Reviewerly/exHarmony huggingface dataset:
Identifiability and estimation of recursive max-linear models

We address the identifiability and estimation of recursive max-linear structural equation models represented by an edge weighted directed acyclic graph (DAG). Such models are generally unidentifiable and we identify the whole class of DAGs and edge weights corresponding to a given observational distribution. For estimation, standard likelihood theory cannot be applied because the corresponding families of distributions are not dominated. Given the underlying DAG, we present an estimator for the class of edge weights and show that it can be considered a generalized maximum likelihood estimator. In addition, we develop a simple method for identifying the structures of the DAGs. With probability tending to one at an exponential rate with the number of observations, this method correctly identifies the class of DAGs and, similarly, exactly identifies the possible edge weights.
Infrared and Visible Image Matching Algorithm Based on SIFT and LDB

A new local feature extraction method (BSPL) is proposed and applied to heterogeneous image matching to solve the problem that the traditional SIFT features have poor matching performance in heterogeneous image matching. A number of improvements have been made to ensure that common features of heterogeneous images can be extracted efficiently. The gradient histogram-equalized image is used as the input matching image; The bilateral filtering is used to construct the scale space pyramid to replace the Gaussian filtering of the traditional SIFT, which can make the details such as the edges of the image better preserved; PCA-based LDB descriptor is used as feature expression to improve the robustness of feature expression. Experimental results show that the proposed local feature descriptor has rotation and scale invariance, and effectively improves the number of matching points, matching accuracy, matching precision and matching adaptability, which is an effective infrared and visible image matching method.
Quantum Distinguishing Complexity, Zero-Error Algorithms, and Statistical Zero Knowledge

We define a new query measure we call distinguishing complexity, denoted QD(f) for a Boolean function f. Unlike a query algorithm, which must output a state close to |0> on a 0-input and a state close to |1> on a 1-input, a quantum distinguishing algorithm can output any state, as long as the output states for any 0-input and 1-input are distinguishable. Using this measure, we establish a new relationship in query complexity: For all total functions f, Q_0(f)=O~(Q(f)^5), where Q_0(f) and Q(f) denote the zero-error and bounded-error query complexity of f respectively, improving on the previously known sixth power relationship.We also define a query measure based on statistical zero-knowledge proofs, QSZK(f), which is at most Q(f). We show that QD(f) in fact lower bounds QSZK(f) and not just Q(f). QD(f) also upper bounds the (positive-weights) adversary bound, which yields the following relationships for all f: Q(f) >= QSZK(f) >= QD(f) = Omega(Adv(f)). This sheds some light on why the adversary bound proves suboptimal bounds for problems like Collision and Set Equality, which have low QSZK complexity.Lastly, we show implications for lifting theorems in communication complexity. We show that a general lifting theorem for either zero-error query complexity or for QSZK would imply a general lifting theorem for bounded-error query complexity.
Analisis PIECES dalam Implementasi Kebijakan E-Learning di IAIN Kendari

This study aims to analyze the implementation of e-learning policies at IAIN Kendari. Using a qualitative descriptive method, the data was collected through observation at the online page of https://elearning.iainkendari.ac.id, interviews and documentation. The results of PIECES analysis calculations in the application of e-learning are seen from several aspects, including: performance, information, economy, control and services are in the category of important with the value of 3.46%, 3.82%, 3.77%, 3.85% and 3.88% respectively. Regarding the aspect of efficiency, the value is 4.28% within the category Very Important. Whereas, the implementation of e-learning learning policies using Edward's theory was investigated from four aspects, namely: 1) communication that was conducted through the socialization of e-learning workshops; 2) resources including human/technician resources that were still very lacking as well as infrastructure with the addition of 80 Mbps bandwidth to five faculties, rectorates, libraries and postgraduate programs; 3) dispositions were given to the policy users, namely lecturers who were still less motivated in using e-learning, and 4) bureaucratic structure related to operational standards procedures that was designed in the form of e-learning guidebooks. The result found that e-learning implementation was in the category of important in the aspect of performance, information, economics, control and service, and in the category of very important in the aspect of efficiency. E-learning system at IAIN Kendari is acceptable for all academic civity, however they need personal motivation to implement and utilize it for classroom learning supported by speed internet access.
Stemtech Model in Asean Universities:an Empirical Research at Can Tho University

