Reviewerly/exHarmony huggingface dataset:
Approximate Learning of Limit-Average Automata

Limit-average automata are weighted automata on infinite words that use average to aggregate the weights seen in infinite runs. We study approximate learning problems for limit-average automata in two settings: passive and active. In the passive learning case, we show that limit-average automata are not PAC-learnable as samples must be of exponential-size to provide (with good probability) enough details to learn an automaton. We also show that the problem of finding an automaton that fits a given sample is NP-complete. In the active learning case, we show that limit-average automata can be learned almost-exactly, i.e., we can learn in polynomial time an automaton that is consistent with the target automaton on almost all words. On the other hand, we show that the problem of learning an automaton that approximates the target automaton (with perhaps fewer states) is NP-complete. The abovementioned results are shown for the uniform distribution on words. We briefly discuss learning over different distributions.
Improvement on "Secure multi-party quantum summation based on quantum Fourier transform"

Recently, Yang et al. (Quantum Inf Process:17:129, 2018) proposed a secure multi-party quantum summation protocol allowing the involved participants to sum their secrets privately. They claimed that the proposed protocol can prevent each participant's secret from being known by others. However, this study shows that the participant who prepares the initial quantum states can obtain other participants' secrets with an inverse quantum Fourier transform attack. A modification is then proposed here to solve this problem.
Welcome to the metamodel: A reply to Pablé

In 2017, Empedocles: European Journal for the Philosophy of Communication published an article by Adrian Pablé, an integrationist linguist, which considers the contribution that Roy Harris’ theory of sign can make to communication theory in terms of the constitutive metamodel of communication theory. This brief response to that contribution concludes that integrationism is a useful but limited perspective and that its claim to exclusive validity should be rejected by communication theorists.
Analisis Potensi Bencana Banjir HIlir Daerah Aliran Sungai Way Kuripan Kota Bandar Lampung

Floods are always a problem, especially in residential areas. This is in line with the development of people's lives. The Way Kuripan River is a river that passes through several villages in the city of Bandar Lampung, which often overflows into community settlements if the rainy season arrives, especially in the Pesawahan, Kuripan and Karang Cities which are downstream from the river. Therefore, it is necessary to have an analysis to find out the river water level that passes through an area if the rainy season arrives whether or not it can be accommodated by the river body or not. Based on the survey in the field, it is known that the width of the Way Kuripan River is found in the Betung Hills, West Tanjung Karang District, which is 20 m and the depth of the river is 4.51 m. if the rainy season arrives, the upper reaches of the Way Kuripan River have a water level that is 0.5 m - 3.0793 m so that the depth of the river is still able to be accommodated by different river bodies with downstream rivers in Pesawahan and Karang Cities where due to sedimentation the depth of the river is only 2.78 m and the width of the river is 10 m, while during the rainy season the river water level reaches 0.5 - 5,216 m. so that the water that passes through is unable to be accommodated in the river body and overflows in the area.
COMIC LEARNING MEDIA DEVELOPMENT IN EFFORTS TO GROW LITERACY CULTURE AND ENVIRONMENTAL CARE ATTITUDE FOR ELEMENTARY SCHOOL STUDENT

&lt;p&gt;&lt;em&gt;This research was aimed to describe the science comic design with &lt;/em&gt;&lt;em&gt;the environmental care theme as a learning medium for developing the literacy culture and environmental care attitude for elementary school students&lt;/em&gt;&lt;em&gt;. Research and Development (R &amp;amp; D)&lt;/em&gt;&lt;em&gt; approach was implemented by utilizing 4D models which was limited to the Develop stage only&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;em&gt; The comic media was reviewed and assessed by an expert of media, a linguist, an expert of material, and three elementary school teachers.&lt;/em&gt;&lt;em&gt;. &lt;/em&gt;&lt;em&gt;The data were collected by using observation technique and questionnaire. The results showed that the comic science media with the theme of environmental care can be used as a learning medium that can develop a culture of literacy and attitudes of environmental care in primary schools based on the contents of the curriculum standard 2013. The quality result of the assessment from the expert and the student response indicated a good category.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

In this paper, we present a new model of e-Learning platforms based on semantic micro services using discovery, selection and composition methods to generate learning paths.In this model, each semantic micro service represents an elementary educational resource that can be a course, an exercise, a tutorial or an evaluation implementing a precise learning path objective.The semantic micro services are described using ontologies and deployed in multi-instances in a cloud environment according to a load balancing and a fault tolerance system.Learners' requests are sent to a proxy micro service having learning paths abstract structures represented as an oriented graph.Proxy micro service analyses the request to define the learner profile and context in order to provide him with the semantic micro services responsible of the educational resources satisfying his functional and non-functional needs.In this model, to achieve an optimal learning path generation a two steps process is employed, where local optimization uses semantic discovery and selection based on a matchmaking algorithm and a quality of service measurement, and global optimization adopts an ant colony optimization algorithm to select the best resource combination.Our experimental results show that the proposed model can effectively returns optimized learning paths considering individual, collective and pedagogical factors.
FMRI data augmentation via synthesis

