Reviewerly/exHarmony huggingface dataset:
The engaging of early age children in environmental behavior through wayang kancil folktale

School is a medium to solve the waste problem through clean lifestyle psychoeducation. Wayang kancil folktale uses animal characters as an alternative method to teach early age children to reduce littering behavior. This approach is applied because the animal world is close to the children's world. In addition, an interaction between children and the storyteller through folktale occurs during wayang kancil show. This study is action research which involves 14 second-year nursery student from a pre-school institution in Yogyakarta. The A-B-A experiment design puts the hypotheses of wayang kancil folktale to stimulate littering behavior at an early age to the test. Baseline, intervention, and reversal measurement was conducted consecutively for nine days which was recorded in observation sheets. Comprehension test was carried out before and after the intervention. The observation result was analyzed using visual analysis. Meanwhile, a comprehension score is rated by implemented paired sample t-test. The coefficient of reliability test of inter-rater was 0.971. Pre and post-intervention comprehension score increased rapidly (p = 0.000; &lt; 0.01). The result proves that wayang kancil folktale was able to reduce childrenâ€™s littering behavior.
A genetic algorithm rooted in integer encoding and fuzzy controller

The premature convergence is the essential problem in genetic algorithms and it is strongly related to the loss of genetic diversity of the population. In this study, a new sexual selection mechanism which utilizing mate chromosome during selection proposed and then technique focuses on selecting and controlling the genetic operators by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with some other operators, heuristic and local search algorithms commonly used for solving benchmark problems published in the literature.
Framework For Lossless Data Compression Using Python

A lot has been done in the field of data compression, yet we don’t have a proper application for compressing daily usage files. There are appropriate and very specific tools online that provide files to be compressed and saved, but the content we use for streaming our videos, be it a Netflix video or a gaming theater play, data consumed is beyond the calculation of a user. Back-end developers know all about it and as developers we have acknowledged it but not yet achieved it in providing on an ease level. Since the user would not never be concerned about compression, developers can always take initiative while building the application to provide accessibility with compression before-hand. We have decided to create a framework that will provide all the functionality needed for a developer to add this feature. Making use of the python language this process can work. I’m a big fan of Python, mostly because it has a vibrant developer community that has helped produce an unparalleled collection of libraries that enable one to add features to applications quickly. For the DEFLATE lossless compression, has a higher level of abstraction provided by the zlib C library, in Python it is generally provided by the Python zlib library which is an interface, we have a lot to do including the audio, video and subtitles of the file. We also make use of the fabulous ffmpy library. ffmpy is a Python library that provides access to the ffmpeg command line utility. ffmpeg is a command-line application that can perform several different kinds of transformations on video files, including video compression, which is the most commonly requested feature of ffmpeg. Frame rate and audio synchronization are few other parameters to look closely. This is an ongoing project and there remains few implementation aspects, data compression remains a concern when touched upon the design. We along with python community intend to solve this issue.
A CCD based machine vision system for real-time text detection

Text detection and recognition is a hot topic in computer vision, which is considered to be the further development of the traditional optical character recognition (OCR) technology. With the rapid development of machine vision system and the wide application of deep learning algorithms, text recognition has achieved excellent performance. In contrast, detecting text block from complex natural scenes is still a challenging task. At present, many advanced natural scene text detection algorithms have been proposed, but most of them run slow due to the complexity of the detection pipeline and cannot be applied to industrial scenes. In this paper, we proposed a CCD based machine vision system for real-time text detection in invoice images. In this system, we applied optimizations from several aspects including the optical system, the hardware architecture, and the deep learning algorithm to improve the speed performance of the machine vision system. The experimental data confirms that the optimization methods can significantly improve the running speed of the machine vision system and make it meeting the real-time text detection requirements in industrial scenarios.
Attention Model Using Full-Time Information For Sentences Emotion Classification

This paper proposed a method that uses the output information of LSTM at each time point for the final sentence emotional judgment, and constructed three classification models based on the existing attention model and different filters. In this paper, the model was trained on the basis of pre-trained word vectors and compared with the existing models. Through rigorous experiments, it is found that the performance of the model improved by this article is better than the model that does not use full-time information in emotional classification, and models using different filters show different results.
Zero-Aware Pattern Databases with 1-Bit Compression for Sliding Tile Puzzles