In order to train human resources to adapt to the digital era, universities in ASEAN region have invested and developed programs related to Science, Technology, Engineering and Mathematics (STEM) for many years. In that trend, under the support of Arizona State University (from the United States), Can Tho University, a member of the ASEAN University Network, has built a model to promote creativity, in which Technology elements are particularly emphasized and it is called STEMTech. An experiment on STEMTech model at Can Tho University was conducted in 2018 with three steps: (1) Training for students about STEM education, STEMTech model and some new technologies; (2) Each group of students make STEM products based on STEMTech model; (3) Student surveys. A statistical analysis was used to evaluate and improve STEMTech model. The results show that learners perceive STEMTech model quite positively, nevertheless, the access to the Technology factor in the model is quite difficult. This shows the necessary to develop STEMTech models to overcome limitations for learners and replicate this model to several other universities in ASEAN region.
PRIMARY SCHOOL TEACHERS PREPAREDNESS TO DEVELOP PUPILS' DIGITAL LITERACY IN TEACHING MATHEMATICS

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 7580-7584ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.1831Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Why organise information about transmedia?

The chapter seeks to demonstrate that requirements posed by transmedia are highly relevant for library metadata practices. It gives an overview of new models of learning and literacy that strongly rely on the ability to aggregate, assess, categorise, use and create meaningful information out of portions coming from various communicative contexts. In this context, transmedia is seen as a paradigm of our future interaction with information. It is suggested that transmedia may encourage the development of learning skills and multiple forms of literacy, as well as the overall ability to construct meaning from diverse outputs. Bibliographic organisation can support these processes and create opportunities for further research into user engagement with transmedia content. The chapter also provides guidance on how to overcome potential threats to implementation of new, more transmedia-friendly metadata practices. It is proposed that libraries should collaborate more extensively with other information organising communities, including users.
ANNETT-O: an ontology for describing artificial neural network evaluation, topology and training

Deep learning models, while effective and versatile, are becoming increasingly complex, often including multiple overlapping networks of arbitrary depths, multiple objectives and non-intuitive training methodologies. This makes it increasingly difficult for researchers and practitioners to design, train and understand them. In this paper, we present ANNETT-O, a much-needed, generic and computer-actionable vocabulary for researchers and practitioners to describe their deep learning configurations, training procedures and experiments. The proposed ontology focuses on topological, training and evaluation aspects of complex deep neural configurations, while keeping peripheral entities more succinct. Knowledge bases implementing ANNETT-O can support a wide variety of queries, providing relevant insights to users. In addition to a detailed description of the ontology, we demonstrate its suitability for the task via a number of hypothetical use-cases of increasing complexity.
Text Filtering through Multi-Pattern Matching: A Case Study of Wu–Manber–Uy on the Language of Uyghur

Given its generality in applications and its high time-efficiency on big data-sets, in recent years, the technique of text filtering through pattern matching has been attracting increasing attention from the field of information retrieval and Natural language Processing (NLP) research communities at large. That being the case, however, it has yet to be seen how this technique and its algorithms, (e.g., Wu–Manber, which is also considered in this paper) can be applied and adopted properly and effectively to Uyghur, a low-resource language that is mostly spoken by the ethnic Uyghur group with a population of more than eleven-million in Xinjiang, China. We observe that technically, the challenge is mainly caused by two factors: (1) Vowel weakening and (2) mismatching in semantics between affixes and stems. Accordingly, in this paper, we propose Wu–Manber–Uy, a variant of an improvement to Wu–Manber, dedicated particularly for working on the Uyghur language. Wu–Manber–Uy implements a stem deformation-based pattern expansion strategy, specifically for reducing the mismatching of patterns caused by vowel weakening and spelling errors. A two-way strategy that applies invigilation and control on the change of lexical meaning of stems during word-building is also used in Wu–Manber–Uy. Extra consideration with respect to Word2vec and the dictionary are incorporated into the system for processing Uyghur. The experimental results we have obtained consistently demonstrate the high performance of Wu–Manber–Uy.
Sparsity of curves and additive and multiplicative expansion of rational maps over finite fields

