Reviewerly/exHarmony huggingface dataset:
A MULTI CRITERIA RECOMMENDATION MODEL FOR JAUNT

Abstract. Nowadays, people in most parts of the world always visit, travel and have fun in their cities or other cities, and they spend considerable time and money in their city or in other cities as a tourist. The existence of an intelligent and automated system that can provide the most suitable recreational and cultural offerings at any time and place, with regard to financial capability and time and transport constraints, as well as individual interests and personalization; has always been felt. Recommender systems can be used to suggest suitable recreational options for the user. The main difference between the recommendation model in this study and the previous models is to focus on the short-term planning of a few hours for one day. Previous models were often based on planning a few days a week or days of the month. Also, the cost factor has been considered in this research, which has been less considered in previous models. We used collaborative filtering based on logistic regression to predict whether a type of places is a proper proposition to a user or not. Our case study is about recommending the board game cafés in the city of Kerman, Iran and the result shows that mixed groups between 15 to 30 years old are the best target and our model can predict if board game café is a good suggestion to different users. We used correlation based recommender systems when board game cafes are a proper suggestion for a user and there are at least two options for the user. In case there is no information about the user and his previous rating, popularity based recommender system can be useful. We also used content based recommender systems to give recommendations by having some background information about previous itineraries of a user and his rating to those.
Frameworks Comparative study of Classification Models based on Variable Extraction Model for Status Classify of Contraception Method in Fertile Age Couples in Indonesia

In terms of minimizing the risk of death in mothers the use of contraceptive methods really needs to be improved and the success of the use of contraceptive methods. This study aims to compare several popular classification models used to classify the status of the use of contraceptive methods in fertile age couples in Indonesia so that they can be used and the implementation of policies that are more impartial using the variable extraction integration method. The proposed model in this study is a comparative study of classification models include Logistic Regression (LR), k-Nearest Neighbor (k-NN), Naïve Bayes (NB), C4.5, and CART. For the purpose of testing the model, Accuracy, AUC, F-measure, Sensitivity (SN), Specificity (SP), Positive Predictive Value (PPV), and Negative Predictive Value (NPV) are used to test frameworks comparative study of classification models. Based on the experimental results, RL shows superior and stable performance compared to other methods. It can be concluded, the RL method is the right choice method to classify the status of use of contraceptive methods in couples of childbearing ages in Indonesia.
The Collection Assessment is Done…Now What?

About collection managementThe methods used to manage collections have changed dramatically in the last half century, a phenomenon that has been well documented in the literature. 1 This has been due in part to a reaction to the rapid growth of information resources, 2 increased costs of acquiring these resources coupled with decreased share of institutional funding towards libraries, 3 and the shift to digital formats, resulting in changes in methods of making these resources accessible to library patrons.Some in the field have gone so far as to suggest that collection management is undergoing a "paradigm shift." 4 These changes have increased the need for information about the collections themselves, notably inputs (costs and needs), outputs (purchases/acquisitions, circulations, and uses), and outcomes (citations, student grades, and faculty grant successes). 5 About collection evaluationCollection analyses, evaluations, and assessments are an important aspect of collection development services provided by librarians.Collection evaluation "encompasses analysis of the library's collection, its use, and ultimately its impact," with the "real objective" being not to measure a collection's quality, but rather its "utility-how effective the collection is in satisfying the purpose for which it is intended." 6This emphasis on outcomes reflects the growing trend of assessment in librarianship as a whole, which is well summarized by Megan Oakleaf in her seminal work, The Value of Academic Libraries.In this report, Oakleaf recommends that librarians go beyond traditional measures of inputs and outputs, (primarily acquisitions [costs and counts]) and use data, and instead examine the impact that library services, including collections, have on student and faculty outcomes. 7
AUTOMATIC VEHICLE RECOGNITION FOR URBAN TRAFFIC MANAGEMENT

