Reviewerly/exHarmony huggingface dataset:
The Communicative Competence Components Analysis in Using English through EIR

This study was aimed to explain the communicative competence components of the fourth semester English Department students through English Interaction Room (EIR). The communicative competence components in using English were analyzed based on the grammatical competence, discourse competence, sociolinguistic competence and strategic competence.&#x0D; This study used qualitative method which means that the data of this study were explained by using descriptive analysis method. The result showed that (1) Mostly, the utterances of the participants were ungrammatical in syntax, while some were in morpheme, but those were acceptable (2) Mostly the utterances were appropriate with the topic discussed. the participants were able to construct a unified discourse, (3) Mostly, the utterances seemed natural and used less formal language, and (4) Most participants used achievement strategy to hold the conversation.&#x0D; In conclusion, most of EIR participants are communicative in using English viewed from the discourse competence, sociolinguistic competence and strategic competence. They need to increase their competence of using English grammar.&#x0D; Keywords: Communicative Competence; Using English; English Interaction Room (EIR).
Online dictionary learning for car recognition using sparse coding and LARS

&lt;p&gt;The bag of feature method coupled with online dictionary learning is the basis of our car make and model recognition algorithm. By using a sparse coding computing technique named LARS (Least Angle Regression) we learn a dictionary of codewords over a dataset of Square Mapped Gradient feature vectors obtained from a densely sampled narrow patch of the front part of vehicles. We then apply SVMs (Support Vector Machines) and KMeans supervised classification to obtain some promising results.&lt;/p&gt;
An Error Analysis of Students in Writing Narrative Text

This research is aimed to find out the typical errors on students in writing narrative text by using simple past tense at grade IX of SMP SwastaTalitakum Medan. The researchers used a descriptive qualitative method in this research. Based on the research findings, it can be concluded that the errors of misinformation are the most frequent errors in which the ninth grade students committed 233 errors in regular verb. Moreover , it was followed by errors of misinformation with 150 errors, addition with 38 errors, omission with 29 errors and misordering with 16 errors. Then errors in regular verb, it was followed by errors of misordering with 36 errors, omission with 38 errors, addition with 56 errors and misinformation with 156 errors. It can be concluded that after analyzing through the research, the writer got the data about students’ errors, which are commonly made in narrative writing using simple past. Their errors are misinformation, addition, omission and misordering.&#x0D; Keywords: error analysis, simple past tense
Group-Wise Dynamic Dropout Based on Latent Semantic Variations

Dropout regularization has been widely used in various deep neural networks to combat overfitting. It works by training a network to be more robust on information-degraded data points for better generalization. Conventional dropout and variants are often applied to individual hidden units in a layer to break up co-adaptations of feature detectors. In this paper, we propose an adaptive dropout to reduce the co-adaptations in a group-wise manner by coarse semantic information to improve feature discriminability. In particular, we showed that adjusting the dropout probability based on local feature densities can not only improve the classification performance significantly but also enhance the network robustness against adversarial examples in some cases. The proposed approach was evaluated in comparison with the baseline and several state-of-the-art adaptive dropouts over four public datasets of Fashion-MNIST, CIFAR-10, CIFAR-100 and SVHN.
Monthly Soil Temperature Modeling Using Gene Expression Programming

Soil temperature is a critical variable controlling below-ground processes for global and continental carbon budgets. However, there are an insufficient number of climatic stations monitoring soil temperature. In this study, GEP model was used for estimation of monthly soil temperature using air temperature, depth, relative humidity and solar radiation data for the Antalya, Isparta, and Burdur in Turkey. This model was tested using measured meteorological data. The values of R2 between observed and predicted soil temperatures ranged from 0.95 to 0.97. Predictions with GEP model show good agreement with actual soil temperature measurements. New equations are presented for calculation of soil temperatures at different depths. The GEP-based formulations are very practical to predict soil temperature. Soil temperature prediction with GEP model is helpful in various processes, including agricultural decision, heating or cooling of buildings and ground-source heat pump applications.
Functionality Discovery and Prediction of Physical Objects

Functionality is a fundamental attribute of an object which indicates the capability to be used to perform specific actions. It is critical to empower robots the functionality knowledge in discovering appropriate objects for a task e.g. cut cake using knife. Existing research works have focused on understanding object functionality through human-object-interaction from extensively annotated image or video data and are hard to scale up. In this paper, we (1) mine object-functionality knowledge through pattern-based and model-based methods from text, (2) introduce a novel task on physical object-functionality prediction, which consumes an image and an action query to predict whether the object in the image can perform the action, and (3) propose a method to leverage the mined functionality knowledge for the new task. Our experimental results show the effectiveness of our methods.
Estimating the Density of States of Boolean Satisfiability Problems on Classical and Quantum Computing Platforms