For a prime $p$ and a polynomial $F(X,Y)$ over the finite field $\mathbb{F}_p$ of $p$ elements, we give upper bounds on the number of solutions of $$ F(x,y)=0, \quad x\in\mathcal{A}, \, y\in \mathcal{B}, $$ where $\mathcal A$ and $\mathcal B$ are very sma
Objective-Oriented System for Modelling Building Energy Supply Based on PV Modules

The created object-oriented system for modelling the building's energy supply based on PV modules. The complex task of modelling the energy supply of a building on the basis of PV modules is considered. The use of specialized programs for modelling roof PV systems and determining full or partial electricity consumption from the network replacement feasibility (efficiency) is proposed. The simulation results enable homeowners to decide on the future establishment of a PV home energy system. Test results were obtained for a residential house in Kyiv region (Ukraine), taking into account the real solar insolation data.
Particle Flow Bayes’ Rule

We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation. We prove that such an ODE operator exists. Its neural parameterization can be trained in a meta-learning framework, allowing this operator to reason about the effect of an individual observation on the posterior, and thus generalize across different priors, observations and to sequential Bayesian inference. We demonstrated the generalization ability of our particle flow Bayes operator in several canonical and high dimensional examples.
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting. Our approach is based on flexible Gaussian process priors for the generative mappings and interchangeable Dirichlet process priors to learn the structure. The introduction of the Dirichlet process as a specific structural prior allows our model to circumvent issues associated with previous Gaussian process latent variable models. Inference is performed by deriving an efficient variational bound on the marginal log-likelihood on the model.
Using a Glicko-Based Algorithm to Measure In-Course Learning.

Techniques are described by which learner skill can be estimated over time, even in the presence of large data sets. A rating system can be used in which a learner's submission of a course assessment can be interpreted as a single match, where the learner and the course assessment are competing against each other. This approach can be applied to online learning platforms that include very many learners who have submitted assignments very many times in many courses. The described techniques provide reliable estimates of assessment difficulty and learner ability. The described scoring framework may be used as a basis for various applications that account for a learner's ability, such as adaptive diagnostic tests and personalized recommendations.
A New Approach to Optimize the Defuzzification Unit of Fuzzy Systems

Fuzzy systems have numerous advantages in mathematical and logical operations, and that's why they are being used in parallel with digital processors to handle some of their mathematical functionalities. The focus of this paper is on the optimization of the output side of the system, which is defuzzifier unit. The defuzzifier transforms the fuzzy data of the system to crisp data and can be implemented using different structures. The structure used in this paper, consists of three types of blocks: adders, multipliers and dividers. Since the system works by the side of a digital processor, an analog to digital converter (ADC) should be added next to the defuzzifier, to convert the crisp output to digital data. Considering the defuzzifier and the ADC as a single unit, it will have four blocks, which occupy a large chip area and consume a huge amount of power. To mitigate these objections, some solutions should be proposed. By designing the circuits in current mode there will be no need for adders and by delegating the role of division to ADC, the divider will be deleted. The proposed circuits are simulated in H-spice and the layouts are drawn in Cadence virtuoso. The simulation results show that power consumption and the chip area of the proposed structure (counting ADC) are 40 mw and 300μm*150μm, the results also prove that the proposed method has better quality factor (QF) compared to former works.
Computer Vision Approaches to Waste Containers Detection

The work presented in this article is the result of a prelaminar investigation that aims at using computer vision techniques to replace the current method of performing detection of waste contains via radio-frequency identification. Comparatively to the current method, this approach is more agile and diminishes the resources needed for an implementation. The approach discussed is focused on the use of convolutional neural networks, specifically the network YOLO. Using this method of identification, it was attained an accuracy of 92% of the waste containers.
The Language of Emoji in Social Media

The language of emoji in social media - The very fast development of information technology which is characterized by an inﬂux
Research on Classification Method of CRH Maintenance Parts Based on Entropy Weight-Clustering Analysis

There are many kinds of CRH maintenance parts, whose inventory management is complex. In order to manage CRH maintenance parts more reasonably, the research on classification method of spare parts is conducted. The classification method of clustering analysis is optimized with the help of the entropy weight method, and the optimized classification method of entropy weight-clustering analysis is used to classify the CRH maintenance parts. The classification results are compared with the traditional clustering analysis results, which verifies the rationality of the method of entropy weight-clustering analysis on the classification of CRH maintenance parts.
Vanadium in the Arctic zone of Russia on the Kola region example: prevalence, sources, industrial potential

