Reviewerly/exHarmony huggingface dataset:
SEQUENCE MINING FOR COMPLEX PATTERN FINDING

Abstract The processes of aging play out across multiple variables and multiple timescales, with patterns of daily, and weekly behavior that may be influenced by each other and by changes across the aging process. Further, many of these patterns do not fit neatly into the linear modeling approaches common in the field. Sequence mining, an approach from the data mining literature, provides a means of identifying commonalities and differences in these sequences in ways that can begin to handle the multivariate and multi-timescale nature of behaviors in aging. In this talk, I present an example of sequence mining to illustrate its ability to find arbitrarily complex patterns of behavior that characterize and distinguish groups and individuals.
Metode Dan Media Promosi Kesehatan Terhadap Perubahan Perilaku Pengobatan Penderita TB Paru Di Wilayah Puskesmas Putat Jaya Kota Surabaya

Pulmonary Tuberculosis is still a world health problem, where the World Health Organization (WHO) reports that most of the world's population is stricken with this disease, mostly in developing countries, estimated in Indonesia. Efforts to control pulmonary TB are carried out nationally with the DOTS (Directly Observed Treatment Shourt Course), this program is a direct monitoring of short-term treatment. The type and design of the research used in this study were experimental with the pattern of posttest-only control group design. The number of samples in the study were 27 people each group, so the number of respondents was 54 people. The results of the study using the Mann Whitney test showed that the knowledge variables of Tuberculosis sufferers were assessed by the average rating of 32.48 and the ranking control group an average of 22.52 with a difference in the delta value of 9.96, while the value of ρ was 0.016 . On the attitude variable of pulmonary tuberculosis patients the average treatment group is 31.48 and in the ranking control group an average of 23.52 with a difference in the value of delta 7.96, while the value of ρ value is 0.061 <0.05. In the behavioral variable of patients with pulmonary TB the treatment group ranked 32.51 and the mean control group was 30.22 with the difference in the delta value of 2.29 while the ρ value was 0.024 <0.05. Based on the results of research that can be interpreted that this is a difference of opinion between groups related to the control group, while in the knowledge variable and groups related to control groups, it can refute what is meant by promotional promotions with lecture methods and provide leaflets to changes in behavior of pulmonary Tuberculosis treatment.
INFLUENCE OF THE QUALITY OF MC.GAZINE TOWARDS STUDENTS READING INTEREST OF SMA NEGERI 3 MAGELANG

The students of SMA Negeri 3 Magelang have low interest in reading to their own printed media, that is Mc.Gazine. The low reading interest affected by several factors, one of them is the quality of the product. Therefore, this research aims to figure out the influence of the quality of Mc.Gazine towards students’ reading interest of SMA Negeri 3 Magelang. The method used in this research was quantitative survey. The sample taken was approximately 35% of the whole population. Data collection applied in this research was questionnaire. Validity and reliability test were used to test the items in the questionnaire. Meanwhile, the data were analyzed using product moment Pearson correlation and simple linear regression. The results of the research were elaborated using S-O-R (Stimulus-Organism-Respond) Theory and Uses and Gratification Theory. To describe the results of the research, it was found out that the quality of school magazine Mc.Gazine towards students’ reading interest was at the medium state or category. It can be concluded that both variables i.e independent and dependent, have strong relationship. It is proven by the correlation coeficient value at 0,624 nearly to 1. On the other hand, the quality of Mc.Gazine (variable X) has positive significant influence towards the reading interest of the students of SMA Negeri 3 Magelang (variable Y) with the value at 0,000 < 0,05. Then, it was also found out that variable X gave contribution for approximately 39% on variable Y, therefore, the equation will show that when the variable contribution of variable X increases, then variable Y follows.
Guides: Borrow and Request Materials: Billing

Borrowing UNCSA materials, lending policies, and billing; Purchase Requests and Interlibrary Loan.
Semi-Supervised Induction of POS-Tag Lexicons with Tree Models

We approach the problem of POS tagging of morphologically rich languages in a setting where only a small amount of labeled training data is available.We show that a bigram HMM tagger benefits from re-training on a larger untagged text using Baum-Welch estimation.Most importantly, this estimation can be significantly improved by pre-guessing tags for OOV words based on morphological criteria.We consider two models for this task: a character-based recurrent neural network, which guesses the tag from the string form of the word, and a recently proposed graph-based model of morphological transformations.In the latter, the unknown POS tags can be modeled as latent variables in a way very similar to Hidden Markov Tree models and an analogue of the Forward-Backward algorithm can be formulated, which enables us to compute expected values over unknown taggings.We evaluate both the quality of the induced tag lexicon and its impact on the HMM's tagging accuracy.In both tasks, the graph-based morphology model performs significantly better than the RNN predictor.This confirms the intuition that morphologically related words provide useful information about an unknown word's POS tag.
Practical yet Provably Secure: Complex Database Query Execution over Encrypted Data

