Reviewerly/exHarmony huggingface dataset:
Development of Work Sheets for Students (LKPD) in Writing Explanation Text

This study aims to describe the results of product trials based on the development of needs analysis results and product validation results that have been assessed. The product developed was LKPD in writing explanatory texts for high school students. This research is part of research and development (R&amp;D) research. The method used in this research is qualitative and quantitative. Products are tested on students on a small scale. The product was tested on a class XI student of SMA Dharma Loka Pekanbaru. Data collection techniques were carried out through the questionnaire and test stages. Questionnaires were given to students to describe the quality of teaching materials (LKPD) that had been made through filling out questionnaire instruments. The test is given to students in accordance with the instructions and questions in the LKPD so a score is obtained based on established assessment criteria. Data obtained, analyzed through SPSS. The results of the data analysis provide an overview of the aspects of content eligibility, aspects of language readability, presentation of material, and graphics which have very strong correlations for the preparation of LKPD. This is indicated by the level of correlation of the aspect of content eligibility 0.852 with a significance level of 0.000 &lt;0.5; aspects of language readability 0.759 with a significance level of 0.000 &lt;0.5; presentation of material 0.979 with a significance level of 0.000 &lt;0.5; and graphics 0.702 with a significance level of 0,000 &lt;0.5.
Multiple Level Information Security Using Image Steganography and Authentication

The security of information during transmission in the open network is crucial.While sharing digital data on the internet, it is essential to observe information security goals: confidentiality, integrity, authenticity, and accuracy.This paper presents a multiple tier information security through image steganography technique using a novel puzzle in YCbCr color space, and digitally signed stego image for authentication.Experimental results show that the resemblance between the stego-images and cover images is high.The recognition of the stego image is small, with the use of the Human Visual System (HVS) having an average PSNR value of 47.72db.The probability of detection of the stego-images using Chi-Square Analysis is low, with the average value of 4.39E-05.
InterCriteria Analysis of data from intellectual and physical evaluation tests of students practicing sports activities

Physical activity among university students is essential for complementing sedentary behaviour and for individuals' future mental and physical health.At the same time, intellectual qualities like operative, analytic, logical thinking, attention span, short-term memory, long-term memory, etc., are important factors for the successful participation in different academic sports activities.In view of this, it is important to evaluate the initial level of intellectual and physical development by applying a group of specific tests.In this paper, we propose the application of the approach of InterCriteria Analysis (ICrA) to data, obtained from university students practicing sports activities in order to evaluate the appropriateness of the used tests.
Correcting Predictions for Approximate Bayesian Inference

Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corrects for inaccuracies in posterior inference by altering the decision-making process. We train a separate model to make optimal decisions under the approximate posterior, combining interpretable Bayesian modeling with optimization of direct predictive accuracy in a principled fashion. The solution is generally applicable as a plug-in module for predictive decision-making for arbitrary probabilistic programs, irrespective of the posterior inference strategy. We demonstrate the approach empirically in several problems, confirming its potential.
Development Student Worksheet in the Topic of Regular Circular Motion with Blended Learning based Edmodo as Learning Motivations for Student

The research aims to develop learners ' worksheets on the subject of circular motion with the Edmodo based blended Learning as well as the motivation to learn learners. The test subjects in this study were vocational school grade of X students. The model used is the ADDIE development model, which includes five stages: Analysis, Design, Development, Implementation, and Evaluation. To know the feasibility of media validation. To know the feasibility of media done validation. This validation is in the form of a poll with predefined criteria and indicators. From the development of student worksheet conducted an expert test of materials, media experts and users with a percentage of 79.68%, 77.27% and 83.62% with that percentage, then the student worksheet with blended learning based Edmodo As the motivation to learn learners in the categorized worthy as a learning media.
Deep Scalable Supervised Quantization by Self-Organizing Map

