Reviewerly/exHarmony huggingface dataset:
Emotion Correlation Mining Through Deep Learning Models on Natural Language Text

Emotion analysis has been attracting researchers' attention. Most previous works in the artificial intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recognized. Correlation among emotions contributes to the failure of emotion recognition. In this paper, we try to fill the gap between emotion recognition and emotion correlation mining through natural language text from web news. Correlation among emotions, expressed as the confusion and evolution of emotion, is primarily caused by human emotion cognitive bias. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural network models are presented. The emotion confusion law is extracted through orthogonal basis. The emotion evolution law is evaluated from three perspectives, one-step shift, limited-step shifts, and shortest path transfer. The method is validated using three datasets-the titles, the bodies, and the comments of news articles, covering both objective and subjective texts in varying lengths (long and short). The experimental results show that, in subjective comments, emotions are easily mistaken as anger. Comments tend to arouse emotion circulations of love-anger and sadness-anger. In objective news, it is easy to recognize text emotion as love and cause fear-joy circulation. That means, journalists may try to attract attention using fear and joy words but arouse the emotion love instead; After news release, netizens generate emotional comments to express their intense emotions, i.e., anger, sadness, and love. These findings could provide insights for applications regarding affective interaction such as network public sentiment, social media communication, and human-computer interaction.
The Errors Analysis of Students’ Grammatical Writing on Narrative Text

The purpose of this research was to know both the type of error and the most dominant error made by students in writing narrative text at the second grade students of SMK N 1 Sigli. This study is qualitative research as suggested by Noor (2011). The purpose of this research is to analyze the errors which actually committed by students on their writing. This research is a case study which clarifies the natural circumstance. The writer collect the data by using a set of written test. The procedures of Error Analysis (EA) adopted from Corder (1974) as cited in Ellis (2008:48). The data nanalized by using table of freqency the result showed that the error made by the students can be categorized into four types of errors. Those were errors of omission, errors of addition, errors of misformation, and errors of misordering. Another result showed that the most dominant error in writing narrative text made by the students was error of misformation. In describing the type of error, it can be inferred that there are 25 items of error, which occur in the students’ sentences.
Reflection principles, propositional proof systems, and theories

The reflection principle is the statement that if a sentence is provable then it is true. Reflection principles have been studied for first-order theories, but they also play an important role in propositional proof complexity. In this paper we will revisit some results about the reflection principles for propositional proofs systems using a finer scale of reflection principles. We will use the result that proving lower bounds on Resolution proofs is hard in Resolution. This appeared first in the recent article of Atserias and Müller as a key lemma and was generalized and simplified in some spin-off papers. We will also survey some results about arithmetical theories and proof systems associated with them. We will show a connection between a conjecture about proof complexity of finite consistency statements and a statement about proof systems associated with a theory.
Kompetensi Bahasa dan Kompetensi Komunikatif Peserta Didik

In everyday life, someone's language competence is very necessary. Language competence is one's mastery of the content contained in the language learned. Without language competence a person becomes irregular in language. The purpose of this study is to explain and find out about language competence or language skills and communicative competencies or one's appearance in interacting in language. Communicative competence concerns the social and cultural knowledge that speakers have to help them use and interpret linguistic forms. The problems that will be answered in this study use library research, which will answer what are the components and language competencies. Data collected from various references; both primary, secondary and supporting data. The results of the study show that there are five language competencies according to Canale and Swain namely linguistics, socio-linguistics, speech acts (speech acts), speech sets, strategies. In language competence one must master language skills, namely listening, speaking, reading and writing.
급조폭발물(IED)과 한국군 대응체계의 효율성 제고방안 고찰 -전·평시 軍의 예방·대응 체계를 중심으로-

Conventional terrorism was a means of propaganda, incitement and mass destruction to achieve political and religious purposes, but new terrorism is indiscriminately executed to an unspecified number of people. Especially, Korea has been designated as a terrorist target country by boosting the anti - Korean sentiment of international terrorists due to the activities of international peacekeeping forces. 10 out of 3,119 domestic incidents were caused by IED.The purpose of this article is to redefine the perception of the IED, which made the US, which has state-of-the-art equipment, strongest army, and professional staff, struggling in the war on terrorism. In addition, by comparing and analyzing the response system of major countries, it is necessary to enhance the effectiveness of prevention and response by recognizing the necessity of reinforcing detection, identification, discovery, elimination ability, education and training system, and integrated response system for IED. The scope of the study is to grasp the concept of IED terror and its actual situation and to raise a practical countermeasure.In this paper, IED is used as an independent variable, the Korean response system is used as a dependent variable, recognition, response strategy, response organization and mission performance system, As a parameter.It is saddened that IED-related research has been in a state of rampant since it began in 2008. Therefore, we intend to re-emphasize the strategic and operational concepts of IED in terms of contents and to give consciousness and collaboration of relevant functions by specifying areas that need improvement and reinforcement. Of course, it can not be overlooked that this study can not represent the whole and there are realistic limitations. Military precaution and response activities are roused. It has bright prospect that the mutual consensus among the related organizations (functions) will spread and the organic cooperation and co-operation will be strengthened.
Fully Dynamic Inference with Deep Neural Networks