Encrypted databases provide security for outsourced data. In this work novel encryption schemes supporting different database query types are presented enabling complex database queries over encrypted data. For specific constructions enabling exact keyword queries, range queries, database joins and substring queries over encrypted data we prove security in a formal framework, present a theoretical runtime analysis and provide an assessment of practical performance characteristics.
PENGARUH MATHMAGIC DENGAN MODEL PEMBELAJARAN SCRAMBLE TERHADAP HASIL BELAJAR SISWA

Abstract. Learning outcomes are a measure of the success of students in learning the material delivered by the teacher. Data on students 'mathematics learning outcomes obtained at Bilah Hulu 2 High School showed that students' mathematics learning outcomes were relatively low so it was necessary to improve them, especially on mathematics subjects. The objectives of this research were to find out knowing the effect of mathmagic on the scramble learning model on student learning outcomes in the subject matter of Inverse Functions. This type of research is quantitative research using The One Group Pre-Post Test test Design. The research instruments used tests of student learning outcomes in the form of multiple choices. Data were analyzed by the T-Test, namely Paired Sample T-Test. The results average learning outcomes of class X MIA 1 Bilah Hulu 2 High School before being taught using mathmagic with a scramble learning model of 10.14 and a standard deviation of 3.563 and after being taught using mathmagic with scramble learning model of 16, 72 and standard deviation 2,987. Because the data obtained from the results of the t-test at a significance level of 0.05 is t count> t table that is 16.784> 2.045 then Ha is accepted. From these results, it can be concluded that there is a mathmagic influence with the scramble learning model on student learning outcomes by matching the question cards and answer cards available in random circumstances. Keywords: Mathmagic, Scramble, Learning Outcomes
Service Quality Measurement and Competition Strategy Analysis of Online Shopping

E-Commerce refers to such business behavior as conducting trades on a virtual network through the Internet. The core elements contributing to the success of online stores include not only the web-based presentation and the low-price strategy but the quality of online service as well. Importance-Performance Analysis is a method frequently used to evaluate performance and analyze competition strategies. Using IPA, this study analyzed and investigated the service quality of the online shopping of a case company and proposed 10 indexes to measure the service quality of online shopping. a number of suggestions on competition strategies were provided for the case company to improve its competition strategies.
Organization of Lifelong Learning in the System of Professional Training of Teachers

The relevance of the present issue is caused by the need for carrying out lifelong learning of teachers as an essential condition of their professional development. The aim of the article is to prove the necessity of integrating formal and informal professional and optional development of educators. The main methods of the research are analysis and synthesis of the data obtained. The authors hold an analysis of the lifelong learning development in the EU countries and in Russia, which can be formal and informal. The latest represents an entirely new phenomenon in the social and educational practice. Its content bases on their own principles, carries out specific functions and solves existing problems in an innovative way. The article also views the strategies and models of lifelong professional education of teachers being under a significant pressure of informational and communicational technologies and relying on informal mechanisms of giving knowledge and forming competences. In the research part of the article the objective and subjective difficulties of teachers and their professional needs are revealed. Lifelong learning is bound to overcome these difficulties and satisfy the necessity of forming and developing new professional competences. The analysis of the documents of educational institutions shows the dominance of informal education. It proves the need for defining the status of informal education as a constituent part of professional development of an educator.
Program Pelatihan Penulisan Artikel Ilmiah: Menuju Guru Berkualitas

PROGRAM OF SCIENTIFIC ARTICLE WRITING TRAINING: TOWARDS QUALIFIED TEACHERS. Writing scientific articles is one of teacher’ efforts to improve their quality and develop their professionalism. Unfortunately, their reading interest and wriitng habit are still low. This makes the teachers have difficulties when they have to submit a promotion because they do not have enough background knowledge and understanding in writing scientific articles, while one of the requirements for submitting a promotion is to publish a scientific article. Therefore, a training on scientific article writing was held for teachers in Situbondo to provide knowledge and skills for them to write scientific articles. After participating in this activity, teachers are expected to be able to publish scientific articles and be able to improve the quality and professionalism of their work so that the quality of education becomes better.
Sistem Pengelolaan Konten Berbasis Android Untuk Mempermudah Pembuatan Aplikasi Augmented Reality