Approximate Nearest Neighbor (ANN) search is an important research topic in multimedia and computer vision fields. In this article, we propose a new deep supervised quantization method by Self-Organizing Map to address this problem. Our method integrates the Convolutional Neural Networks and Self-Organizing Map into a unified deep architecture. The overall training objective optimizes supervised quantization loss as well as classification loss. With the supervised quantization objective, we minimize the differences on the maps between similar image pairs and maximize the differences on the maps between dissimilar image pairs. By optimization, the deep architecture can simultaneously extract deep features and quantize the features into suitable nodes in self-organizing map. To make the proposed deep supervised quantization method scalable for large datasets, instead of constructing a larger self-organizing map, we propose to divide the input space into several subspaces and construct self-organizing map in each subspace. The self-organizing maps in all the subspaces implicitly construct a large self-organizing map, which costs less memory and training time than directly constructing a self-organizing map with equal size. The experiments on several public standard datasets prove the superiority of our approaches over the existing ANN search methods. Besides, as a by-product, our deep architecture can be directly applied to visualization with little modification, and promising performance is demonstrated in the experiments.
Recursively Binary Modification Model for Nested Named Entity Recognition

Recently, there has been an increasing interest in identifying named entities with nested structures. Existing models only make independent typing decisions on the entire entity span while ignoring strong modification relations between sub-entity types. In this paper, we present a novel Recursively Binary Modification model for nested named entity recognition. Our model utilizes the modification relations among sub-entities types to infer the head component on top of a Bayesian framework and uses entity head as a strong evidence to determine the type of the entity span. The process is recursive, allowing lower-level entities to help better model those on the outer-level. To the best of our knowledge, our work is the first effort that uses modification relation in nested NER task. Extensive experiments on four benchmark datasets demonstrate that our model outperforms state-of-the-art models in nested NER tasks, and delivers competitive results with state-of-the-art models in flat NER task, without relying on any extra annotations or NLP tools.
Effect of Cloud Accounting as income statement on Accountant Performance

A simple and secure security accounting system is a system needed by various large and small companies where of course the company is never separated from accounting in order to process financial expenditures and revenues owned by companies that have a purpose to make a profit. Efforts to achieve success in a company can be seen from financial management which can be monitored and can be managed properly so that finance can be controlled well too, for that accuracy is an important role so with the company's cloud accounting it can be easier to monitor and also manage financial well, so it will be easier to make income / loss statements. cloud accounting provides a user friendly look that can certainly facilitate users. The purpose of this study is so that companies can pay more attention to monitoring and managing finances well so that it can facilitate the making of income statement . In this study took place used observational research methods and field library studies so that the system made can meet the existing needs of the company.
Span-Based Neural Buffer: Towards Efficient and Effective Utilization of Long-Distance Context for Neural Sequence Models

Neural sequence model, though widely used for modeling sequential data such as the language model, has sequential recency bias (Kuncoro et al. 2018) to the local context, limiting its full potential to capture long-distance context. To address this problem, this paper proposes augmenting sequence models with a span-based neural buffer that efficiently represents long-distance context, allowing a gate policy network to make interpolated predictions from both the neural buffer and the underlying sequence model. Training this policy network to utilize long-distance context is however challenging due to the simple sentence dominance problem (Marvin and Linzen 2018). To alleviate this problem, we propose a novel training algorithm that combines an annealed maximum likelihood estimation with an intrinsic reward-driven reinforcement learning. Sequence models with the proposed span-based neural buffer significantly improve the state-of-the-art perplexities on the benchmark Penn Treebank and WikiText-2 datasets to 43.9 and 35.2 respectively. We conduct extensive analysis and confirm that the proposed architecture and the training algorithm both contribute to the improvements.
Localize, Assemble, and Predicate: Contextual Object Proposal Embedding for Visual Relation Detection

Visual relation detection (VRD) aims to describe all interacting objects in an image using subject-predicate-object triplets. Critically, valid relations combinatorially grow in O(C2 R) for C object categories and R relationships. The frequencies of relation triplets exhibit a long-tailed distribution, which inevitably leads to bias towards popular visual relations in the learned VRD model. To address this problem, we propose localize-assemble-predicate network (LAP-Net), which decomposes VRD into three sub-tasks: localizing individual objects, assembling and predicting the subject-object pairs. In the first stage of LAP-Net, Region Proposal Network (RPN) is used to generate a few class-agnostic object proposals. Next, these proposals are assembled to form subject-object pairs via a second Pair Proposal Network (PPN), in which we propose a novel contextual embedding scheme. The inner product between embedded representations faithfully reflects the compatibility between a pair of proposals, without estimating object and subject class. Top-ranked pairs from stage two are fed into a third sub-network, which precisely estimates the relationship. The whole pipeline except for the last stage is object-category-agnostic in localizing relationships in an image, alleviating the bias in popular relations induced by training data. Our LAP-Net can be trained in an end-to-end fashion. We demonstrate that LAP-Net achieves state-of-the-art performance on the VRD benchmark while maintaining high speed in inference.
Design and Implementation of an Infrared-Based Sensor for Finger Movement Detection