Modern deep neural networks are powerful and widely applicable models that extract task-relevant information through multi-level abstraction. Their cross-domain success, however, is often achieved at the expense of computational cost, high memory bandwidth, and long inference latency, which prevents their deployment in resource-constrained and time-sensitive scenarios, such as edge-side inference and self-driving cars. While recently developed methods for creating efficient deep neural networks are making their real-world deployment more feasible by reducing model size, they do not fully exploit input properties on a per-instance basis to maximize computational efficiency and task accuracy. In particular, most existing methods typically use a one-size-fits-all approach that identically processes all inputs. Motivated by the fact that different images require different feature embeddings to be accurately classified, we propose a fully dynamic paradigm that imparts deep convolutional neural networks with hierarchical inference dynamics at the level of layers and individual convolutional filters/channels. Two compact networks, called Layer-Net (L-Net) and Channel-Net (C-Net), predict on a per-instance basis which layers or filters/channels are redundant and therefore should be skipped. L-Net and C-Net also learn how to scale retained computation outputs to maximize task accuracy. By integrating L-Net and C-Net into a joint design framework, called LC-Net, we consistently outperform state-of-the-art dynamic frameworks with respect to both efficiency and classification accuracy. On the CIFAR-10 dataset, LC-Net results in up to 11.9$\times$ fewer floating-point operations (FLOPs) and up to 3.3% higher accuracy compared to other dynamic inference methods. On the ImageNet dataset, LC-Net achieves up to 1.4$\times$ fewer FLOPs and up to 4.6% higher Top-1 accuracy than the other methods.
Association Mining for Super Market Sales using UP Growth and Top-K Algorithm

Frequent itemsets(HUIs) mining is an evolving field in data mining, that centers around finding itemsets having a utility that meets a user-specified minimum utility by finding all the itemsets. A problem arises in setting up minimum utility exactly which causes difficulties for users. By setting minimum utility underneath average, too many incessant itemsets will be generated, which in turn will make the mining process quite inefficient. No frequent itemsets will be found if the minimum utility is set too huge. The research focuses on generating frequent itemsets by using the transaction weighted utility of each product. While using UP growth methodology for discovering high utility items from large datasets it takes more time and consumes more memory due to which it is less efficient. So to overcome these drawbacks of UP growth we use the Top-K algorithm which makes it more scalable and efficient. Therefore, we use the Top-K algorithm which does not require a minimum threshold.
MoView Engine : An Open Source Movie Recommender

Recommendation is an ideology that works as choice-based system for the end users. Users are recommended with their favorite movies based on history of other watched movies or based on the category of the movies. These types of recommendations are becoming popular because of their ability to think and react as human brain. For this purpose, deep learning or artificial intelligence comes into picture. It is the ability to think as a human brain as give the output best suited to the end users liking. This paper focuses on implementing the recommendation system of movies using deep learning with neural network model using the activation function of SoftMax to give an experience to users as friendly recommendation. Moreover, this paper focuses on different scenarios of recommendation like the recommendation based on history, genre of the movie etc.
New Frontiers in Behavioral Strategy

Behavioral strategy has long been a central subject in organizational and strategy research. This panel symposium will highlight critical gaps in the behavioral strategy literature and propose future directions to advance our understanding for the human element in strategy. In particular, our distinguished group of panelists will discuss 1) intra-psychic mechanisms from the perspectives of cognitive and social psychology and neuroscience, 2) the situated and structural aspects of behavioral strategy in a multi-level context, 3) how firm strategists make decisions at the collective level and communicate with stakeholders, and 4) how to take advantage of soft data produced by intelligent systems (e.g. AI and social media) to augment human intelligence and enable smarter decision making.
THE PREDICTION OF NATIONAL EXAM SCORES OF JUNIOR HIGH SCHOOL STUDENTS USING K-NEAREST NEIGHBOR (k-NN) ALGORITHM

