Reviewerly/exHarmony huggingface dataset:
Pengaruh Motivasi Belajar Terhadap Prestasi Belajar Matematika Siswa Kelas VIII SMPN 1 Kuala Behe

&lt;em&gt;The purpose of this study was to determine the effect of learning motivation on mathematics learning achievement of eighth grade students of SMP N 1 Kuala Behe acedemic year of 2018/2019 Landak Regency. This type of research was quantitative with ex post facto approach. The population of all eighth grade students at SMP N 1 Kuala Behe. The number of sample in this study was 64 students who were selected using saturated sampling technique. The data analysis technique used simple linear regressionwith prerequisite test namely normality of Kolmogorov-Smirnov method, heteroscedasticity of Glejser method, linearity using test for linearity, multicolonity using Variance Inflation Factor (VIF), and autocorrelation of Durbin-Watson method. The result shows that there is an effect of learning motivation on mathematics learning achievement of eighth grade students of SMP N 1 Kuala Behe academic year 2018/2019 Landak Regency. The result of R square = 0,915 x 100% = 19,5% which indicates that the mathematics learning achievement of eighth grade students of SMP N 1 Kuala Behe can be explained by learning motivation of 19,5%.&lt;/em&gt;
Draculab: A Python Simulator for Firing Rate Neural Networks With Delayed Adaptive Connections

Draculab is a neural simulator with a particular use scenario: firing rate units with delayed connections, using custom-made unit and synapse models, possibly controlling simulated physical plants. Draculab also has a particular design philosophy. It aims to blur the line between users and developers. Three factors help to achieve this: a simple design using Python’s data structures, extensive use of standard libraries, and profusely commented source code. This paper is an introduction to Draculab’s architecture and philosophy. After presenting some example networks it explains basic algorithms and data structures that constitute the essence of this approach. The relation with other simulators is discussed, as well as the reasons why connection delays and interaction with simulated physical systems are emphasized.
Learning Ex Nihilo

This paper introduces, philosophically and to a degree formally, the novel concept of learn- ing ex nihilo, intended (obviously) to be analogous to the concept of creation ex nihilo. Learning ex nihilo is an agent’s learning “from nothing”, by the suitable employment of inference schemata for deductive and inductive reasoning. This reasoning must be in machine-verifiable accord with a formal proof/argument theory in a cognitive calculus (i.e., here, roughly, an intensional higher-order multi-operator quantified logic), and this reasoning is applied to percepts received by the agent, in the context of both some prior knowledge, and some prior and current interests. Learning ex nihilo is a challenge to con- temporary forms of ML, indeed a severe one, but the challenge is here offered in the spirit of seeking to stimulate attempts, on the part of non-logicist ML researchers and engineers, to collaborate with those in possession of learning-ex nihilo frameworks, and eventually attempts to integrate directly with such frameworks at the implementation level. Such integration will require, among other things, the symbiotic interoperation of state-of-the- art automated reasoners and high-expressivity planners, with statistical/connectionist ML technology.
Breast Cancer Diagnosis using a Neural Network

This work presents a neural network used to diagnosis patients with benign or malignant breast cancer. The study is carried out using the Breast Cancer Wisconsin dataset. To solve the problem a feedforward neural network (NN) with multilayers was used. In the work, the implementation was made in Python, using two different libraries (sklearn and keras). Experimental results were obtained by performing simulations in both developed applications, and the performance of the neural classifier was evaluated through the performance measures of the classification systems and the ROC curve. The results were promising, since the NN was able to discriminate with high accuracy the two separable sets discriminating the benign or malignant tumor patients.
Three-dimensional Radial Visualization of High-dimensional Continuous or Discrete Data

This paper develops methodology for 3D radial visualization of high-dimensional datasets. Our display engine is called RadViz3D and extends the classic RadViz that visualizes multivariate data in the 2D plane by mapping every record to a point inside the unit circle. The classic RadViz display has equally-spaced anchor points on the unit circle, with each of them associated with an attribute or feature of the dataset. RadViz3D obtains equi-spaced anchor points exactly for the five Platonic solids and approximately for the other cases via a Fibonacci grid. We show that distributing anchor points at least approximately uniformly on the 3D unit sphere provides a better visualization than in 2D. We also propose a Max-Ratio Projection (MRP) method that utilizes the group information in high dimensions to provide distinctive lower-dimensional projections that are then displayed using Radviz3D. Our methodology is extended to datasets with discrete and mixed features where a generalized distributional transform is used in conjuction with copula models before applying MRP and RadViz3D visualization.
Augmented reality objects design in augmented story book mobile application for better engagement