Abstract. Automatic vehicle recognition has an important role for many applications such as supervision, traffic management and rescue tasks. The ability of online supervision on the distribution of vehicles in urban environments prevents traffic, which in turn reduces air pollution and noise. However, this is extremely challenging due to the small size of vehicles, their different types and orientations, and the visual similarity to some other objects in very high resolution images. In this paper, an automatic vehicle recognition algorithm is proposed based on very high spatial resolution aerial images. In the first step of the proposed method, by generating the image pyramid, the candidate regions of the vehicles are recognized. Then, performing reverse pyramid, decision level fusion of the vehicle candidates and the land use/cover classification results of the original image resolution are performed in order to modify recognized vehicle regions. For evaluating the performance of the proposed method in this study, Ultracam aerial imagery with spatial resolution of 11 cm and 3 spectral bands have been used. Comparing the obtained vehicle recognition results from the proposed decision fusion algorithm with some manually selected vehicle regions confirm the accuracy of about %80. Moreover, the %78.87 and 0.71 are respectively the values for overall accuracy and Kappa coefficient of the obtained land use/cover classification map from decision fusion algorithm.
Adversarial Domain Adaptation for Chinese Semantic Dependency Graph Parsing

The Chinese Semantic Dependency Graph (CSDG) Parsing reveals the deep and fine-grained semantic relationship of Chinese sentences, and the parsing results have a great help to the downstream NLP tasks. However, most of the existing work focuses on parsing in a single domain. When transferring to other domains, the performance of the parser tends to drop dramatically. And the target domain often lacks the annotated data, so it is difficult to train the parser directly in the target domain. To solve this problem, we propose a lightweight yet effective domain adaptation component for CSDG parsing that can be easily added to the architecture of existing single domain parser. It contains a data sampling module and an adversarial training module. Furthermore, we present CC SD, the first Chinese Cross-domain Semantic graph Dependency dataset. Experiments show that with the domain adaptation component we proposed, the model can effectively improve the performance in the target domain. On the CCSD dataset, our model achieved state-of-the-art performance with significant improvement compared to the strong baseline model.
Struktur Percakapan Guru dengan Siswa dalam Interaksi Pembelajaran Bahasa Indonesia

The 2013 curriculum is a revision of ideas derived from the previous curriculum where students’ active performance is imposed in study. One of students’ physical activities expected from Indonesian language instruction is speaking or actively involving in class conversation. Therefore, this study aims at describing the structure of teachers and students’ conversation in Indonesian language teaching interaction. This is a descriptive qualitative study. The techniques of data obtaining are recording and taking notes, and the technique of data analysis is heuristic. The result of this study shows the conversation structures in Indonesian language instruction are 1) turn taking occurred when there is a chance to speak. 2) pausing (long and short) occurred since there is no response and when there is hesitation in delivering messages. 3) overlapping occurred since there is a similar utterance or language element produced simultaneously. 4) backchanneling occurred since there are comprehension and acceptance or agreement towards utterances, and 5) adjacency pairs occurred since there are greetings, questions, and requests. &#x0D; Keywords: conversation structure, interaction in teaching
Language Learning at Natural Disaster Area in Elementary Schools

Abstract&#x0D; One of the challenges in designing language learning in elementary schools is the ability to predict the skills of learners. To meet this need the Ministry of Education and Culture in designing the national exam which is one of the standardized tests in Indonesia. The use of multiple choice in national exams has an efficient basis for the correction of results after the exam is over. You can imagine homework from ministry of Education and culture on the national curriculum. The complex case faced by Indonesia today is the location of Indonesia. Geographically, the Eurosasia and pacific plates are surrounded by active volcanoes. For this reason language learning is preferably related to the literature of children with the teaching method of copy by master creative writing.&#x0D; &#x0D; Keywords: Elementary School, Natural Disaster, Copy By Master, 21st Century Challenge
Students Reading Competence For Detail Information In Expository Text

The purpose of this reseach to know how the students competence on their reading comprehension especially their ability in getting detailed information in expository text. The population of this research is 10 students of class X Akutansi SMK Modellink Makbusun. The research used descriptive analysis design. The instrument of this research was the reading test which focused on detail information in expository text. The result of students competence in getting detail information in expository text was 60% or 6 students got score among 70-100 and it was catagorized into good score. The other students or 2 students got in catagorized at “poor” catagory. The average of score students was 71.6. The competence of students in getting information in expository text was catagorized into “good” category.
AUTOMATIC FIRE EXTINGUISHING SYSTEM USING "T", "Z", "O" LETTERS TECHNIQUE BASED ON A PARALLEL STRUCTURE