Given a Boolean formula ϕ(x) in conjunctive normal form (CNF), the density of states counts the number of variable assignments that violate exactly e clauses, for all values of e. Thus, the density of states is a histogram of the number of unsatisfied clauses over all possible assignments. This computation generalizes both maximum-satisfiability (MAX-SAT) and model counting problems and not only provides insight into the entire solution space, but also yields a measure for the hardness of the problem instance. Consequently, in real-world scenarios, this problem is typically infeasible even when using state-of-the-art algorithms. While finding an exact answer to this problem is a computationally intensive task, we propose a novel approach for estimating density of states based on the concentration of measure inequalities. The methodology results in a quadratic unconstrained binary optimization (QUBO), which is particularly amenable to quantum annealing-based solutions. We present the overall approach and compare results from the D-Wave quantum annealer against the best-known classical algorithms such as the Hamze-de Freitas-Selby (HFS) algorithm and satisfiability modulo theory (SMT) solvers.
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test

Learning the probability distribution of high-dimensional data is a challenging problem. To solve this problem, we formulate a deep energy adversarial network (DEAN), which casts the energy model learned from real data into an optimization of a goodness-of-fit (GOF) test statistic. DEAN can be interpreted as a GOF game between two generative networks, where one explicit generative network learns an energy-based distribution that fits the real data, and the other implicit generative network is trained by minimizing a GOF test statistic between the energy-based distribution and the generated data, such that the underlying distribution of the generated data is close to the energy-based distribution. We design a two-level alternative optimization procedure to train the explicit and implicit generative networks, such that the hyper-parameters can also be automatically learned. Experimental results show that DEAN achieves high quality generations compared to the state-of-the-art approaches.
Personalized Game Reviews

One way of subjective evaluation of games is through game reviews. These are critical analyses, aiming to give information about the quality of the games. While the experience of playing a game is inherently personal and different for each player, current approaches to the evaluation of this experience do not take into account the individual characteristics of each player. We firmly believe game review scores should take into account the personality of the player. To verify this, we created a game review score system, using multiple machine learning algorithms, that computes multiple review scores for different personalities which allow us to provide a more holistic perspective of this value, based on multiple and distinct player profiles. Our results support that the approach is statistically and significantly better than using the weighted average score provided by metacritic.com, currently one of the most popular websites that aggregate video game reviews, among other media products.
VISUALIZATION AND COMPREHENSION: Corroborating children’s reading ability

This study aimed at finding out the influence of visualization on students’ reading comprehension ability. Quantitative approach was employed in carrying out this research. The data were collected through test as it was the main instrument of this study. The data analysis was carried out using manual statistics calculation to find out the mean score before and after the treatment. From the result, it was obtained that the most effect that visualization has is on the inferring; second, it is on details; then it is followed by main idea. The smallest influence is made on vocabulary. Thus, it can be concluded that visualization while reading can help students a lot in terms of inferring, details, and main idea.
Spline curve deformation model with prior shapes for identifying adhesion boundaries between large lung tumors and tissues around lungs in CT images

Purpose Automated segmentation of lung tumors attached to anatomic structures such as the chest wall or mediastinum remains a technical challenge because of the similar Hounsfield units of these structures. To address this challenge, we propose herein a spline curve deformation model that combines prior shapes to correct large spatially contiguous errors (LSCEs) in input shapes derived from image‐appearance cues.The model is then used to identify the adhesion boundaries between large lung tumors and tissue around the lungs. Methods The deformation of the whole curve is driven by the transformation of the control points (CPs) of the spline curve, which are influenced by external and internal forces. The external force drives the model to fit the positions of the non‐LSCEs of the input shapes while the internal force ensures the local similarity of the displacements of the neighboring CPs. The proposed model corrects the gross errors in the lung input shape caused by large lung tumors, where the initial lung shape for the model is inferred from the training shapes by shape group‐based sparse prior information and the input lung shape is inferred by adaptive‐thresholding‐based segmentation followed by morphological refinement. Results The accuracy of the proposed model is verified by applying it to images of lungs with either moderate large‐sized (ML) tumors or giant large‐sized (GL) tumors. The quantitative results in terms of the averages of the dice similarity coefficient (DSC) and the Jaccard similarity index (SI) are 0.982 ± 0.006 and 0.965 ± 0.012 for segmentation of lungs adhered by ML tumors, and 0.952 ± 0.048 and 0.926 ± 0.059 for segmentation of lungs adhered by GL tumors, which give 0.943 ± 0.021 and 0.897 ± 0.041 for segmentation of the ML tumors, and 0.907 ± 0.057 and 0.888 ± 0.091 for segmentation of the GL tumors, respectively. In addition, the bidirectional Hausdorff distances are 5.7 ± 1.4 and 11.3 ± 2.5 mm for segmentation of lungs with ML and GL tumors, respectively. Conclusions When combined with prior shapes, the proposed spline curve deformation can deal with large spatially consecutive errors in object shapes obtained from image‐appearance information. We verified this method by applying it to the segmentation of lungs with large tumors adhered to the tissue around the lungs and the large tumors. Both the qualitative and quantitative results are more accurate and repeatable than results obtained with current state‐of‐the‐art techniques.
Distinguisher on full-round compression function of GOST R

