Reviewerly/exHarmony huggingface dataset:
Inflation Rate Prediction in Indonesia using Optimized Support Vector Regression Model

Inflation is a indicator which illustrated the economics condition of a country. This moneter phenomenom is signed with the increase of price in entire case. It can cause an effect for political sector which impact to economic stability in a nation. The importance of inflation control is very important due to the high and unstable of inflation will cause negative impactÂ to economic and social in society.Â One of the solutions to control the inflation rate is predicting the inflation rate. This research using SVR as machine learning that is being optimized by GA as evolutionary agorithm as predicting method. SVR can solve nonlinear regression problems to linear regression using Kernel function that easy to implement. But, in SVR there is no general rule to set the parameters of SVR. Therefore, this research proposed to use GA to optimize the parameters of SVR. GA can solve the optimization problems in various research of economics prediction problem. Based on the testing that has been conducted, GA-SVR generate the MSE value is 0.03767, lower than SVR basic method is 0.053158. It proves that GA-SVR method can be utilized for predicting.
SVDTWDD Method for High Correct Recognition Rate Classifier With Appropriate Rejection Recognition Regions

At present, regions of the same class determined by Support Vector Machines (SVM) classifier, Support Vector Domain Description (SVDD) classifier and Deep Learning (DL) classifier may occupy regions of other classes or unknown classes in feature space. There exists a risk that samples of other classes or unknown classes are wrongly classified as a known class. In this paper, the Support Vector Domain Tightly Wrapping Description Design (SVDTWDD) method with appropriate rejection regions and the corresponding incremental learning algorithm are proposed to overcome the above problem. The main work includes: (1) We develop a construction algorithm of the tightly wrapping set for the homogeneous feature set; (2) Based on the homogeneous feature set and tightly wrapping set, a novel algorithm is presented for obtaining the tightly wrapping surface of the homogeneous feature region; (3) The method for constructing all the public regions outside of the tightly wrapping surface and the intersections of wrapping regions in two different tightly wrapping surfaces, as the rejection region of the classifier; (4) An incremental algorithm is also presented based on the SVD-TWDD method. The experimental results with UCI data sets show that the correct recognition rate of our proposed method is nearly100% even if with a low rejection rate.
Comprehensive Document Summarization with Refined Self-Matching Mechanism

Under the constraint of memory capacity of the neural network and the document length, it is difficult to generate summaries with adequate salient information. In this work, the self-matching mechanism is incorporated into the extractive summarization system at the encoder side, which allows the encoder to optimize the encoding information at the global level and effectively improves the memory capacity of conventional LSTM. Inspired by human coarse-to-fine understanding mode, localness is modeled by Gaussian bias to improve contextualization for each sentence, and merged into the self-matching energy. The refined self-matching mechanism not only establishes global document attention but perceives association with neighboring signals. At the decoder side, the pointer network is utilized to perform a two-hop attention on context and extraction state. Evaluations on the CNN/Daily Mail dataset verify that the proposed model outperforms the strong baseline models and statistical significantly.
The Modified Probability Hypothesis Density Filter With Adaptive Birth Intensity Estimation for Multi-Target Tracking in Low Detection Probability

The existing Probability Hypothesis Density (PHD) filters with birth intensity estimation only operate on single or two consecutive scan data for multi-target tracking. However, for those targets with low detection probability, it is hard to achieve a satisfactory level of track initiation and maintenance. To overcome the weakness above, we propose a modified PHD filter with adaptive birth intensity estimation. The core of the proposed filter is to define two state sets as the formal set and the temporary set. In the framework of measurement driven estimation, we classify the measurements into three categories depending on whether it is in the neighborhood of the state in above two sets. And the birth states of the formal set and the temporary set are generated by the classified measurements respectively. In addition, if there is no matching measurement for the state in the formal set, duplicate the corresponding state as the birth state of the temporary set. For each state in temporary set, we introduce a forgetting factor and a dynamic detection probability in filter to cope with the rapid decrease of its intensity due to the absence of measurement. If its forgetting factor is over the dead threshold, the state will be deleted from the set. Based on the principles above, we derive the Gaussian-mixture (GM) implementation of the PHD filter proposed in this paper. Experiment results show that, in low detection probability scenario, the modified PHD filter outperforms other PHD filters with birth intensity estimation.
THE USE OF TEAM GAME TOURNAMENT TO IMPROVE STUDENTS’ ELT CLASSROOM INTERACTION AND READING COMPREHENSION IN RELATION TO SELF-EFFICACY