A pattern database (PDB) is a pre-computed lookup table storing shortest distances from abstract states to abstract goal states. PDBs are key components in heuristic search as their entries are used to prune paths that cannot lead to an optimal solution. With the sliding-tile puzzle as an exemplary application domain, we present methods to improve the precision and size of PDBs by improving additive pattern databases to zero-aware additive pattern databases (ZPDBs), reducing the compression rate from previously 1.6 bit to 1 bit per entry, generating optimal additive pattern partitionings, and building effective collections of pattern databases. With these enhancements, we achieve an overall 8.59-fold performance gain on the 24-puzzle compared to the previously best set of 6-tile PDBs.
Pengujian Algoritma MTCNN (Multi-task Cascaded Convolutional Neural Network) untuk Sistem Pengenalan Wajah

Measurement of facial similarity or checking similarity is done using features. The algorithm for describing the most up-to-date and best face features for generating features is Deep Convolutional Neural Network (DCNNs). Based on this, this study uses MTCNN (Multi-task Cascaded Convolutional Neural Network) as one variation of the DCNN method. In this research, we built a research system to test results with javascript. Given the many needs that are based on mobile or can be run on a smartphone. One of them is to support the absent feature that is used in a mobile manner such as the reporting system of sales and marketing performance or members of the police personnel who normally work on a mobile basis. From the results of the tests carried out automatically using several variation models testing the image of the Aberdeen dataset as many as 60 images from 30 different people used in the face recognition research system using MTCNN with influencing image parameters such as lighting variations, object position variations, then the position taken and expression face on the object image, the research system managed to do face recognition by 100%. Thus, true positive values are equal to the amount of data tested and zero negative true values.
A Theoretical Comparison of the Bounds of MM, NBS, and GBFHS

Recent work in bidirectional front-to-end heuristic search has led to the development of novel algorithms that have advanced the state of the art after many years without major developments. In particular, three different algorithms with very different behavior have been proposed: MM, NBS and GBFHS. In this paper we perform a theoretical comparison of these algorithms, defining lower and upper bounds for each of them and analyzing why a given algorithm displays beneficial characteristics that the others lack. With this information, we propose a simple and intuitive near-optimal algorithm to be used as a baseline for comparison in bidirectional front-to-end heuristic search.
CRSRL: Customer Routing System Using Reinforcement Learning

Allocating resources to customers in the customer service is a difficult problem, because designing an optimal strategy to achieve an optimal trade-off between available resources and customers' satisfaction is non-trivial. In this paper, we formalize the customer routing problem, and propose a novel framework based on deep reinforcement learning (RL) to address this problem. To make it more practical, a demo is provided to show and compare different models, which visualizes all decision process, and in particular, the system shows how the optimal strategy is reached. Besides, our demo system also ships with a variety of models that users can choose based on their needs.
Belief Update without Compactness in Non-finitary Languages

The main paradigms of belief change require the background logic to be Tarskian and finitary. We look at belief update when the underlying logic is not necessarily finitary. We show that in this case the classical construction for KM update does not capture all the rationality postulates for KM belief update. Indeed, this construction, being fully characterised by a subset of the KM update postulates, is weaker. We explore the reason behind this, and subsequently provide an alternative constructive accounts of belief update which is characterised by the full set of KM postulates in this more general framework.
Micro Agent and Neural Network based Model for Data Error Detection in a Real Time Data Stream

In this paper, we present a model for learning and detecting the presence of data type errors in a real time big data stream processing context.The proposed approach is based on a collection of micro-agents.Each micro-agent is trained to detect a specific type of error using an atomic neural network based on a sample multilayer perceptron.The supervised learning process is based on a binary classifier where the training data inputs are represented by data types and data values.The Micro-Agent for Error Detection (MAED) is deployed at several instances depending on the number of error types to be handled.The orchestration mechanism of data streams to be examined is performed by a special Host Micro Agent (HMA).This later receives in real time a data stream, splits the current record into several elementary fields.Each field value is streamed to an instance of MAED Agent which responds with a signal of presence or not of a specific data type error of the corresponding data field.For each detected data type error, the HMA Agent selects and performs the appropriate cleaning algorithm from a repository to correct the present errors of the data stream.To validate this approach, we propose an implementation based on Framework Deep Learning 4j for the Machines Learning part and JADE as a Multi Agent System (MAS) platform.The used dataset is generated by an events generator for smart highways.
JARGON CAPRES – CAWAPRES FIKTIF; NURHADI – ALDO DALAM WACANA PRA-PEMILU DI INDONESIA TAHUN 2019