&lt;span lang="EN-US"&gt;The folktale is a kind of story that &lt;/span&gt;&lt;span lang="EN-US"&gt;has some good points, such as developing a sense of story and shaping up positive attitudes towards children. However, people seem to abandon those good points and often give the children smartphones instead of folktale through the conventional book. Recently, smartphones’ capabilities have rapidly improved, so that augmented reality (AR) story books which combine conventional book and smartphone can be developed. This research is conducted to examine which one from three provided models that work best for engagement in a folktale augmented book. Those designs will be discussed in both subjective and objective approach, correspondingly. The result from subjective approach is the animated video AR design becomes the most pleasing design. The picture and narrator’s voice AR design, by contrast, becomes the most efficient design based on objective approach.&lt;/span&gt;
A Data Driven Approach for Motion Planning of Autonomous Driving Under Complex Scenario.

Intelligent motion planning is one of the core components in automated vehicles, which has received extensive interests. Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability. Sampling based methods cannot guarantee the optimality of the generated trajectories. Whereas the optimization-based methods are not able to perform motion planning in real-time, and limited by the simplified formalization. In this work, we propose a learning-based approach to handle those shortcomings. Mixed Integer Quadratic Problem based optimization (MIQP) is used to generate the optimal lane-change trajectories which served as the training dataset for learning-based action generation algorithms. A hierarchical supervised learning model is devised to make the fast lane-change decision. Numerous experiments have been conducted to evaluate the optimality, efficiency, and generalization capability of the proposed approach. The experimental results indicate that the proposed model outperforms several commonly used motion planning baselines.
Enabling heterogeneous recommendations in OSS development

Open source software (OSS) forges contain rich data sources that are useful for supporting development activities. Research has been done to promote techniques and tools for providing open source developers with innovative features aiming at obtaining improvements in terms of development effort, cost savings, and developer productivity, just to mention a few. In the context of the EU H2020 CROSSMINER project we are conceiving a set of recommendations to assist software programmers in different phases of the development process. To this end, we defined a graph-based representation to encode in a homogeneous manner different aspects of OSS ecosystems as well as to incorporate various well-founded recommendation techniques. Following the proposed paradigm, we have implemented recommender systems for providing various artifacts, such as third-party libraries and API usage. The preliminary results we achieved so far are promising: our proposed systems are able to suggest highly relevant items with respect to the current development context. In this paper, we describe what has been achieved so far as well as our planned medium and longer-term objectives. As a proof of concept, we present a use case where we built a context-aware recommender system to recommend API function calls and usage patterns.
ENHANCING DISTANCE EDUCATION STUDENTS’ LEARNING EXPERIENCE THROUGH EMERGING TECHNOLOGIES

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 3022-3029ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0797Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Developing a QoE Monitoring Approach for Video Service Based on Mobile Terminals

Quality of experience (QoE) is crucial for wireless network operators with respect to both technical evolution and profit promotion. However, operators are still unable to monitor user QoE effectively because traditional network quality indices are not directly related to the user’s audiovisual experiences. Motivated by the fact that mobile devices are the network elements closest to the users, we explore the possibility of estimating user QoE by capitalizing on mobile terminal capabilities. For this purpose, we first collect over eighty thousand data records through a self-developed mobile application operating in the real world conditions with the support of a prominent Chinese wireless network operator (i.e., China Unicom). The collected data consist of 89 objective parameters concerning wireless video services and 4 types of subjective user scores. Then, we perform data preprocessing and feature selection based on Spearman correlation analyses. Finally, we establish two predictive models for QoE estimation based on the classification tree C4.5 algorithm and the gradient boosting decision tree (GBDT) algorithm, respectively. The experimental results demonstrate that the two decision tree-based models outperform other machine learning algorithms. Specifically, the prediction accuracy of the GBDT is approximately 70% for a five-level scale and approaches 90% for a more practical 3-level scale. Therefore, this study strongly demonstrates the feasibility of the user terminal-based approach for QoE prediction.
Multilevel polygonal descriptor matching defined by combining discrete lines and force histogram concepts

A new method allowing to describe shapes from a set of polygonal curves using a relational descriptor is proposed in this paper. An approach based on discrete lines at several increasing widths is run on the contour of an object to provide a multi-level polygonal representation from accurate description to more and more rough aspects. On each polygon, a force histogram is calculated to define a relational feature signature following a set of directions integrating both spatial relation organization and disparities of the shape in a same distribution. Three different matching schemes are proposed to compare multilevel distributions: global representation, level to level following extracted maxima. This new method is fast and a first experimental study achieved on a common database shows its good behavior.
Critical Awareness of Media and Teacher Education: An Experience with Colombian ELT Pre-Service Teachers