From January 2013, GOST R 34.11-2012 hash function has been the new Russian Federal standard hash function which replaced the old one GOST R 34.11-97. GOST R hash function can compress any message up to 2512 bits into 256 bits and 512 bits, which are called GOST R-256 and GOST R-512 respectively. In this paper, we present distinguishers for the compression function and its internal permutation of GOST R hash function by exploiting the integral cryptanalysis. Firstly, we propose an 8.75-round known-key distinguisher for the internal permutation with the time complexity of 2128. Then by extending the distinguisher, we propose the known-key distinguisher for 12-round internal permutation with the time complexity of 2192.4, and the memory is about 2129 bytes. Since the compression function is designed by the Miyaguchi-Preneel mode, we can transform the distinguishers for permutation to ones for the compression function. As a result, we present the 8.75-round and full 12-round distinguishers for the GOST R compression function. This is the first publish to propose distinguishers for full-round GOST R compression function and its internal permutation.
Analysis and Design of Voice Assisted Learning System Based on Baidu AI

With the development of educational informationization in the Era of 2.0, the education industry has gradually turned into intelligent technology. More and more new intelligent technologies are used in classroom teaching, but few of them are used to assist students in autonomic learning after class. The study attempts to build a language assisted learning system based on baidu AI to help students learn independently. The application of the voice assisted learning system can create favorable conditions for personalized and autonomic learning of students, and provides students with a new learning pattern to meet the requirements of the information era.
Financial deepening, inclusive finance and economic growth: analysis based on endogenous growth theory

Abstract This article studies the relationship between financial development and economic growth from the perspectives of financial deepening and inclusive finance. The theoretical research expands the financial endogenous economic growth model, constructs the Hamilton function, using the first-order optimization condition to obtain explicit relationship between economic growth and financial development under balanced path. Theoretical research shows that financial deepening and inclusive finance are significant and stable in promoting economic growth.
Privacy-Preserving Attribute-Based Multi-keyword Search Encryption Scheme with User Tracing

Attribute-based keyword search encryption (ABKSE) can provide fine-grained data sharing and keyword search over encrypted data. However, most of the existing ABKSE schemes need a large amount of computation cost in the encryption phase and only support single-keyword queries in the search phase, and the access policy embedded in the ciphertext cannot be hidden, which will leak some sensitive information to malicious user. Furthermore, some malicious authorized users could leak their secret keys to others for benefits, but it is difficult to identify the malicious users who disclosed the secret keys when the same attributes are shared by many users. To address the above problems, we propose a privacy-preserving attribute-based multi-keyword search encryption scheme with user tracing. In the proposed scheme, we adopt the AND gate on multi-value attribute technique to hide the access policy. Meanwhile, an aggregate technique is used to achieve fast multi-keyword search and Shamir’s threshold scheme is utilized to trace the malicious users. The security analysis demonstrates that the proposed scheme is selectively secure and fully traceable. Performance analysis shows that the proposed scheme is efficient and practical.
Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning

Node embedding is a crucial task in graph analysis. Recently, several methods are proposed to embed a node as a distribution rather than a vector to capture more information. Although these methods achieved noticeable improvements, their extra complexity brings new challenges. For example, the learned representations of nodes could be sensitive to external noises on the graph and vulnerable to adversarial behaviors. In this paper, we first derive an upper bound on generalization error for Wasserstein embedding via the PAC-Bayesian theory. Based on this, we propose an algorithm called Adversarial PAC-Bayesian Learning (APBL) in order to minimize the generalization error bound. Furthermore, we provide a model called Regularized Adversarial Wasserstein Embedding Network (RAWEN) as an implementation of APBL. Besides our comprehensive analysis of the robustness of RAWEN, our work for the first time explores more kinds of embedded distributions. For evaluations, we conduct extensive experiments to demonstrate the effectiveness and robustness of our proposed embedding model compared with the state-of-the-art methods.
Vehicular networking and computer vision-based distance estimation for VANET application using Raspberry Pi 3

This research was implementing vehicle networking using WIFI connection and computer vision to measure the distance of vehicles in front of a driver. In particular, this works aimed to improve a safe driving environment thus supporting the current technology concept being developed for inter-vehicular networking, VANET, especially in its safety application such as Overtaking Assistance System. Moreover, it can wirelessly share useful visual information such as hazard area of a road accident. In accordance with Vehicle-to-Vehicle (V2V) concept, a vehicle required to be able to conduct networking via a wireless connection. Useful data and video were the objects to be sent over the network established. The distance of a vehicle to other vehicles towards it is measured and sent via WIFI together with a video stream of the scenery experienced by the front vehicle. Haar Cascade Classifier is chosen to perform the detection. For distance estimation, at least three methods have been compared in this research and found evidence that, for measuring 5 meters, the iterative methods shows 5.80. This method performs well up to 15 meters. For measuring 20 meters, P3P method shows a better result with only 0.71 meters to the ground truth. To provide a physical implementation for both the detection and distance estimation mechanism, those methods were applied in a compact small-sized vehicle-friendly computer device the Raspberry Pi. The performance of the built system then analyzed in terms of streaming latency and accuracy of distance estimation and shows a good result in measuring distance up to 20 meters.
Uso de las TIC en los procesos de enseñanza y aprendizaje del contenido “Enlace Químico y sus Propiedades”, centrado en habilidades cognitivas en estudiantes de educación media chilena

This research evaluates the effectiveness of the use of ICT in the teaching and learning process of 9th graders in the content of Chemical Bonds and its properties, during two annual school periods at the School "Juan Luis Undurraga Aninat", Santiago de Chile. This research has a quantitative approach, and is a field and quasi-experimental work (pre and post-test), whose instruments were evaluations and surveys. The results showed that the percentages of achievement for each indicator increase on average 29 points; the learning ranges decrease in insufficient students (69% to 9%) and show mobility towards "Good" (5% to 40%) and "Very Good" (2% to 12%) categories, without observing significant differences in the category "Acceptable". On the other hand, the development of scientific skills increases, on average, above 30 points. The most significant increases were: identify (75%), observe (79%), recognize (75%), apply (78%), compare (90%) and analyze (81%). It is concluded that the use of ICT has positive effects on student learning in this topic.
Perancangan Sistem Informasi Pengolahan Data Penjualan Secara Kredit dan Controlling Stock Dengan Menerapkan Metode Backorder Pada Toko Master Menggunakan Bahasa Pemrograman Java dan Database MySql

&lt;p&gt;&lt;em&gt;Had done the research on the Master Store, where the problems are used as the basis in writing this is a system of data processing sales on credit and controlling stock. Through the process of direct observation of the field has gained the weaknesses of the existing system. Although not entirely but more directed to a specific problem, it is expected to contribute significantly to the users of the system. The author designed the information system of data processing sales on credit and controlling stock based on Java Master Store, because with this information system will be faster, precise, and accurate. In designing this new system used the Java programming language and MySQL database, with the design of this new system the authors embody the system of data processing sales on credit and efficient controlling stock on the Master Store.&lt;/em&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords: Credit Sales Data Processing And Controlling Stock, JAVA, MySQL&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;
The complexity of cultural outcomes as Indonesian language teaching material for foreign speakers (BIPA)

Culture plays an important role in language learning with the aim of promoting communication skills for foreign students.Language is also a set of spoken voices which are arbitrators and must be meaningful.The main function of language is as a tool and communication tool.As for language learning the role of the instructor is needed because professional instructors in language and culture support literacy, so students want to have literacy and cross-cultural skills.Thus, cross-cultural understanding is also needed to be a supporting component in the language learning process of a country, without knowing the culture in the local area, foreign students will have difficulty in socializing with the local community.In the process of communicating the use of individual languages is always associated with language, language users and language use.This research was written with the aim of describing the cultural results contained in Indonesian textbooks for foreign speakers.This research was written with the aim of describing the cultural results contained in the textbook.The level of the textbook analyzed was my best friend's level a1, A2, B1, and B2.The research method used in conducting research is a qualitative descriptive research method.Based on the results of the study, found several cultural results at each level or level.The results of this study are: livelihood (MH) 37.5%, life equipment and technology (PH) 6.25%, arts (K) 31.25%,knowledge systems (SP) 6.25%, religion (R) 6.25% and language (B) 12.5%.