The prediction of the acquisition of national exam scores for Junior High School (JHS) students is intended to know the results of the student’s national exam early when students take the national examination. Knowledge gained from the results of this prediction will be important information for the school to take appropriate steps so that the acquisition of student national exam scores can be improved even better . The acquisition of student national  exam scores is low and there is no prediction model that is used to predict the achievement of student national exam scores is a problem that needs to be addressed . This paper propose a p redicting student’s national ex a m scores for four national exam subjects ( INDONESIA N, ENGLISH, MAT HEMATICS and SCIENCE ) using K-Nearest Neighbor (k-NN) as a prediction method and compare it with Decission Tree method . The results of the study showed that the prediction k-NN model had better performance than the prediction model of Decission Tree . Performance results obtained by evaluating using derivatives of the confussion matrix terminology to determine the value of accuracy, sensitivity (recall), and precission each subjects . To measure the performance of predictive methods used the value of accuracy in each method and each subject . The greater the accuracy value ( max 1 ), then the better performance of the prediction model used. Performance of k-NN in average accuracy=0.85, precision=0.87, recall=0.91 is better than Decission Tree method performance with accuracy=0.82, precision=0.85, and recall=0.89.
A Parallel Environment Designing for OWL Thinking

A huge volume of information available today is in the form of images and searching the wanted images is very difficult and highly time-consuming. The search may take longer periods as the search volume on the internet is very huge and also the relevance of extracted images is still not up to the mark. The technologies like ontology and languages like OWL help us to tag the images that describe the semantic of the images. Hence, it helps in faster searching of the wanted images. Also, another challenge with OWL and Semantic web is the speed in which one can derive the relationships between various objects extracted from the images. The challenge is to extract the semantic from the images more efficiently using a parallel approach. In this paper, we explore the different techniques for generating semantic knowledge using parallel approaches like the T-box approach, merge classification, extract concept for matching ontology. We propose an enhanced method to speed-up the computation by combining T-box and merge classification techniques.
SECURED IMAGE SYSTEM USING GRAYSCALE IMAGE ENCRYPTION

In latest years, there is a speedy development in the multimedia and network technologies in computer era. Transmission of multimedia information over the network leads the major problems of security, privacy and data size. Images are widely used and the major problems are how to protect the images and also reduce size of the image in order to maximize the network utilization. Various techniques are there with a view to secure the image and to reduce the size of the image. Security and privateness aren’t taken into consideration in the sooner Image compression techniques. To provide the privacy and security, the encryption is applied as well as compression reduces the data size. So that, to overcome the issues in multimedia and network technologies, compression is combined with encryption. In order to get higher network utilization, the encrypted images are compressed. An efﬁcient image Encryption Then Compression (ETC) system is designed. In proposed scheme Advanced Encryption Standard algorithm is used to encrypt the image with the intention to get high security.
i-LENUK: Sistem Pendataan Penyu Berbasis Aplikasi Bagi Pokmaswas Pantai Taman Kili Kili, Trenggalek, Jawa Timur

Taman Kili Kili Beach is a nesting place for turtles. There are Olive Ridley (Lepidochelys olivacea), Hawksbill Turtle (Deretmocelys imbricate), Green Turtle (Chelonian mydas)and Leatherback (Dermocelys oliacea) that found completing their natal homing to this area during the night. The beach area was conserved and managed by a group of local community that called as Pokmaswas Taman Kili-Kili Beach with consideration of the government at the village level. However, due to limitation of Pokmaswas human resources, turtle data tabulation process still conducted by filling sheet manually and it went ineffective and prone to lost. i-LENUK, an application-based on android that developed for turtle data tabulation and database system for the Pokmaswas of Taman Kili Kili introduced and applied to solved and increase effectiveness. Pokmaswas members have been trained as tabulator and administrator in the turtle data tabulation including verification and maintain data as well
A Hybrid of Random Over Sample Examples and Boosted C5.0 Algorithms for Breast Cancer Diagnosis on Imbalanced Data

To surmount the two-class imbalanced problem existing in the breast cancer diagnosis, a hybrid method of ROSE sampling approach with Boosted C5.0 ensemble classifier (R-Boosted C5.0) is proposed. ROSE as the sampling method is utilized to balance the class distribution. Boosted C5.0 is then used as the classifier. To serve this purpose, Wisconsin Breast Cancer Dataset (WBCD), Wisconsin Diagnosis Breast Cancer (WDBC) and three imbalanced datasets have been studied. Assessing by Matthews Correlation Coefficient (MCC), the performance of proposed method on WBCD and WDBC datasets were 98.5% and 93.0%, respectively. The experimental results show that the proposed work outperforms in contrast with the rest of the classifiers. It can be used as a clinical decision support system to assist breast cancer prediction. In practice, the proposed methodology can be further applied to class imbalanced data classification.
An Approach To Predict Software Application Success Using Voting Ensemble Method

