and compare their own algorithms. Then, we will show how we have integrated a natural selection algorithm with a neural network in order to improve them. Moreover, we will demonstrate how the Fully Connected Neural Network and the NeuroEvolution of Augmenting Topologies (NEAT) [3] algorithms are implemented in the context of vehicular learning on LAOP. Finally, we will display the different results extracted from LAOP by tuning several various parameters such as the weight mutation chance and the car density in the simulation.
Le voci dei personaggi: insegnare l’uso del discorso riportato nella scuola primaria

This paper presents the results of a study conducted on the written narratives of two fifth-grade primary school classes involved in the project Observing the Interlanguage. The aim was evaluating the effects of a pedagogical intervention in which the pupils were guided to observe the linguistic-discursive properties of oral and written texts (literary and produced by the students themselves) on their choices regarding the use of reported speech. The results show that children in the experimental classes write more vivid narratives than their peers in two control classes, because of their greater use of direct speech. The study demonstrates the effectiveness of pedagogical interventions which give pupils an active role as “little researchers and involve them in collaborative tasks of observation and manipulation of their own and others' texts.
A Clustering Based Anonymization Model for Big Data

Today, a lot of institutions collect and store big data belongs to their respondents (client, patients, users, firms etc.). The main purposes of these actions can be such as doing their missions and providing better services (modeling, extracting behavior patterns, disease detection, making future plans, creating policies, developing decision-making mechanisms). To benefit from the collected big data at a higher level, it is inevitable to publish the data. However, if the big data includes sensitive information about responders, a direct release of these data may cause disclosure of identities of respondents. Hence new solutions to protect the privacy of respondents are always required. Anonymization is a utility-based privacy preserving approach that is frequently used in privacy-preserving big data publishing (PPBDP). In this paper, a clustering-based anonymization model on Spark is proposed and applied for the first time. The main purpose of the proposed approach is evaluating anonymization problem as a clustering problem. Distributed k-Means algorithm is used for anonymization in the proposed model. In order to adopt a clustering-based approach to k-anonymity, some assumptions were made. As a result, the proposed model provides a plausible solution to PPBDP.