Today's media spectacle is shaping societies, cultures, and even our own values and identities at a global scale.Even though media can be used to promote education, democratic self-expression, and social progress, it is often used to perpetuate social injustices, prejudice, and violence.Media texts are exponentially displayed in physical and virtual public spaces and their impact on humanity is unimaginable.Teachers of all fields face the challenge and the responsibility to educate sensitive humans who are able to critically consume or produce media messages.English language teachers are no exception.In this paper, the author describes a qualitative research study conducted by a teacher educator exploring how a group of Colombian pre-service English teachers developed critical awareness of media as they critically deconstructed advertisements displayed in the public spaces of their rather rural communities.Implications for teacher education emerging from this study and new questions for further research will be presented.
ВИКОРИСТАННЯ ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНИХ ТЕХНОЛОГІЙ ДЛЯ ФОРМУВАННЯ НАУКОВО-ДОСЛІДНИЦЬКОЇ КОМПЕТЕНТНОСТІ МАЙБУТНІХ ДОКТОРІВ ФІЛОСОФІЇ

The article deals with the problem of the information and communication technologies (ICT) use for the scientific and research competency formation of the future doctors of philosophy (PhD) in specialty 011 "Educational, pedagogical sciences", specialization "Theory and teaching methods: Germanic / Romance languages". The analysis of approaches to the definition of the scientific research competency content and structure has been carried out. The research oriented general and special competencies set needed to the PhD in methodology of teaching foreign languages and cultures, which are formed mainly in the process of studying the academic disciplines "Methodology and the scientific research technologies in the methods of teaching foreign languages and cultures" and "Information technologies in scientific research practice" has been specified. It has also been determined that there is a certain difficulty in finding the optimal way of transferring knowledge from the field of ICT to the future PhD professionally oriented practical activity, which requires a well-grounded selection of cloud, multimedia, projection, information search, analytical research technologies and computer-based tools as a purpose of their study, and further use by postgraduate students at all stages of the dissertation preparation (design, information search, analytical-modeling, experimental-research, narrative, practical). The examples of the postgraduate students use of the Web-sites, blogs, Wiki, cloud services for data storage, scientific search systems, Smart-technologies, the Internet information resources, educational electronic editions and video services in the process of self-preparation for interactive lectures and practical classes in the discipline "Methodology and the scientific research technologies in the methods of teaching foreign languages and cultures" have been given.
Whose Research is it Anyway? Academic Social Networks Versus Institutional Repositories

INTRODUCTION Looking for ways to increase deposits into their institutional repository (IR), researchers at one institution started to mine academic social networks (ASNs) (namely, ResearchGate and Academia.edu) to discover which researchers might already be predisposed to providing open access to their work. METHODS Researchers compared the numbers of institutionally affiliated faculty members appearing in the ASNs to those appearing in their institutional repositories. They also looked at how these numbers compared to overall faculty numbers. RESULTS Faculty were much more likely to have deposited their work in an ASN than in the IR. However, the number of researchers who deposited in both the IR and at least one ASN exceeded that of those who deposited their research solely in an ASN. Unexpected findings occurred as well, such as numerous false or unverified accounts claiming affiliation with the institution. ResearchGate was found to be the favored ASN at this particular institution. DISCUSSION The results of this study confirm earlier studies’ findings indicating that those researchers who are willing to make their research open access are more disposed to do so over multiple channels, showing that those who already self-archive elsewhere are prime targets for inclusion in the IR. CONCLUSION Rather than seeing ASNs as a threat to IRs, they may be seen as a potential site of identifying likely contributors to the IR.
Use of Information and Communication Technology ICT in Agriculture to Uplift Small Scale Farmers in Rural Pakistan