Mobile app distribution platform consisting of Google play store and Apple Store gets covered with several hundreds of new apps every day with many more enthusiastic developers working independently or in a crew to make them successful. With huge competition from all over the world, it is vital for a developer to recognize if he is proceeding in the proper direction or not. It is not like making a film wherein presence of famous celebrities increase the chance of success even earlier than the movie is released, it is not the case with developing apps. Since maximum Play Store apps are free, the revenue version is pretty unknown and unavailable as to how the in-app purchases, in- app advertisements and subscriptions make a contribution to the fulfillment of an app. Thus, a software’s success is normally decided through the wide variety of installs and the star ratings that it has acquired over its lifetime in place of the sales it generated. So in order to test if the app is assembly the expectancy of human beings we need a software that will check that apps evolved are successful or not. The framework for predicting success of software application is proposed in this paper. It is a software which will provide the achievement of app/software program relying not only on number of install and star rating but will consider all the factors of an application description and customers reviews. The exploratory data analysis to jump in deeper into the Google Play Store information is performed. The relationships with specific functions inclusive of how the wide variety of phrases in an app call for instance, affect installs are used to use them to find out which apps are much more likely to succeed. Using those extracted functions and the of sentiment of customers the proposed method will predict the “success” of an application using Google Play Store Data. The algorithm applied are Support Vector Machine, K-Nearest Neighbour, Decision Tree and Random Forest. In order to improve accuracy the Voting Ensemble technique is applied in proposed method. The accuracy of various algorithm is compared to judge the performance of proposed method. Keywords : hyperplane, dataset, Support Vector Machine, Exploratory Data Analysis, voting ensemble
Using NGOMSL for Formative Feedback Generation in a Virtual Learning Environment

This research paper presents a feedback generation system using the Natural GOMS Language (NGOMSL) to describe the learner's tasks and performance expectation in a virtual reality-based learning environment.The acronym GOMS stands for Goals, Operators, Methods, and Selection Rules, and is derived from Card, Moran, and Newell's Human Processor Model.It is a representation of a series of "how-to's" required by a system in order to accomplish the intended tasks.NGOMSL is a variation of GOMS that provides an easy-to-read framework that highlights the underlying procedural rules of the task and the assessment of learner mastery of the task.A robust NGOMSL model will be able to provide the essential instructions regarding how the learner's perceptual, cognitive, and motor subsystems should work, and also improve the learner's ability to complete the task with proficiency.In this paper we first use the domain of industrial robot programming as an example to discuss the difference between the Virtual Reality (VR) based learning environment and the traditional learning environment.Second, using a typical task in industrial robot programming as the point of discussion, our previous work in GOMS and its limitation are discussed.Next the rationale of using NGOMSL to model the procedural knowledge in a VR-based learning environment is presented, along with a comparison of using the NGOMSL model for the same task previously described using the GOMS model.The preliminary results of time duration measurement is presented, along with the discussion of how the proposed feedback system will work.This paper will conclude with a discussion of future work needed to implement the NGOMSL instructional models in a classroom setting.
Predicting the convergence of BiCG method from grayscale matrix images

The convergence of the BiConjugate Gradient (BiCG) method depends on its input matrices. We tried to predict the convergence of BiCG method by applying a Convolutional Neural Network to matrices that had been converted to grayscale images. Using 875 real non-symmetric matrices in the SuiteSparse Matrix Collection, we applied the 5-fold cross-validation method and were able to predict convergence with an average accuracy that exceeded 80\% for all cases in the test collection.
An Extension of the Quadratic Error Function for Learning Imprecise Data in Multivariate Nonlinear Regression

Multivariate noises in the learning process are most of the time supposed to follow a standard multivariate normal distribution. This hypothesis does not often hold in many real-world situations. In this paper, we consider an approach based on multivariate skew-normal distribution. It allows for a multiple continuous variation from normality to nonnormality. We give an extension of the generalized least squares error function in a context of multivariate nonlinear regression to learn imprecise data. The simulation study and application case on real datasets conducted and based on multilayer perceptron neural networks (MLP) with bivariate continuous response and asymmetric revealed a significant gain in precision using the new quadratic error function for these types of data rather than using a classical generalized least squares error function having any covariance matrix.