This paper presents an automatic system based on a parallel structure that can be used to optimize firefighting using the technique of letters „T”, „Z”, „O” . The system, which is in the CAD optimization stage, uses electrically driven linear actuators to direct the water jet, so that the water consumption is reduced, the material damage is minimized without affecting the efficiency of the extinguishing operation.
A Study on the Framework of Public Service Development for Social Problem Solving: Focused on the Performance Management System of Governmental Social Problem-solving R&D Projects

This study presents the framework of govermental R&D project solving social problem. To this end, the research reviewed the characteristics of R&D activities to solve social problems, and compared with existing national R&D projects. As a result, the social problem solving R&D framework was derived around the performance management system.BR The study found that R&D for social problem resolution has characteristics that are carried out around Living Lab, a user-centered R&D activity, and thus requires the introduction of a performance management system centered on usability evaluation. And it has been found appropriate to adopt SROI-centered social value measurement methods to measure the social impact of R&D performance. The R&D framework presented in the study is expected to provide a foundation for the effective development of social problem solving public services.
The Effect of Apps on the Social Media (Facebook) on the Purchasing Decision in the Iraqi Community

The study seeks to explain the impact of social media advertising (Facebook) on the decision to buy the Iraqi public, as a result of the spread of this site, and give a scientific explanation of the impact of the site (Facebook) in this direction.This study comes within the descriptive research that uses the survey method and by default on the same sample of the city of Sulaymaniyah number (100) single.The researcher adopted the observation and questionnaire as the main tools for data collection, and the research section into four topics. The first topic was devoted to the methodological framework of the research. The second topic dealt with the declaration and stages of development. The third topic was devoted to the social networks. The fourth topic included the applied study. The study of the results of the most important: that advertising through social networks (Facebook) does not contribute significantly to the decision-making of the purchase of the Iraqi public, and the results proved that the declaration can achieve the desire of the public in the exposure to technology goods Speaking mobile telephony,. And computers
Modification of Keyword Selection Process to Get Least List with Weighted Keywords by using Essence of both ‘Baxendale’ and ‘Swanson’ Experiment

The 'keyword-based search system' by its name implies that the document is presented with the correct keyword.Keyword-content relations need to be thoroughly analyzed in order to select a document's keywords.In this case, the current method used, even after the use of ontology, has not been adequate.That's why many keywords are being selected for a particular document.And users are bounded to use all those keywords in search of a single document, resulting in a decrease in the precision of retrieval day by day.So in this case it is not possible to get high precision and to reduce quantity of keywords without selecting high quality keywords.The purpose of this paper is to examine how informative a particular keyword is, and to select the highest and most informative keyword and to exclude the least informative keyword through which, 'keyword-based search system' can get fewer but higher quality or weighted keywords as a helping hand to solve 'High Recall and Low Precision'.
PENINGKATKAN KEMAMPUAN BERHITUNG SISWA KELAS III SD MELALUI PERMAINAN LOMPAT KELINCI

The aim of this clas’s action research was intended to increase the numeracy skills of student 3rd grade through to game of rabbit jumping. This research was focus on the 3rd grade of SD GMIT Niki-Niki in the 2018/2019 school year. The analysis on the data which collected through test and interviews showed that the numeracy skills of student 3rd grade improved through the game of rabbit jumping.
A Variational Bayesian algorithm for Extended Target Tracking with Unknown Measurement Noise

The problem of extended target tracking in an unknown non-Gaussian noise field is considered. A Student-t distribution is used to model the measurement noise statistics that combining the sensor error with the extended uncertainty. An improved variational Bayesian algorithm is proposed to estimate the system states and the parameters of the measurement noise. Simulation results show that the proposed method has improved the estimation performance of the extended target tracking system compared with the traditional approaches.
Augmented reality as the medical electronic equipment inventory identifier

&lt;p class="abstract"&gt;&lt;strong&gt;Background:&lt;/strong&gt; Inventory systems are very numerous and require a long time when rechecking. In doing a shortcut time for checking, it can be done the application of augmented reality.&lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Methods:&lt;/strong&gt; This research can be applied to all types and brands of smartphones. The testing was carried out using three different types and brands of smartphones in three conditions: dark rooms, bright room and open spaces for three times for each smartphone. It examines the suitability of displaying asset objects such as Infusion Device Analyzer (IDA-1), Electrical Safety Analyzer (ISA) and Piranha radiology high voltage tester with target objects using existing inventory labels also Barbarian with the target a book cover with the distance 10, 15, 20, 25 and 30 cm. &lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Results:&lt;/strong&gt; By using three different types and brands of smartphones: A, B and C, the incompatibility result percentage of the asset object with the target object is obtained. The average of incompatibility result of Smartphone A, B, and C for the ISA asset object is 83%. The average of incompatibility result of Smartphone A, B, and C for the IDA asset object is 71%, The average of incompatibility result of Smartphone A, B, and C for the Piranha asset object is 36%, For the whole result for Barbarian object asset is 100%.&lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Conclusions:&lt;/strong&gt; Making augmented reality has been successfully carried out with 3D models but there is a discrepancy between the appearance of asset objects with the target object except for the book cover and barbarian.&lt;/p&gt;
Improving Relation Extraction with Relation-Based Gated Convolutional Selector