We present an empirical evaluation of fMRI data augmentation via synthesis. For synthesis we use generative mod-els trained on real neuroimaging data to produce novel task-dependent functional brain images. Analyzed generative mod-els include classic approaches such as the Gaussian mixture model (GMM), and modern implicit generative models such as the generative adversarial network (GAN) and the variational auto-encoder (VAE). In particular, the proposed GAN and VAE models utilize 3-dimensional convolutions, which enables modeling of high-dimensional brain image tensors with structured spatial correlations. The synthesized datasets are then used to augment classifiers designed to predict cognitive and behavioural outcomes. Our results suggest that the proposed models are able to generate high-quality synthetic brain images which are diverse and task-dependent. Perhaps most importantly, the performance improvements of data aug-mentation via synthesis are shown to be complementary to the choice of the predictive model. Thus, our results suggest that data augmentation via synthesis is a promising approach to address the limited availability of fMRI data, and to improve the quality of predictive fMRI models.
Take Note: Using Errorless Learning to Promote Memory Notebook Training

One of the most common outcomes of brain injury is a memory impairment, manifest in an inability to remember new information. A. Baddeley and B. A. Wilson suggest that memory-impaired people rely more on implicit memory when learning new information, but that implicit memory is of little use in correcting errors once erroneous responses have been made. Novel paired associates were studied under two conditions: errorless and errorful learning. During notebook training sessions only the errorless and errorful procedures indicated earlier were carried out: there was no additional free or cued recall during training period. To assess the generality of the errorless learning advantage in the acquisition of novel associations, we have recently carried out of group studies of verbal association learning. In contrast to our findings, J. J. Evans et al. have found more limited success in the use of errorless learning.
The Effectiveness of Problem-Based Learning on the Hoax Informations to Improve Students’ Critical Thinking Skills (Related to Some Foods and Beverages)

The hoax informations that spread through social media had a negative impact on society. Therefore, research had been conducted in Gedongtataan Upper Secondary School No.1 to describe the effectiveness of problem-based learning model on hoax informations in improving students' critical thinking skills. This study was used the matching only pretest-posttest control group design. The sample in this study was the 11th grade of IPA 4 (experimental class) and the 11th grade of IPA 5 (control class) which was obtained by using purposive sampling technique. The results showed that the average n-gain in the experimental and control classes was 0.7 and 0.6, respectively. Based on this, it could be concluded that the problem based learning model on the hoax informations was effective in improving students' critical thinking skills. Keywords: critical thinking skills, hoax informations, problem-based learning.
DEVELOPING MULTIMEDIA-BASED LISTENING MATERIALS FOR THE TENTH GRADE STUDENTS

This study aimed at developing the supplementary multimedia-based listening materials for tenth grade students in the first semester using scientific approach Curriculum of 2013. This study was research development using the model by Brach (2009) and was conducted at MAN 1 Pamekasan. The final product of this study was in a macro-media flash file attached inside CDs. The result of the product evaluation and try-out showed that the materials were interestingly presented in terms of topics and listening activities. Moreover, it was also able to motivate students to learn listening.
Pengaruh Konsep Diri dan Kebiasaan Belajar Terhadap Hasil Belajar Matematika Siswa Kelas VIII SMP YP PGRI 4 MAKASSAR

This research aims to: (1) understand the self-concept of students, habit of students, and students mathematics learning outcomes, (2) determine how the effects of self-concept on students mathematics learning outcomes, the influence of learning habit on students' mathematics learning outcomes, the influence of self-concept and learning habit with the students' mathematics learning outcomes.This research wasex-post facto research. The research was conducted in SMP YP PGRI 4 Makassar with sample 40 people. Data collection in this research used question naires and do cumentation. Technique of data analysis used simple linear regression and multiple regressions. The results showed that (1) the concept of self concept of the students, learning habit, and learning outcomes have a tendency in the high category, (2) a significant difference between self concept and mathematics learning outcomes, learning habit with students' mathematics learning outcomes, self concept and learning habit with the learning outcomes of VIII grademath students of SMP YP PGRI 4 Makassar.
On the Convex Behavior of Deep Neural Networks in Relation to the Layers' Width