With the increasing interest in smart devices and convenient remote control, the need for accurate wireless means of control has become imperative. This gives rise to research in the field of gesture and finger movement detection. This design focuses on exploring techniques involved in hand and finger movement detection, using the depth-sensing infrared cameras embedded on Xbox Kinect Module. The generated 3-D images are first filtered along the z-axis, then two distinct techniques; Haar-Like Features, and Deep Learning using a Convolution Neural Network, are performed on the images to detect hands. Useful metrics like, Precision, Recall, F1-Score and Accuracy are then used to evaluate the efficiency of these techniques. The results show that while the deep learning model is the most accurate with a weighted accuracy of 1.0 (due to the absence of noise in the images) in contrast with 0.97 observed for the Haar-Like features, the Haar-like features technique runs faster due to its static nature. These findings point to the conclusion that the deep learning model is a better technique for detecting hands despite its longer running time.
Activity Recognition and Prediction in Real Homes

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low resolution depth video data from seven apartments, and classify four activities – no movement, standing up, sitting down, and TV interaction – by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.
Knowledge-based building management combining human perception and building automation systems

User feedback offers high potential for improving comfort satisfaction and adapting processes in building operation and management. However, comfort in building zones is usually quantified using sensor devices of building automation systems while feedback of building users is only manually analyzed by facility managers. The goal of the recently started project `HumBAS' is to combine subjective human perception and objective data originating from building automation systems to improve comfort for building users and infer recommendations for facility managers using knowledge engineering. An intuitive interface for the acquisition of human perception as well as a knowledge-based integration of objective and subjective comfort measurements are developed in order to describe an automatic process for direct consideration of user feedback and detection of problems in building management. The paper presents related work, gives an outlook on the overall project goals and addresses first results.
A Combined Method of Skin-and Depth-based Hand Gesture Recognition

Kinect is a promising acquisition device that provides useful information on a scene through color and depth data. There has been a keen interest in utilizing Kinect in many computer vision areas such as gesture recognition. Given the advantages that Kinect provides, hand gesture recognition can be deployed efficiently with minor drawbacks. This paper proposes a simple and yet efficient way of hand gesture recognition via segmenting a hand region from both color and depth data acquired by Kinect v1. The Inception model of the image recognition system is used to check the reliability of the proposed method. Experimental results are derived from a sample dataset of Microsoft Kinect hand acquisitions. Under the appropriate conditions, it is possible to achieve high accuracy in close to real time
Query Rewriting for Ontology-Mediated Conditional Answers

Among many solutions for extracting useful answers from incomplete data, ontology-mediated queries (OMQs) use domain knowledge to infer missing facts. We propose an extension of OMQs that allows us to make certain assumptions—for example, about parts of the data that may be unavailable at query time, or costly to query—and retrieve conditional answers, that is, tuples that become certain query answers when the assumptions hold. We show that querying in this powerful formalism often has no higher worst-case complexity than in plain OMQs, and that these queries are first-order rewritable for DL-Liteℛ. Rewritability is preserved even if we allow some use of closed predicates to combine the (partial) closed- and open-world assumptions. This is remarkable, as closed predicates are a very useful extension of OMQs, but they usually make query answering intractable in data complexity, even in very restricted settings.
Building Semantic Dependency Knowledge Graph Based on HowNet

This paper introduces a method of constructing a semantic dependency knowledge graph (SDKG) by using the rich semantic knowledge in HowNet. The establishment of SDKG depends on correspondence between the lexical dependency labels in semantic dependence bank of BLCU-HIT and the event roles in HowNet. For words with few event roles or those which are not included in the knowledge graph, sememes are recommended to them based on SPWE and SPASE algorithms to extend the SDKG. The paper demonstrates that the experiments achieve an accuracy of 86% when the sememe recommendation is conducted. Considering the establishment of the dependency relationship, a correspondence table in this paper including 87 pieces of data of event role labels mapping to dependency labels is designed. The constructed SDKG has nearly 500000 nodes that contains rich dependency information, which can be used to assist the analysis of the Semantic Dependency Parser. Besides, the results of Semantic Dependency Analysis can be drawn on to supplement the SDKG.
Task and Motion Planning Is PSPACE-Complete