Distant supervision is an effective way to collect large-scale training data for relation extraction. To better solve the wrong labeling problem accompanied by distant supervision, some methods have been proposed to remove noise sentences directly. However, these methods seldom consider the relation label when removing noise sentences, neglecting the fact that a sentence is regarded as noise because the relation it expresses is inconsistent with the relation label. In this paper, we propose a novel method to improve the performance of bag-level relation extractor via removing noise data with a relation-based sentence selector. Specifically, the relation-based gated convolutional unit of the sentence selector can selectively output features related to the given relation, and these features will be used to judge whether a sentence expresses the given relation. The sentence selector is trained with the data automatically labeled by the relation extractor, and the relation extractor improves its performance with the high-quality data selected by the sentence selector. These two modules are trained alternately, and both of them have achieved better performance. Experimental results show that our model significantly improves the performance of the relation extractor and outperforms competitive baseline methods.
Contribution of Branching Order of Dendrites to Morphology of Neural Cells

It is currently believed that the morphology of dendrites depends on two main factors, namely the material of the cytoplasm and the path length of the signal before reaching the soma.In the present work the branching order is introduced as a third factor; it represents the variation of electrical resistivity of different dendritic segments.A mathematical modification of the optimum cost function is applied.The software package TREES toolbox is employed to reconstruct several types of cells, specifically LPTC, HSS, HSN and starburst amacrine.The effectiveness of the present analysis is assessed by both the electrotonic and Sholl footprints.The results indicate a higher degree of matching between the synthesized reconstructions of these cells and their original morphology.Moreover, a reduction in the storage memory of their in silico morphologies is achieved.
Adaptive Attention Annotation Model: Optimizing the Prediction Path through Dependency Fusion

Previous methods build image annotation model by leveraging three basic dependencies: relations between image and label (image/label), between images (image/image) and between labels (label/label).Even though plenty of researches show that multiple dependencies can work jointly to improve annotation performance, different dependencies actually do not "work jointly" in their diagram, whose performance is largely depending on the result predicted by image/label section.To address this problem, we propose the adaptive attention annotation model (AAAM) to associate these dependencies with the prediction path, which is composed of a series of labels (tags) in the order they are detected.In particular, we optimize the prediction path by detecting the relevant labels from the easy-to-detect to the hard-to-detect, which are found using Binary Cross-Entropy (BCE) and Triplet Margin (TM) losses, respectively.Besides, in order to capture the inforamtion of each label, instead of explicitly extracting regional featutres, we propose the self-attention machanism to implicitly enhance the relevant region and restrain those irrelevant.To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.
A Top-Down Model for Character-Level Chinese Dependency Parsing

This paper proposes a novel transition-based algorithm for character-level Chinese dependency parsing that straightforwardly models the dependency tree in a top-down manner. Based on the stack-pointer parser, we joint Chinese word segmentation, part-of-speech tagging, and dependency parsing in a new way. We recursively build the character-based dependency tree from root to leaf in a depth-first fashion, by searching for candidate dependents through the sentence and predicting relation type at each step. We introduce intra-word dependencies into the relation types for word segmentation, and the inter-word dependencies with POS tags for part-of-speech tagging. Since the top-down model provides a global view of an input sentences, the information of the whole sentence and all previously generated arcs are available for action decisions, and all characters of the sentence are considered as candidate dependencies. Experimental results on the Penn Chinese Treebank (CTB) show that the proposed model outperformed existing neural joint parsers by 0.81% on dependency parsing, and achieved the F1-scores of 95.97%, 91.72%, 80.25% for Chinese word segmentation, part-of-speech tagging, and dependency parsing.