This research is aimed to find out the implementation of Team Game Tournament to improve students’ classroom interaction and reading comprehension viewed from self-efficacy. The subject of this study was 30 students at second grade students of SMPN 5 Kopang. Which consists of 14 males and 16 females. This study was classroom action research (CAR) with two cycles, each cycle consists of four steps: planning, acting, observing and reflecting. The first cycle was focused on the students’ classroom interaction. Meanwhile, the second cycle was oriented to solve the students’ improvement of reading comprehension. The data gathering used reading test, observation sheet, and questionnaires and analyzed by using quantitative and qualitative approach. The result of the questionnaire showed the students score in the first cycle was 16.86 or 56% and the second cycle showed the students' score was 27.10 or 90.3% indicating the target of 75 of the minimum criterion has been achieved, it means that the action was stopped in the cycle 2. In other words, the students’ classroom interaction and reading comprehension in relation to self-efficacy was improved by implementing team game tournament (TGT) for the eighth-grade students of SMPN 5 Kopang.
Pengaruh Story-Reading (Buku Bilingual) terhadap Perkembangan Kecerdasan Linguistik Anak Usia Dini

This study aims to find the effect of story reading on the development of early childhood linguistic intelligence, especially in Al Hikmah Rambipuji-Jember Kindergarten. This type of research is descriptive qualitative. Data collection is done by interview, observation and documentation. From the results of the study it was found that story-reading provides several positive influences on improving linguistic intelligence, namely: (1) early childhood know and understand new vocabulary (2) understand the plot of a story (3) compose words to express an implied message of an story. Conclusion, that story-reading can give effect to the development of early childhood linguistic intelligence.&#x0D; Keywords: Story-Reading, Linguistics, Early Childhood.
STRATEGI PENGENALAN BAHASA INGGRIS PADA ANAK USIA DINI DI TK SANTA MARIA BANJARMASIN

This research discusses about the strategy of introducing English in the TK Santa Maria Banjarmasin, include in this research is: methods, media, materials and assessment . This research is descriptive qualitative, the subject of this research are 12 children from class A1 and 13 children from A2 and 1 English teacher in Santa Maria Banjarmasin. The data collected by using of observation, interviews, and documentation. The data analysis in this research used Milles and Huberman which was conducted interactively using data consisting of reductions, presenting data, and conclusion(coding). The results of research shoved every English teaching in TK Santa Maria Banjarmasin that appropriate in the learning process in accordance with the child's development, English development strategies that are equipped: 1) Learning methods used when carrying out activities introducing English methods such as singing , game , listen and repeat, questions and answer and make matches. 2) The media used are display media and worksheets. 3) The material introduces English to children in this school are family, animals, healthy food and Christmas. 4) The assessment used are observation, children product, conversation and anecdotes.
Innovative educational university environment: essence and structural components analysis

We present a detailed analysis of the essence and structural components of the university innovative educational environment. An essence analysis of the innovative educational university environment contains information about the purposeful process of its formation in both substantive and organizational aspects. This aspect depends on the goals and expected results that contribute to the formation of students’ not only professional knowledge, but also personal qualities necessary in an innovative economy. An analysis of the structural components of the innovative educational university environment is considered taking into account the influence of the ideas of synergetics, the cluster approach and pedagogical innovation studies. We give a detailed description of the substantial changes in the structural components of the innovative educational university environment, upon consideration of which we propose a new structural component - innovative resultative. We present the author’s definition of the innovative educational university environment. We substantiate the special influence of the innovative educational university environment on students, which helps to increase not only the level of professional training of students, but also the development of their personal qualities, which are in demand in the conditions of an innovative economy.
Developing a Vocational Engineering Word List: Bridging the Gaps of Secondary and Tertiary Education