[Title: The Jargon of Presidential and Vice Presidential Candidate; Nurhadi-Aldo in the Pre-Election Discourse in Indonesia in 2019] This study discusses the phenomenon of the emergence of the fictional vice-presidential candidate Nurhadi-Aldo in the midst of the heat of the situation in 2019 pre-election Indonesian politics with a number of unique jargon. The uniqueness of jargon is the result of the words that are used so it is interesting to discuss the meaning and structure of the sentence. This study uses a theory that is discourse analysis theory to find the meaning of these jargon. The theory of discourse analysis consists of three structures, namely (1) macro structure, (2) superstructure, and (3) microstructure. The macro structure consists of thematic observation domains or themes. The superstructure consists of a schematic field of observation, namely how the structure and elements of the discourse are arranged in full text. The micro structure consists of the domains of observation (a) semantics, (b) syntax, (c) stylistics, and (d) rhetorical. However, in this study the realm of syntactic observation is not used because it is considered that no data can be examined.
A Privacy Violation Behaviors Preventive System in Using Social Media by Graduate Students of King Mongkut’s Institute of Technology Ladkrabang

Abstract The purpose of this research was to create, test and develop a model for developing a system to protect personal rights violations in using social media, Integrated learning styles using deductive teaching methods and the use of case studies to promote knowledge in developing a model for developing a system to protect personal rights violations in the use of social media of graduate students at King Mongkut Institute of Technology at Ladkrabang. The sample group used to develop the protection system model is a legal expert, 7 people, namely, graduate students of King Mongkut Institute of Technology Ladkrabang, 20 people. The tools used in this research are interview with legal experts and instructors, Knowledge measurement form, Online teaching and learning management system on privacy violation behavior in the use of social media Satisfaction questionnaire for learning styles and Assessment form for certification of integrated teaching and learning styles. The results from the test scores of the pre-school and post-test scores of the sample group showed that the post-test scores had an average score higher than before (before 10.80 and 13.71). In addition, it was found that the post-test scores had a standard deviation less than the test scores before learning (after learning is 1.29 and before 3.70), indicating that the post-test scores were higher than the previous test scores. Therefore, it can be concluded that the sample group when learning through the learning style resulted in higher average student scores.
Perancangan Jaringan VPN Menggunakan Metode Point To Point Tunneling Protocol

PT. Hail Otis Logistics is a Forwarding and Expedition Company that provides Custom Clearence Export and Import services as well as Shipping or Delivery from Partner Companies to Tanjung Priuk Port and vice versa. Currently PT. Hail Otis Logistics is sending company data using e-mail, so the company cannot yet ensure the security of the data sent. Due to the company's problems, designing a VPN is a means of communicating and transferring data securely and maintaining data validity. In this study VPN was built using the PPTP (Point To Point Tunneling Protocol) method to build a VPN network at PT. Hail Otis Logistics. Use PPTP because this method uses a protocol that allows point to point relationships that pass through the IP network. Results obtained in VPN development at PT. Hail Otis Logistics are VPN networks that can connect between headquarters and branch offices to transfer data safely. VPN development can provide security with encryption in every data communication.Keywords: PPTP, Network, VPN
Learning Deep Decentralized Policy Network by Collective Rewards for Real-Time Combat Game

The task of real-time combat game is to coordinate multiple units to defeat their enemies controlled by the given opponent in a real-time combat scenario. It is difficult to design a high-level Artificial Intelligence (AI) program for such a task due to its extremely large state-action space and real-time requirements. This paper formulates this task as a collective decentralized partially observable Markov decision process, and designs a Deep Decentralized Policy Network (DDPN) to model the polices. To train DDPN effectively, a novel two-stage learning algorithm is proposed which combines imitation learning from opponent and reinforcement learning by no-regret dynamics. Extensive experimental results on various combat scenarios indicate that proposed method can defeat different opponent models and significantly outperforms many state-of-the-art approaches.
Learning Assistance from an Adversarial Critic for Multi-Outputs Prediction

We introduce an adversarial-critic-and-assistant (ACA) learning framework to improve the performance of existing supervised learning with multiple outputs. The core contribution of our ACA is the innovation of two novel modules, i.e. an `adversarial critic' and a `collaborative assistant', that are jointly designed to provide augmenting information for facilitating general learning tasks. Our approach is not intended to be regarded as an emerging competitor for tons of well-established algorithms in the field. In fact, most existing approaches, while implemented with different learning objectives, can all be adopted as building blocks seamlessly integrated in the ACA framework to accomplish various real-world tasks. We show the performance and generalization ability of ACA on diverse learning tasks including multi-label classification, attributes prediction and sequence-to-sequence generation.
Maximal Correlation Embedding Network for Multilabel Learning with Missing Labels