Augmented Reality (AR) technology was discovered in 1957. But in fact there are still many who do not know Augmented Reality (AR) because they must master programming languages and some Augmented Reality (AR) application building platforms. Therefore, to introduce and facilitate the creation of Augmented Reality (AR) we create an Android-based Application that makes it easy for users to recognize and create Augmented Reality (AR) applications using Vuforia Cloud Recognition, Unity and PHP that can support Augmented Reality (AR) applications. This application is expected to facilitate users to get to know and make Augmented Reality (AR)
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Deep Unsupervised System Log Monitoring

This work proposes a new unsupervised deep generative model for system logs. It is designed to be generic and may be used in various downstream anomaly detection tasks, such as system failure or intrusion detection. It is based on the (reasonable) assumption that most log lines follow rather fixed syntactic structures, which enables us to replace the costly traditional convolutional and recurrent architectures by a much faster component: a deep averaging network. Our model still exploits a standard recurrent model with attention to capture the dependencies between successive log lines. We experimentally validate the proposed generative model on a real dataset obtained from a state-of-the-art High Performance Computing cluster and show the effectiveness of the proposed approach in modeling the “normal” behaviour of the system.
MEDIA COMPETENCE AS A FACTOR IN THE DEVELOPMENT OF THE INNOVATIVE CULTURE OF THE FUTURE MANAGER OF THE EDUCATIONAL INSTITUTION

The competency-based approach is postulated as the main idea of the Bologna process and acts as a conceptual basis for the development and implementation of existing federal state educational standards of higher education.It also lies at the basis of their current updating and development of the such standards fourth generation in the future.The main task of scientific research in the framework of the competency-based approach is studying the competencies essence and structure and develop effective ways of their formation and self-development.The innovative approach to the self-designing of media competence of future manager of educational institution based on its invariants article discloses.Such competency invariants are revealed and described, reflecting the typical methods of the teacher's actions in working with professionally significant information, and the corresponding groups of knowledge and skills.The possibilities of self-designing technology in the media competency formation are shown.An innovative approach to the self-designing of media competence using the potential of its invariants is proposed, which allows designing the training process and professional self-development process based on media competence invariants and at the same time taking into account the students' -future managers' of the educational institution training level (area of current and near development).
OORT: An Air-flow based Cooling System for Long-term Virtual Reality Sessions

In this paper, we present OORT, a cooling system for head-mounted displays (HMDs) that improves wearing comfort by decreasing skin temperatures of the facial areas covered by the headset. The integrated cooling system consists of an electronically controlled fan blower. The fan compartment is integrated into an hmd padding element with custom-designed air flow channels that provide cool air circulation around the covered facial regions. We report on the design and implementation of OORT as a viable way to provide thermal comfort during long-term virtual reality experiences.
Applying different Feature Selection and Classification Parameters for Categorization

In today's data-intensive world, millions of data is generated, processed and transferred, The main factors for the generation of data is an increase in the usage of social media and so is the increase in data mining methodologies.Text Classification is one of the most important aspects of data mining which includes fetching of data, pre-processing it and then applying classifiers to divide the data into the categories so as it would be easy to process and subject to further experimentation.In this paper, data is subjected through certain feature selection techniques enhancing its parameters and then applied multiple Machine Learning classifiers on it so as to study various parameters of the data which include accuracy, precision and various averages.The impact of increasing or decreasing the categories for classification of text on accuracy through various classifiers is studied which include Naive Bayes, Support Vector Machine and K-Nearest Neighbour and also the combination of individual classifiers in an ensemble classifier.In this research the internal parameters of Feature Selection Techniques and classifiers are also changed which lead to a slightest increase in overall accuracy of the classifier.Reducing different categories also increases accuracy to a greater extent because it also reduces the presence of multiple similar categories which lead to decrease in overall accuracy.Certain changes in the feature selection parameters are also included which is trying algorithms on uni-gram, bi-gram and tri-gram models and out of which bi-gram shows the best overall accuracy result with Support Vector Machine classifier.
A residual dictionary learning method for footprint removal from seismic data

We introduce a novel dictionary learning strategy for removal of footprint patterns and random noise in seismic data.To this end, we construct an augmented dictionary based solely on the atoms learned from the coherenceconstrained dictionary learning (CDL), a method that is very effective on attenuating random noise.It turns out that when seismic data is contaminated with acquisition and/or processing footprint, the atoms of the learned dictionary are contaminated by coherent noise patterns.Hence, it is necessary to carry out a morphological and/or texture attribute classification of the atoms for effective footprint removal.Instead, the method that we propose relies on an augmented dictionary that is constructed using a simple data-driven empirical mode decomposition (EMD) algorithm, which leads to a dictionary that contains signal atoms and a residual dictionary that contains footprint atoms.This avoids the use of complex statistical classifications strategies to segregate the atoms of the learned dictionary.As in CDL, the proposed method does not require the user to know or adjust the noise level or the sparsity of the solution for each data set.Further, it only requires one pass of CDL dictionary learning and is shown to produce successful transfer learning results in field data.This leads to a speed-up of the denoising processing, since random and coherent noise can be removed without calculating the augmented dictionary for each time slice of the 3D data volume.Results on field data demonstrate effective footprint removal with accurate edge preservation on time slices of 3D seismic poststack data.
Label consistent transform learning for pattern classification