There is a meaningful need to have an engineering corpus in Malaysia to bridge the vocabulary gap from secondary to tertiary engineering education. Thus, using the prescribed Malaysian KBSM vocational engineering textbooks, the development of Malaysia’s very own vocational syllabus engineering corpus was facilitated which gave way to the creation of Engineering Word List (EngWL). The word list consists of the properties those of technical and semi-technical engineering lexis. The objectives of the study were summarized as(1) To develop a pedagogic engineering corpus; (2) To create a specific Engineering Word List (EngWL) from the developed corpus. Content analysis (corpus linguistics approach) was used as the research design for the word list creation process. For the purpose of text analysis and concordance, WordSmith Tools Version 5.0 and RANGE were used. As a result, a corpus with the size of 391,505 words (15,619 types) and the EngWL with 841 word families (1,704 types) were created. Hence, lexical products can be utilized by other researchers in the effort of bridging the gaps of secondary and tertiary education.
Protection Against SQL Injection Attack in Cloud Computing

Cloud Computing is a promising paradigm that allows customers to obtain cloud resources and services according to an on-demand, self-service, and pay-by-use business model.There is the number of web application threats in cloud computing one among them is the SQL injection attack (SQLiA).In this, the attackers produce a query of their interest to have illegal access to the database.To prevent this, we use the Twofish encryption algorithm is used to secure the client's sensitive information.In this algorithm, the file uploaded by the data owner is encrypted using the Twofish algorithm and then stored it in a database.
The Efficiency of Teaching Listening and Speaking Skills to Develop Students' Communicative Competences

Th is research aims at d iscovering the teaching method, by which students' commun icative competencies can be developed.The detailed objectives of the study are: designing a co mmunicative method for teaching listening and speaking at Syekh Nurjati Islamic state university -Cirebon-.In addition, the research also endeavors to determine methods of application, its constituents, obstacles, range of effectiv ity, advantages and disadvantages.To achieve such objectives, the researcher used the mixed methods on the concurrent embedded design, which co mbines quantitative and qualitative research.The research is founded on three basic theories , among wh ich are: teaching motor performance, teaching skills and language as a means of communication.The findings revealed that designing the commun icative teaching method correlates with the interactive-constructive approach.In applying the method, several teaching techniques, such as: dialogue, information exchange, role play, lecturing, debating, and d iscussion, were used.The pillars of this teaching method are: using a book based on the communicative approach and creating a positive impression in the students with regard to teaching.Among the obstacles are: introversion of some students: enabling commun icative teaching of developing students' communicat ive competence in the skill of listening and speaking in order to perfect comprehensive competence by securing extra time.
Improving Mental Health using Machine Learning to Assist Humans in the Moderation of Forum Posts

This work investigates the potential for the application of machine learning and natural language processing technology in an online application designed to help teenagers talk about their mental health issues.Specifically, we investigate whether automatic classification methods can be applied with sufficient accuracy to assist humans in the moderation of posts and replies to an online forum.Using real data from an existing application, we outline the specific problems of lack of data, class imbalance and multiple rejection reasons.We investigate a number of machine learning architectures including a state-of-the-art transfer learning architecture, BERT, which has performed well elsewhere despite limited training data, due to its use of pre-training on a very large general corpus.Evaluating on real data, we demonstrate that further large performance gains can be made through the use of automatic data augmentation techniques (synonym replacement, synonym insertion, random swap and random deletion).Using a combination of data augmentation and transfer learning, performance of the automatic classification rivals human performance at the task, thus demonstrating the feasibility of deploying these techniques in a live system.
Cardiac cine MRI left ventricle segmentation combining deep learning and graphical models

We developed an approach for cardiac cine magnetic resonance image (MRI) left ventricle segmentation using small training dataset with noisy manual annotations. Our approach combined the strengths of deep neural networks and normalized cut with continuous regularization. We entered U-net coarse segmentation into the regularized normalized cut module that evaluates the partitioning quality within and between segmentation regions. The resulting challenging optimization problem was efficiently solved through upper bound relaxation in an iterative manner with guaranteed convergence. Within each iteration, we derived an upper bound of the high-order normalized cut term, which was combined with image-grid continuous regularization and solved using a continuous min-cut/max- ow framework. Using 5 and 10 subjects with noisy manual labels for network training, we observed much improved segmentation accuracy and minimized effects due to the size of training dataset and the quality of training annotations.
Integrating Technical Debt Management and Software Quality Management Processes: A Framework and Field Test