The research was conducted to determine the agricultural information needs of the rural farmers and to provide a solution to access this information in the province of Khyber Pakhtunkhwa (KPK) Pakistan.The research was conducted in Ten (10) Villages of different districts of KPK and a number of 460 respondents were selected for study, the study was conducted through a survey method, Face to Face interviews, focus group discussion and farmer meetings, data was collected and analyzed through descriptive statistics, percentage, frequency tables.The results indicated that most of the respondents (34.78%) fall in age group between 41-50 years, it is also found that the first preferred source of information of the farmers is fellow farmers (37.39%) following by Radio (29.13%),Major barriers to access modern information systems is high illiteracy (39.43%) and non-availability of local information centers (36.08%).During the study, a large of the number of farmers acknowledged that they are not familiar with modern practices of agriculture because they are not facilitated with new technologies to access agricultural information; as a result, they are not using such techniques and relying on traditional ways.The research study also identified that due to the highly localized nature of information and several barriers this is very essential to equip rural farmers with updated information through use of (ICT) to enhance production and improve their livelihood.
DSMM: A Dynamic Setting for Memory Management in Apache Spark

Apache Spark (Spark) is a unified analytics engine for large-scale data processing. Unlike traditional data processing engines like Hadoop, Spark is a framework that caches data in memory. Therefore, memory management in Spark is importance. However, there are several factors that interfere with memory management. First, if users want to cache data in memory, they need to choose their own storage level. In this case, if they do not select the optimal storage level, Spark will be put a heavy burden on memory. Next, users need to select the ratio for spark memory directly within Spark. If they do not choose optimal ratio for spark memory, garbage collection overheads will be incurred. In this poster, we propose DSMM that dynamically select the above factors on the system for memory management. Our experimental result shows 13% execution time improvement as compared to standard Spark.
How Does the Data Set and the Number of Categories Affect CNN-based Image Classification Performance?

Convolution neural network(CNN) has been widely applied in many fields and achieved excellent results, especially in image classification tasks.As we all know, many factors affect the performance of image classification.In particular, the size of training data sets and the number of categories are important factors affecting performance.While for most people, a large number of training data set are difficult to obtain or need to do a classification task with a large number of categories.Thus, we consider two questions of this approach: How does the size of the data set affect performance?How does the number of categories affect performance?In order to figure out these two questions, we constructed two types of experiment: Experiment 1, changing the number of categories and exploring how the number of categories affects performance in image classification task.There are 7 groups experiment performed by increasing the number of categories and performed 5 times experiment in each group (35 times experiment in total).Observe the change in accuracy to analyze the impact of the number of categories on performance.Experiment 2, changing data set size and exploring how the data set size affect performance.For each k-classification experiment, we do 5 groups by increasing the size of the training set.There are 35 groups experiment performed 5 times experiment in each group (175 times experiment in total).Observe changes in accuracy to analyze the effect of data set size on performance.For the CNN-based network, the results of experiment 1 show that the more categories, the worse the performance, and the less categories, the better the performance.In addition, when the number of categories to be classified is large, sometimes better accuracy can be obtained.The results of experiment 2 show that the larger the training set, the higher the test accuracy.When the training data set are insufficient, better results can be obtained.Therefore, in classification experiment, when the data set size is small or the number of categories is large, we can do more experiments and retain the best results.Results of this paper not only can guide us to do experiments on image classification, but also have important guiding significance for other experiments based on deep learning.
Connected Resources - Empowering Older People to Age Resourcefully

With this video, we showcase the possibilities of the Internet of Things and machine learning to support improvisation in older people. Available smart products for older people tend to be developed with narrowly predefined use-scenarios, based on stereotypes of elderly as passive, immobile, and technologically incompetent. This type of products may restrict older people's existing capabilities of resourcefulness and autonomy [1]. As an alternative approach, we created Connected Resources, a family of combinable sensors and actuators for older people that encourages them to negotiate and situate use according to their personal and changing circumstances with a high level of freedom. The objects learn older people's unique ways of using these artifacts and share them through an online platform to encourage the development of new strategies. In this way, Connected Resources celebrate older people's creativity, instead of providing normative solutions or enforcing compliance with certain behavior.
Features of reflection of projects of Russian regional universities in the Internet space

И. Е. Бельских, Т. Б
Perceptual Adversarial Networks With a Feature Pyramid for Image Translation

This paper investigates the image-to-image translations problems, where the input image is translated into its synthetic form with the original structure and semantics preserved. Widely used methods compute the pixel-wise MSE loss, which are often inadequate for high-frequency content and tend to produce overly smooth results. Concurrent works that leverage recent advances in conditional generative adversarial networks (cGANs) are proposed to enable a universal approach to diverse image translation tasks that traditionally require specific loss functions. Despite the impressive results, most of these approaches are notoriously unstable to train and tend to induce blurs. In this paper, we decompose the image into a set of images by a feature pyramid and elaborate separate loss components for images of specific bandpass. The overall perceptual adversarial loss is able to capture not only the semantic features but also the appearance.