Transform learning has been successfully applied to various image processing tasks in recent years. Nevertheless, transform learning learns the representation in an unsupervised fashion. To make transform learning suitable for pattern classification, we introduce a label consistency constraint into transform learning and propose a new label consistent transform learning to enhance the classification performance of transform learning. The resulting optimization problem can be solved elegantly by employing the alternative strategy. Experimental results on publicly available databases demonstrate that label consistent transform learning outperforms several dictionary learning approaches and the recently proposed discriminative transform learning. More importantly, label consistent transform learning has the least training time which has the potential in practical applications.
A Hybrid Approach for Opinion Mining Using Twitter Data

Abrupt and fast change of technology give birth to research areas which are directly related to users. NLP is such type of emerging research area in which opinions of users using technology play the important role. As with advancement in social sites people post every information. These posts are related to perspective of user about some company service, regarding any political party and review related to entertainment industry. In NLP, opinion mining is considered as important field because it directly related to society opinion. It can be described as that field in which conclusion of posted opinions are extracted. Since posted messages are short in nature and it results sparse feature matrix giving less values of performance matrix. In this research paper we have proposed Optimised Feature Opinion Classifier for Big Data termed as OFOCBD which works on the principle of optimizing the classifiers giving improved values of performance matrix. In this research paper our proposed algorithms have shown better results as the data is converted into big data.
PENGARUH MODEL PEMBELAJARAN CROSSWORD PUZZLE TERHADAP HASIL BELAJAR IPATENTANG MORFOLOGI TUMBUHAN DI KELAS IV

The purpose of this research is to determine the crossword puzzle learning model on the result of student learning of  Natural Science Subject in 4th grade of 256th State Elementary School, Palembang. The method used in this research is a pre-experimental method with the design of the One Group pretest-posttest design. The sample of this research is 36 students from grade IVA 256th State Elementary School, Palembang which consists of 18 male students and 18 female students. The research instrument used is a written test in the form of multiple choice questions as many as 20 questions. Based on the results of the analysis prerequisite data for pretest and posttest data that the data is normally distributed. Therefore the research hypothesis was tested using the t test . After the research hypothesis was tested by the t test obtained  = 16.56 while from the distribution table t obtained  = 1.690 so that it appears that . Accordingly, it is H0 rejected and Ha accepted. Then, it can be concluded that the crossword puzzle learning model has an effect on the result of student learning of Natural Science Subject in  grade IVA of 256th State Elementary School, Palembang.
On the Elasticity of Parallel Components in a Cloud of High Performance Computing Services

Cloud computing offers virtually unlimited set of resources and flexibility to allocate them through elasticity. But cloud limitations, such as the complexity of configuration and environment dynamicity, may jeopardizes the assurance of QoS requirements. HPC Shelf is a cloud of HPC services that employs a component-oriented architecture to describe hardware and software resources of parallel computing systems. We design a framework for HPC Shelf that employ cloud elasticity concepts for keeping the values of QoS metrics of parallel computing systems inside an acceptable range, enabling adaptations to fulfill the QoS contract restrictions. In our evaluation, using a linear algebra application, we show how HPC Shelf takes advantage of cloud elasticity to reinforce QoS requirements, rectifying assumptions from ill-defined QoS models.
Building the Assessment Toolkits to Assess Collaborative Problem Solving Competence through Teaching Chemistry of the Non-metal

Using theoretical research, this article presents the structure of student’s collaborative problem solving competence. Based on the results published by Patrick's team in 2014, 2015 and OECD 2015, the author proposes 4 levels corresponding to each specific criterias of three component competencies: (1) establishing and maintaining shared understanding, (2) taking appropriate action to solve the problem, (3) establishing and maintaining a team organization. Based on this theory, the paper also proposes a matrix of assessment toolkits and corresponding criterias of collaborative problem solving competence teaching chemistry of the non mental using interdisciplinary integrated teaching perspective and Kolb's Experiential Learning Cycle. Each assessment tool specifically analyzes the functions and techniques implemented in students' collaborative problem solving processes