We present a new representation for task and motion planning that uses constraints to capture both continuous and discrete phenomena in a unified framework. We show that we can decide if a feasible plan exists for a given problem instance using only polynomial space if the constraints are semialgebraic and all actions have uniform stratified accessibility, a technical condition closely related to both controllability and to the existence of a symbolic representation of a planning domain. We show that there cannot exist an algorithm that solves the more general problem of deciding if a plan exists for an instance with arbitrary semialgebraic constraints. Finally, we show that our formalism is universal, in the sense that every deterministic robotic planning problem can be well-approximated within our formalism. Together, these results imply task and motion planning is PSPACE-complete.
CAWA: An Attention-Network for Credit Attribution

Credit attribution is the task of associating individual parts in a document with their most appropriate class labels. It is an important task with applications to information retrieval and text summarization. When labeled training data is available, traditional approaches for sequence tagging can be used for credit attribution. However, generating such labeled datasets is expensive and time-consuming. In this paper, we present Credit Attribution With Attention (CAWA), a neural-network-based approach, that instead of using sentence-level labeled data, uses the set of class labels that are associated with an entire document as a source of distant-supervision. CAWA combines an attention mechanism with a multilabel classifier into an end-to-end learning framework to perform credit attribution. CAWA labels the individual sentences from the input document using the resultant attention-weights. CAWA improves upon the state-of-the-art credit attribution approach by not constraining a sentence to belong to just one class, but modeling each sentence as a distribution over all classes, leading to better modeling of semantically-similar classes. Experiments on the credit attribution task on a variety of datasets show that the sentence class labels generated by CAWA outperform the competing approaches. Additionally, on the multilabel text classification task, CAWA performs better than the competing credit attribution approaches1.
A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models

Modern causal analysis involves two major tasks, discovery and identification. The first aims to learn a causal structure compatible with the available data, the second leverages that structure to estimate causal effects. Rather than performing the two tasks in tandem, as is usually done in the literature, we propose a symbiotic approach in which the two are performed simultaneously for mutual benefit; information gained through identification helps causal discovery and vice versa. This approach enables the usage of Verma constraints, which remain dormant in constraint-based methods of discovery, and permit us to learn more complete structures, hence identify a larger set of causal effects than previously achievable with standard methods.
2018 (CEFR) Diller için Avrupa Ortak Öneriler Çerçevesi Yeni Açıklamalar Metininde Konuşma Becerisinin İncelenmesi

Nowadays, learning a foreign language is not only a goal but also a tool for learning language.The importance of communicating in a foreign language is increasing day by day.This has necessitated many countries to revise their foreign language policies.The Council of the Common European Framework of Languages for Languages (CEFR), which has been published since 2001, is an important resource that greatly influences language teaching and learning, in order to assist foreign language users and regulate language teaching.This study is a qualitative research based on the data of related researches within the subject.In this study, which has not yet been translated into Turkish (CEFR) 2018 new explanations edition, this study examined the ability to speak as a productive activity.In this study, the gains that were added or subtracted from the 2018 (CEFR) speaking skills were determined.In this study, where we talk about speaking skills, we examined oral production which is one of the productive activities.Oral production is divided into five categories in 2018 CEFR.These are: Sustained monologue: Describing experience, Sustained monologue: Giving information, Sustained monologue: Putting a case (e.g. in debate), Public announcements, and Addressing audiences.In the edition of the 2018 CEFR new announcements, "Pre-A1" level has been added to the C2 to A1 language levels, unlike the previous edition, and there is mostly no descriptor.At the specified achievements for different skills, at some levels a new acquisition was not added.At some levels, the old gains were completely removed and at some levels new gains were added to the old gains and presented together.In addition to the added new gains, most of the gains still exist.Preparing the studies conducted in the context of foreign language teaching according to CEFR may provide a healthier result.It is important that Turkish language teaching sets and activities and materials prepared in the field of Teaching Turkish as a Foreign Language are based on CEFR.