The Hessian of neural networks can be decomposed into a sum of two matrices: (i) the positive semidefinite generalized Gauss-Newton matrix G, and (ii) the matrix H containing negative eigenvalues. We observe that for wider networks, minimizing the loss with the gradient descent optimization maneuvers through surfaces of positive curvatures at the start and end of training, and close to zero curvatures in between. In other words, it seems that during crucial parts of the training process, the Hessian in wide networks is dominated by the component G. To explain this phenomenon, we show that when initialized using common methodologies, the gradients of over-parameterized networks are approximately orthogonal to H, such that the curvature of the loss surface is strictly positive in the direction of the gradient.
A Graph-Based Color Lines Model for Image Analysis

This paper addresses the problem of obtaining a concise description of spectral representation for color images. The proposed method is a graph-based formulation of the well-known Color Lines model. It generalizes the lines to piece-wise lines, been able to fit more complex structures. We illustrate the goodness of proposed method by measuring the quality of the simplified representations in images and videos. The quality of video sequences reconstructed by means of proposed color lines extracted from the first frame demonstrates the robustness of our representation. Our formalism allows to address applications such as image segmentation, shadow correction among others.
Real-time Vision-based Depth Reconstruction with NVidia Jetson

Vision-based depth reconstruction is a challenging problem extensively studied in computer vision but still lacking universal solution. Reconstructing depth from single image is particularly valuable to mobile robotics as it can be embedded to the modern vision-based simultaneous localization and mapping (vSLAM) methods providing them with the metric information needed to construct accurate maps in real scale. Typically, depth reconstruction is done nowadays via fully-convolutional neural networks (FCNNs). In this work we experiment with several FCNN architectures and introduce a few enhancements aimed at increasing both the effectiveness and the efficiency of the inference. We experimentally determine the solution that provides the best performance/accuracy tradeoff and is able to run on NVidia Jetson with the framerates exceeding 16FPS for 320 x 240 input. We also evaluate the suggested models by conducting monocular vSLAM of unknown indoor environment on NVidia Jetson TX2 in real-time. Open-source implementation of the models and the inference node for Robot Operating System (ROS) are available at this https URL.
The Implementation of Gerakan Literasi Sekolah (Gls) Program in Elementary School

The purpose of Gerakan Literasi Sekolah (GLS) program is to manage schools as a&#x0D; literary ecosystem. The components of GLS program such as facilities &amp; infrastructure,&#x0D; activities, and school literacy team determine the successful implementation of&#x0D; the program. This study aims at describing the condition of the GLS program&#x0D; implementation in state elementary schools of Sleman district. Data were obtained&#x0D; through a questionnaire by elementary school teachers. This reserach gauges the&#x0D; implementation level of GLS program from several aspects like the conditions of&#x0D; facilities &amp; infrastructure, the school literacy team, the public involvement, and the&#x0D; activities of GLS program. The research resulted that 44% of the schools in Sleman&#x0D; district have been implemented GLS program in good category.
Multi-Element Long Distance Dependencies: Using SPk Languages to Explore the Characteristics of Long-Distance Dependencies

In order to successfully model Long Distance Dependencies (LDDs) it is necessary to understand the full-range of the characteristics of the LDDs exhibited in a target dataset. In this paper, we use Strictly k-Piecewise languages to generate datasets with various properties. We then compute the characteristics of the LDDs in these datasets using mutual information and analyze the impact of factors such as (i) k, (ii) length of LDDs, (iii) vocabulary size, (iv) forbidden subsequences, and (v) dataset size. This analysis reveal that the number of interacting elements in a dependency is an important characteristic of LDDs. This leads us to the challenge of modelling multi-element long-distance dependencies. Our results suggest that attention mechanisms in neural networks may aide in modeling datasets with multi-element long-distance dependencies. However, we conclude that there is a need to develop more efficient attention mechanisms to address this issue.
THE -PROVABILITY LOGIC OF

Abstract For the Heyting Arithmetic HA, $HA^{\text{*}} $ is defined [14, 15] as the theory $\left\{ {A|HA \vdash A^\square } \right\}$ , where $A^\square $ is called the box translation of A (Definition 2.4). We characterize the ${\text{\Sigma }}_1 $ -provability logic of $HA^{\text{*}} $ as a modal theory $iH_\sigma ^{\text{*}} $ (Definition 3.17).
Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification

The graph convolution network (GCN) is a widely-used facility to realize graph-based semi-supervised learning, which usually integrates node features and graph topologic information to build learning models. However, as for multi-label learning tasks, the supervision part of GCN simply minimizes the cross-entropy loss between the last layer outputs and the ground-truth label distribution, which tends to lose some useful information such as label correlations, so that prevents from obtaining high performance. In this paper, we pro-pose a novel GCN-based semi-supervised learning approach for multi-label classification, namely ML-GCN. ML-GCN first uses a GCN to embed the node features and graph topologic information. Then, it randomly generates a label matrix, where each row (i.e., label vector) represents a kind of labels. The dimension of the label vector is the same as that of the node vector before the last convolution operation of GCN. That is, all labels and nodes are embedded in a uniform vector space. Finally, during the ML-GCN model training, label vectors and node vectors are concatenated to serve as the inputs of the relaxed skip-gram model to detect the node-label correlation as well as the label-label correlation. Experimental results on several graph classification datasets show that the proposed ML-GCN outperforms four state-of-the-art methods.
Sistem Pendukung Keputusan Pengadaan Perangkat Teknologi Informasi Menggunakan Metode Logic Fuzzy Pada PT. Kojo Balairaja

This study discusses decision support system of the procurement information technology devices using fuzzy logic method at PT. Kojo Balairaja. The research objectives are (1) to determine the eligibility criteria for the procurement of information technology devices that are appropriate to the needs of the company, (2) to determine costs according to the company's budget, (3) to determine the quality of devices held, and (4) to determine the update of the devices held. One of the difficullties that found is the manager’s in making decsion. An example manager’s decision in considering some aspects such as costs and needs but they do not consider eligibility criteria, the quality and updating of the equipment needed by the company. The method used the Fuzzy Logic method karena metode ini dapat memetakan perangkat yang dibutuhkan. The results of this study, namely the system built can help managers at PT. Kojo Balairaja in making decisions to procure information technology devices in accordance with the eligibility criteria, namely managers can approve, consider and reject the device request, the quality and updating of the equipment needed and can predict the cost of good quality but the price is moderate and affordable by the company.
The scope of the concept of information and the future of information science

The key concept of information science is the concept of information which is tied to a number of complications. The main problem is that there is no definition of this concept. The purpose of this article is an analysis of the concept of information from the position of classical logic. The main method of the article is a conceptual analysis. First, we briefly deal with the overview of the concepts of information, with concepts and their definition as such and with the scope of the concept of information. Then, we provide an analysis of 31 important definitions of the concept of information which were developed within the scope of information science and related fields, and we consider relations between the concept of information and the concepts in other disciplines. Conceptual analysis of the concept of information leads to the conclusion that information is probably a concept that somehow addresses the entire reality, thus that it is a term, which is in the classical logic described as transcendental. This fact, in the view of the fact that information science is a special field, seems to be a serious methodological problem. Problems associated with the broadness of the concept of information have three possible solutions: transformation of information science into the universal science, narrowing the concept of information to a special term, or replacement of the concept of information by a different one. At the end of the article, we briefly point out our solution to the problem.
Investigating Users’ Responses to Context-Aware Presentations on Large Displays in Public Transport

Public displays are increasingly used in public transport to present information such as departure and arrival times or network maps. Since this information is displayed generically, users often have problems to find the specific information they need. We propose context-aware visualizations on public displays to support passengers by improving personalized information access. Several visualizations for this domain were identified, for example, highlighting individual route or pricing information by fading out the background or increasing readability by font size adaptation. To investigate the influence of adapted content on the user we tested prototypical presentations that show personalized information concerning personal trips. In our user study with 20 participants we analyzed these visualizations to compare their efficiency in contrast to non-adaptive content by measuring time to perform specific tasks. This work presents the results of our user study. They show that especially highlighted information supports the user in finding personalized information faster.
Advanced Designing Assistant System for Smart Design Based on Product Image Dataset

Existing product images are very important references for designing a new scheme. However, the designers have to collect and organize the product image data manually without proper tools, which may be time-consuming, inefficient and expensive. The rapid growth of product design has called for a smart system to assist designers with a quick start in designing a new product. Therefore, we propose an advanced designing assistant system (ADAS) to help the designers handle the large-volume product images more efficiently and create better design. The ADAS utilizes big data and artificial intelligence technology to achieve mass product data acquisition, analysis, retrieval, and design scheme generation. The ADAS utilizes builds a product image dataset firstly to decrease high cost of time and money in images collection task. Furthermore, based on this dataset, the ADAS develops three applications: (1) image retrieval and infringement analysis, (2) multi-label semantic annotation, (3) automatic design scheme generation. Experiments are conducted to validate the merits of the proposed system. And the results show that the ADAS could support designers with high quality from initial data collection to image retrieval, infringement analysis, semantic learning, and design scheme generation throughout the entire flow of the design task, greatly shortening the design period and improving efficiency.