Software Quality Management(SQM) guarantees that the required degree of value is accomplished by submitting enhancements to the item improvement process. Software Quality Management intends to build up a culture inside the group and it is viewed as everybody's concern. SQM ought to be free of undertaking the executives to guarantee autonomy of expense and schedule adherences. It straightforwardly influences the process quality and in a roundabout way influences the product quality.
Data hiding using integer lifting wavelet transform and DNA computing

DNA computing widely used in encryption or hiding the data. Many researchers have proposed many developments of encryption and hiding algorithms based on DNA sequence to provide new algorithms. In this paper data hiding using integer lifting wavelet transform based on DNA computing is presented. The transform is applied on blue channel of the cover image. The DNA encoding used to encode the two most significant bits of LL sub-band. The produced DNA sequence used for two purpose, firstly, it use to construct the key for encryption the secret data and secondly to select the pixels in HL, LH, HH sub-bands for hiding in them. Many measurement parameters used to evaluate the performance of the proposed method such PSNR, MSE, and SSIM. The experimental results show high performance with respect to different embedding rate.
The Sequent Calculus of Skew Monoidal Categories

Szlachányi’s skew monoidal categories are a well-motivated variation of monoidal categories in which the unitors and associator are not required to be natural isomorphisms, but merely natural transformations in a particular direction. We present a sequent calculus for skew monoidal categories, building on the recent formulation by one of the authors of a sequent calculus for the Tamari order (skew semigroup categories). In this calculus, antecedents consist of a stoup (an optional formula) followed by a context, and the connectives behave like in the standard monoidal sequent calculus except that the left rules may only be applied in stoup position. We prove that this calculus is sound and complete with respect to existence of maps in the free skew monoidal category, and moreover that it captures equality of maps once a suitable equivalence relation is imposed on derivations. We then identify a subsystem of focused derivations and establish that it contains exactly one canonical representative from each equivalence class. This coherence theorem leads directly to simple procedures for deciding equality of maps in the free skew monoidal category and for enumerating any homset without duplicates. Finally, and in the spirit of Lambek’s work, we describe the close connection between this proof-theoretic analysis and Bourke and Lack’s recent characterization of skew monoidal categories as left representable skew multicategories. We have formalized this development in the dependently typed programming language Agda.
Scene Flow Estimation From Sparse Light Fields Using a Local 4D Affine Model

In this paper, we address the problem of scene flow estimation from sparsely sampled video light fields. We first propose a local 4D affine model to represent scene flows, taking into account light field epipolar geometry. The model parameters are estimated per cluster in the 4D ray space. They are derived by fitting the model on initial motion and disparity estimates obtained by using 2D dense optical flow estimation techniques. We demonstrate that the model is very effective for estimating scene flows from 2D optical flows. The model regularizes the optical flows and disparity maps, and interpolates disparity variation values in occluded regions. The proposed model allows us to benefit from deep learning-based 2D optical flow estimation methods while ensuring scene flow geometry consistency in the 4 dimensions of the light field.
Parsimonious Minimal Learning Machine via Multiresponse Sparse Regression

The training procedure of the minimal learning machine (MLM) requires the selection of two sets of patterns from the training dataset. These sets are called input reference points (IRP) and output reference points (ORP), which are used to build a mapping between the input geometric configurations and their corresponding outputs. In the original MLM, the number of input reference points is the hyper-parameter and the patterns are chosen at random. Therefore, the conventional proposal does not consider which patterns will belong to each reference point group, since the model does not implement an appropriate way of selecting the most suitable patterns as reference points. Such an approach can impact on the decision function in terms of smoothness, resulting in high complexity models. This paper introduces a new approach to select IRP for MLM applied to classification tasks. The optimally selected minimal learning machine (OS-MLM) relies on the multiresponse sparse regression (MRSR) ranking method and the leave-one-out (LOO) criterion to sort the patterns in terms of relevance and select an appropriate number of input reference points, respectively. The experimental assessment conducted on UCI datasets reports the proposal was able to produce sparser models and achieve competitive performance when compared to the regular strategy of selecting MLM input RPs.
The effects of multiple layers feed-forward neural network transfer function in digital based Ethiopian soil classification and moisture prediction

In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh.