Reviewerly/exHarmony huggingface dataset:
Toward Automated Courseware Production for the ExPLoRAA Learning Environment

This paper presents a newborn collaboration between heterogeneous AI competences. In particular, it describes current work on the integration of machine learning techniques for the automatic generation of contents for an Intelligent Tutoring System grounded on automated planning techniques. The joint use of these two approaches allows on the one hand to facilitate the task of instructional designers in defining and preparing courses, and on the other hand to dynamically support the use of content according to different users context.
RDF Multi-query Optimization Algorithm Based on Triple Pattern Reordering

Under the premise of accelerating statistics by RDF storage index and narrowing the scope of semantic pruning, the distance-based triple pattern reordering algorithm is used to obtain the optimal connection order of a single query. After converting each query to a left deep tree, the left deep tree identification algorithm is used to find the common subtree and evaluate its cost. Establish materialized view and corresponding update replacement mechanism for valuable common subtrees. While optimizing a single query to a certain extent, it improves the possibility of the existence of common result sets among multiple queries. By making full use of query sharing, the overall execution cost of query set can be reduced. The experimental results show that the algorithm of this paper has better query performance than the existing query schemes, whether it is on single query or multiple query. Especially in the case that the RDF data set is large in scale, the number of queries in the query set is large, and the query statement is more complicated, the multi-query optimization method of this paper is better.
RELATIONSHIP OF THE METHODOLOGY FOR WORKING WITH TASKS ON INFORMATICS AND MATHEMATICS

Methodological content (goals, requirements, dialogue issues) are described for the stages of task solution that are problem analysis, finding a way to solve the problem, making a decision, summarizing. The possibility of implementing a unified methodology for the tasks for the operation of printers from the courses of mathematics and informatics is substantiated. The technique is implemented in interactive computer presentations that presented on the site INFO.
Background and Foundational Research for the Revision of the TEPS

Since the first administration of the TEPS in 1999, there have been many important changes in the field of language teaching and assessment.The emergence of new information and communication technology has drastically changed the ways we communicate.This has made it necessary to re-conceptualize the construct of English language proficiency.To respond to such needs and to TEPS test takers' feedback that had accumulated for more than 15 years, a series of research projects was undertaken to revise the TEPS during the years of 2016-2018.Major changes included a reduction in the number of items and testing time and the addition of new item types (e.g., testlets) and more authentic passage formats (e.g., e-mails, instant messages, online newspaper articles).This paper introduces the background to how the decision to revise the TEPS came to be made and describes the rationale behind the revision of the TEPS test blueprint and specification.
A Method of Pointer Instrument Reading for Automatic Inspection

In automatic inspection, it is required to automatically read the indicator value of the pointer instrument from images. However, in the cases of different shooting positions and focal lengths, the images will be blurred and deformed, which bring difficulties to indicator value reading. This paper proposes a method for reading the indicator values of pointer instruments automatically. First, we make clear templates for each type of dashboard. Then, we use KAZE to extract the features for the input image, use KNN to match the features from this image and the templates, use RANSAC to reduce the wrong matchings, and get the type and area of instrument based of matching results. Finally, we calculate the indicator value by edge information, pointer line information from probabilistic Hough transform, and labeled prior knowledge. The experimental results show the output values within a degree scale is 96.4% in 560 samples, which meets the requirements of automatic inspection.
Information Competencies in Chilean Education

Within the complexity of the Information Society, which is constantly mediated the impact of Information and Communications Technology (ICT), demand for the incorporation of key competencies educational processes, which highlights the digital information skills, which are related to the processing of information in a virtual context and the competence of digital processing, particularly in relation to the context offered by the Internet. From this aspect, the formation of students from the first teaching degree becomes important. This study evaluates the effectiveness of an educational intervention in a b-learning context for training in informational skills at the primary degree of education. In this way, a pre-experimental design with pre-test and post-test was applied to a sample of 328 elementary students in Chile, in the sector of the Province of Maipo. The data collection began with a validated instrument for evaluating performance in the four dimensions of information competencies: Search, Evaluation, Processing and Communication of the digital Information. After checking the previous assumptions of normality and homoscedasticity, the corresponding statistical tests were applied in the data analysis. The results establish an increase in the learning of informational competences based on the application of the educational intervention.
Leveraging Context Information for Joint Entity and Relation Linking

As an important module in most knowledge base question answering (KBQA) systems, entity and relation linking maps proper nouns and relational phrases to corresponding semantic constructs (entities and relations, respectively) in a given KB. Because different entities/relations may have the same mentions, joint disambiguation has been proposed to identify the exact entity/relation from a list of candidates using context information. Existing joint disambiguation methods, like the method in EARL (Entity and Relation Linker), mainly focus on modeling the co-occurrence probabilities of different entities and relations in input questions, while paying little attention to other non-mention expressions (e.g., wh-words). In this paper, we propose the Extended Entity and Relation Linker (EEARL), which leverages full context information to improve linking accuracy. EEARL firstly extracts the context information for each mention and the attribute features for each entity/relation via character-level and word-level LSTMs and constructs context vectors and feature vectors, respectively, and then calculates the similarity between the two vectors to re-score all the candidates. Experimental results on two benchmark datasets (LC-QuAD and QALD) show that EEARL outperforms EARL and several baseline methods in terms of both entity linking and relation linking accuracy.
A utility-driven surveillance approach to trade-off security and privacy

In recent years advances in machine learning methods such as deep learning has led to significant improvements in our ability to track people and vehicles, and to recognise specific individuals. Such technology has enormous potential to enhance the performance of image-based security systems. However, wide-spread use of such technology has important legal and ethical implications, not least for individuals right to privacy. In this paper, we describe a technological approach to balance the two competing goals of system efficacy and privacy. We describe a methodology for constructing a “goal-function” that reflects the operators preferences for detection performance and anonymity. This goal function is combined with an image-processing system that provides tracking and threat assessment functionality and a decision-making framework that assesses the potential value gained by providing the operator with de-anonymized images. The framework provides a probabilistic approach combining user preferences, world state model, possible user actions and threat mitigation effectiveness, and suggests the user action with the largest estimated utility. We show results of operating the system in a perimeter-protection scenario
The growth determinant based on economic and non economic factors (empirical evidence in Central Java Province, Indonesia)

Abstract The study of economic growth has been grown, if previously only paying attention to economic factors, now also consider non-economic factors. The development of growth studies has provided a new study perspective in recent decades so that it slowly develops into its own interest in study. This paper wants to fill this up by combining economic and non-economical factors as determinants of growth. This paper uses individual data for districts or cities in Central Java Province. The analysis technique used is ordinary least squares through multiple regression. The paper's findings show that only investment factors as a representation of economic factors that influence growth, while non-economic factors do not have a significant influence on growth, where the influence of non-economic factors produces ambiguous findings. From the findings of this paper, it still have some limitations that must be corrected for further study.
Semi Active Control of Solar Tracker Using Variable Position of Added Mass Control

This paper offers a new method to control solar panel orientation. The technique is applied by varying the position and ratio of the added mass to the total mass of solar panel, m/m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (%). The added mass position is varied from the axis point up to the end point of the solar panel. The solar panel orientation is influenced by the moving mass position. The oscillation in solar panel caused by the moving mass is eliminated by adjusting the equivalent damping torque c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">τ</sub> , and the solar panel maximum orientation is varied by changing the equivalent torsion spring k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">τ</sub> . The simulation results proved that the solar panel orientation changed from 0 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> to 60,63 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> for parameter values m/m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> = 15%, r/0,5r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> = 35%, c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">τ</sub> = 3 Nm/rad/sec and k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">τ</sub> = 1 Nm/rad. The ability of the system to control the orientation and ratio of m/m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> dan r/0,5r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> is effective to reduce energy consumption of the solar panel orientation controlling system.
A Novel Recommendation System for Next Feature in Software

Software that needs to fulfill many tasks requires a large number of components. Users of these software need a lot of time to find the desired functionality or follow a particular workflow. Recommendation systems can optimize a user's working time by recommending the next features he/she needs. Given that, we evaluate the use of three algorithms (Markov Chain, IndRNN, and LSTM) commonly applied in sequence recommendation/classification in a dataset that reflects the use of the accounting software from Fortes Tecnologia. We analyze the results under two aspects: accuracy for top-5 recommendations and training time. The results show that the IndRNN achieved the highest accuracy, while the Markov Chain reached the lowest training time.
Analysis of a Hazard Prediction Model by Means of Variable Importance

Hazard prediction is an important element for intelligent robotic transporters to detect potential hazards like road roughness, drivability, and positive/negative obstacles from features obtained by sensor measurements. Analysis results by means of variable importance are presented for a hazard prediction model learned by random forests. Mean decrease accuracy (MDA) provides a quantitative feature importance estimation that explains which features are influential for the prediction model to make predictions. Partial dependence plot provides a qualitative explanation about how values of an important feature are used by the model. A data-driven feature selection method to find a threshold of important features by exploiting MDA is introduced. Those properties give an insight into the domain knowledge learned by the hazard prediction model as well as a reason why a prediction is returned. Explanation of inside mechanism of intelligent robotic systems is a key factor for the acceptance by societies.
Functional form estimation using oblique projection matrices for LS-SVM regression models

Kernel regression models have been used as non-parametric methods for fitting experimental data. However, due to their non-parametric nature, they belong to the so-called "black box" models, indicating that the relation between the input variables and the output, depending on the kernel selection, is unknown. In this paper we propose a new methodology to retrieve the relation between each input regressor variable and the output in a least squares support vector machine (LS-SVM) regression model. The method is based on oblique subspace projectors (ObSP), which allows to decouple the influence of input regressors on the output by including the undesired variables in the null space of the projection matrix. Such functional relations are represented by the nonlinear transformation of the input regressors, and their subspaces are estimated using appropriate kernel evaluations. We exploit the properties of ObSP in order to decompose the output of the obtained regression model as a sum of the partial nonlinear contributions and interaction effects of the input variables, we called this methodology Nonlinear ObSP (NObSP). We compare the performance of the proposed algorithm with the component selection and smooth operator (COSSO) for smoothing spline ANOVA models. We use as benchmark 2 toy examples and a real life regression model using the concrete strength dataset from the UCI machine learning repository. We showed that NObSP is able to outperform COSSO, producing stable estimations of the functional relations between the input regressors and the output, without the use of prior-knowledge. This methodology can be used in order to understand the functional relations between the inputs and the output in a regression model, retrieving the physical interpretation of the regression models.
Showcasing Modelio and pure:variants Integration in REVaMP$$^2$$ Project

REVaMP$$^2$$ project is part of the ITEA 3 industry-driven Research, Development and Innovation programme in the domain of software innovation. REVaMP$$^2$$ project aims to develop automated and comprehensive tools that support massive customizing of “Software-Intensive Systems and Services” (SIS) Product Lines (PL). This approach requires agile round-trip engineering processes for managing the different configurations in legacy assets, and for more systematic and automated variability management. For this purpose, the project puts among its priorities the standardization of a variability language called “Variability Exchange Language” (VEL) as a format for describing variability in models. This paper showcases the integration of the model-driven engineering (MDE) tool “Modelio” with the variability management tool “pure::variants” using the VEL language as data exchange format. VEL is used to model both the feature model, also referred to as “150% model”, with all possible variation points and all variant configurations coming from the features selection for the variant of a specific product.
HCI in praxis

HCI education reflects the continual evolution of HCI, embracing the changing landscapes of technology, infrastructure, and technology use. This forum aims to provide a platform for HCI educators, practitioners, researchers, and students to share their perspectives, reflections, and experiences related to HCI education. --- Sukeshini Grandhi, Editor
Research on MSK Demodulation Method for M-Sequence in IFF System

Abstract M-Sequence, short for Maximum Linear Feedback Shift Register Sequence, is widely used in identification friend or foe (IFF) system because of its good equalization and autocorrelation characteristics, and Minimum Shift Keying (MSK) signal has been paid more attention in military communication field because of its excellent signal characteristics. A new demodulation method for MSK modulation signal of M-Sequence is studied. The MSK signal is sampled by A/D converter, and an integration circuit is designed in the FPGA. The same M-Sequence is generated by the same M-Sequence source polynomial as the integrator coefficient. The peak value of the integrated signal is judged to determine the M-Sequence. When the symbol rate is 1 Mbps and the carrier rate is 1 Mbps and M-Sequence is 5 bits, the recognition time of the new method is less than 1 us, which is less than one fifth of the recognition time of the traditional demodulation method. This method simplifies the demodulation process and is easy to implement. It improves the recognition speed of M-Sequence in MSK modulation signal and provides some reference value for the development of IFF system.
Design and Control for Vacuum Contact Devices of Mobile Wall Climbing Robot Application in Complex Environment

Problem statement: The structure of vacuum grippers includes a system of sensitivity of the vacuum level, the angle of inclination of the surface and the trajectory of the robot. The vacuum level in the vacuum contact devices may vary in a wide range of values and allows the efficient adjustment of the flow level and pressure in the supply system of the mobile robot. Strategy based on decision making as example of control was developed taking into account the situational changes in the complex environment in which the robot moves. Purpose: To test the developed control algorithms, a series of experiments with vacuum grippers in air and water environments at different depths of underwater application was carried out. Based on the results of the work, recommendations were given for the design of adaptive vacuum grippers, which can be used on robots of vertical movement during their operation in complex environments. To obtain reliable experimental data SEMS system was used. Results: The design and control of the vacuum contact device of the mobile wall climbing robot implemented for vertical movement and operating in complex environments are considered. Variable design of vacuum contact devices capable for adaptation to changes in the dynamic environment on surfaces arbitrarily oriented in space was proposed. Practical significance: Various control modes for vacuum creation devices are proposed. It depends on the quality of the attachment surface and the operating environment of the wall climbing mobile robot.
Exploiting Human Pose for Weakly-Supervised Temporal Action Localization

Weakly-supervised temporal action localization aims to predict when and what actions occur in untrimmed videos with only video-level class labels. Most current methods make prediction based on global features, while ignoring the classification performance of local descriptions of human body. Additionally, these methods generate incomplete proposals via thresholding, which is too single and crude. To acquire high-quality proposals, we focus on incorporating local information, i.e. human body poses in videos, and propose a noval method called Class Activation and Pose Pattern (CAPP) for weakly-supervised temporal action localization. In our method, action proposals are generated by two modules: a Class Activation Sequence (CAS) module and a Pose Pattern Sequence (PPS) module. The CAS module fuses global features and local features to improve clip-level classification performance and the PPS module adds complementary proposals with high recall via pose pattern clustering. CAPP outperforms the state-of-the-art methods on both the THUMOS-14 and ActivityNet v1.2 datasets, which demonstrates the effectiveness of our method.
The Role of Bee Colony Algorithmics on Learning Machine by Data Mining Method

What the intellectual basis of the bee colony algorithm is based on can be easily stated in one sentence: Bees always choose the best way out of various obstacles in nature to access food. The bees choose the shortest route from the different paths to the food, with the bees secreting substances from the pheromone after finding the food, which is traced to white after rain. The bees find the path above when faced with a path that has more pheromones. In this paper, the studies show that the learning machine data set, which is a sampling of leukemia data, tested the accuracy of the error measurement on the bee colony algorithm, and the accuracy of the error before and after. It looks bad when performing data execution.
Kernel Circulant Object Tracking Based on Illumination Invariant Features

In order to improve accuracy and robustness of object tracking, and to resolve the problem that kernel circulant object tracking is easily affected by illumination changes, this paper presents a kernel circulant object tracking method based on illumination invariant features. Firstly, the locality sensitive histograms of input image are calculated and illumination invariant features are extracted. Then, the precise position of tracking object is obtained by the responded confidence image, which can be quickly computed in frequency domain between input image and template image. Tests of many video sequences demonstrate that the proposed method can track the target accurately and effectively. Comparing to the conditional kernel circulant tracking algorithm, the tracking error using our method decreases 24 pixels and the tracking precision increases 28%. As a result, the proposed algorithm has the better robustness under abruptly variant illumination and pose.
Recognition of Voiceprint Using Deep Neural Network Combined with Support Vector Machine

With the rapid development of artificial intelligence technology of deep learning method, it has been applied to many fields, especially to life science. In this paper, a novel approach for the task of voiceprint recognition was proposed. The combination of deep belief network (DBN) and support vector machine (SVM) was used to identify the voiceprint of 10 different individuals. Based on a 24-dimension Mel Frequency Cepstrum Coefficient (MFCC), the authors extracted 256-dimension deep voiceprint features via DBN model that is developed by stacking three layers of Restricted Boltzmann Machine (RBM), and conducted voiceprint recognition by approach of SVM. According to the recognition results, the new approach can significantly improve both of accuracy and efficiency when it was compared with traditional voiceprint features and recognition models. The low dimensional features of voiceprint was extracted into higher dimensions by DBN model, while SVM can avoid the elevation of computation complexity caused by the increases of feature dimension. The combined strengths have been fully expressed by the experimental results.