Multilabel learning, the problem of mapping each data instance to a subset of labels, appears frequently in many real-world applications. However, obtaining complete label annotation for every instance requires tremendous efforts, especially when the label set is large. As a result, multilabel learning with missing labels remains as a common challenge. Existing works either cannot handle missing labels or lack nonlinear expressiveness and scalability to large label set. In this paper, we present a novel end-to-end solution for multilabel learning with missing labels. Our algorithm, Maximal Correlation Embedding Network learns a low dimensional label embedding using an encoder-decoder architecture. It exploits label similarity through a maximal correlation regularization in the embedded label space to reduce the classification bias due to missing labels. A series of experiments on popular multilabel datasets demonstrate that our approach outperforms state of the art, both in complete data and partially observed data.
Grammatical Errors Made By the Students of English Department UISU Year 2018 in Their Writing Class

This research attempts to identify the grammatical errors produced by students of&#x0D; English Department of UISU year 2018 in their final paper of writing class. In particular,&#x0D; it attempts to classify the errors based on Surface Strategy Taxonomy proposed by&#x0D; Dulay, Burt, and Krashen (1982). The data used in this research are taken from the&#x0D; submitted assignments of English Department students of Universitas Islam Sumatera&#x0D; Utara year 2018 in General English, particularly nine students in writing class. The&#x0D; researcher found 178 errors in fourteen linguistic categories which are divided into&#x0D; four parts: omission, addition, misformation, and misordering. From all the four parts,&#x0D; the researcher discovered that the most frequent errors found are in the part of&#x0D; misformation (97 errors; 54.49%), followed by omission (38 errors; 31.11%), misordering&#x0D; (22 errors; 12.78%), and addition (20 errors; 11.11%). While from the linguistic categories,&#x0D; the three most frequent errors found are misformation of verbal (30 errors), misordering&#x0D; of complex sentence (20 errors), and omission of determiner (19 errors).
Deep Learning Based Noise Level Classification of Medical Images

In recent years, medical image technology is widely used in medical field. However, the process of imaging, storage and transmission often make the image quality reduced and affect the visual and post-processing effect, and the degradation of medical image often leads to the interference of noise. Therefore, obtaining the level of noise in medical images is an important part in image quality improvement. In order to obtain the noise level in medical image, a novel image noise level classification network based on deep learning is designed, which incorporates inception structure and dense blocks to make full use of their advantages to extract the features of noise. The inception structure is used to extract the features of noise under different resolutions to get the features of different receptive fields. Meanwhile, the dense block structure is used to take advantage of feature reuse to ensure noise features transfer across the network. Experiments on lung CT images show that the classification accuracy of the proposed method is 99.5%. The method proposed has a good effect in the application of noise level classification and provides a reliable noise prior for the image enhancement using SRMD (super-resolution of MAP and dimensionality stretching) method.
Image Segmentation Application of Conditional Convolution Restricted Boltzmann Machine

The traditional image segmentation method is not effective in characterizing the specific features of the target and the representation of high-level information, while the deep learning model doing this job well. In the existing Conditional Restricted Boltzmann Machine (CRBM), Restricted Boltzmann Machine (RBM) is used to capture the correlation of the output space. The condition represents the mapping relationship between features and categories. The deeper Conditional Deep Boltzmann Machine (CDBM) model developed on the basis of CRBM can obtain higher order information to improve the accuracy of image segmentation. However, the different image sizes in real life and various interference factors of images limit the performance of the above two types of models, and the convolution operation is outstanding in extracting the detailed local features of the image. Combining these two points, the Conditional Convolution Restricted Boltzmann machine (CCRBM) is proposed, further improving the accuracy and flexibility of image segmentation in shadow, clutter background and other situations of the images. The proposed method is tested in two datasets, the experiments results demonstrate the effective of the proposed method.
The Innovation Cycle for Sustainable ICT Education

Usually, the cycle of innovation is sold as a great progress in Education. However, in Education, the cycle of innovation does not exist as we might expect. Innovation is cyclical by itself. Each step of the structure can be modified, improved and complemented without waiting for a whole process that shows logic in other areas (engineering, logistics, and psychology, for instance) but that, in education, seems to be a luxury. This position paper shows why and how to perform a dynamic innovation cycle that enhances learning and teaching experiences, Worldwide, including North-North, North-South and South-South approaches, supported by initiatives by UNESCO, the International Council of Distance Education, Open Education Consortium, the European Commission and others.