Abstract This article gives a brief overview of the prevalence, sources and industrial potential of vanadium in the Arctic zone of Russia on the Kola region example. Within the Kola region many solid minerals is mining: apatite-nepheline ores of the Khibiny, rare earth elements ore of Lovozero, baddeleyite-apatite-magnetite ores of the Kovdor, ferruginous quartzites of Olenegorsk, copper-nickel deposits of Pechenga etc. In the region potential deposits of platinum group elements, chromium, titanium, lithium and beryllium are explored. Recent studies conducted by the workers of the KSC RAS, in combination with geological and exploration data of predecessors, indicate that within the Kola region there are large sources of vanadium. Numerous occurrences of vanadium mineralization, as well as deposits of vanadium, are mainly localized within the entire Paleoproterozoic Pechenga-Imandra-Varzuga (PIV) rift belt, which stretches for more than 500 km across the entire Kola Peninsula - from the throat of the White sea in the North-East to the Norwegian Caledonian in the North-West. The time of the belt development is considered to be at the period 2.5-1.7 Ga. Elevated concentrations of vanadium is installed in metasomatites at the contact of volcanics and dolomite in Prikhibin’e, in sedimentary rocks of the North Pechenga area and throughout the black-shale strata of the entire PIV zone. Mineralization, composed by extremely rare vanadium minerals, established in massive sulfide ores of the PIV belt, however, has only scientific interest. Potential sources of industrial extraction of vanadium are complex Fe-Ti-V ores, which are components of mafic/ultramafic layered intrusions. The study of Fe-Ti-V ores of the region continues.
Target Recognition Based on Kinect Combined RGB Image with Depth Image

Target recognition is the most important premise in detection and tracking of human objects. Using the global or local features to identify the human body is the mainstream way to track human body. This paper proposes a system of target recognition based on Kinect which combined the global and local features. Firstly, the RGB image is obtained by the Kinect. And then the people were separated from the background in the RGB image. After that the RGB image is transformed into a histogram. Then, the depth image was obtained by the Kinect. The spatial coordinates of bone nodes were obtained by the Kinect bone tracking technology such as the head, shoulder center, spine, and hip center of the human body. The Euclidean distance of the bone points in space were calculated. Finally, the results of each step were combined as a basis for recognition. Our method can reduce the influence of external light, light and other conditions on recognition.
Color Image Segmentation Using Generalized Inverted Dirichlet Finite Mixture Models By Integrating Spatial Information

Mixture models are popular statistical approaches for image segmentation. However, mixture models based segmentation faces some difficulties. The first problem is the estimation of the number of clusters (M). Secondly, the spatial information is generally not considered. In this paper, we have used two methods to counter these issues. The first method uses spatial information as the prior knowledge of M. This prior knowledge does not give the direct value of M instead it provides some indirect information which can be used to estimate the optimal value of M. The second one uses Markov Random Field (MRF) to integrate spatial information. MRF based models need high computational power due to their complexity. They cannot be used directly in the Maximization step (M-Step) of Expectation-Maximization (EM) algorithm. The MRF model used in this paper does not require high computational power and can be easily integrated with the M-Step. We have implemented Inverted Dirichlet (ID) and Generalized Inverted Dirichlet (GID) mixture models using these two methods. For experiments, we have used 500 Berkeley dataset (BSD500). In order to compare the image segmentation results, the outputs of ID mixture model (IDMM) and GID mixture model (GIDMM) are compared with the Gaussian mixture model (GMM), using segmentation performance evaluation metrics. The results obtained from GIDMM and IDMM are more promising than those obtained with GMM.
Causal Relationship between N-REIT's Dividend Yield and Money Market Indicators

This paper examined the relationship between the Nigerian Real Estate Investment Trusts (N-REITs) and Money Market Indicators (MMIs) which comprise of: Currency in Circulation (CIC), Broad Money Supply (BMS), Corporate-private Sector (CPS), Prime Lending Rate (PLR) and Treasury Bill Rate (TBR). Data for the N-REITs were sourced from the annual published report of SkyeREIT (an indirect real estate investment vehicle of Skye Shelter Fund Plc), while that of the MMI were sourced from the quarterly published bulletins of the Central Bank of Nigeria (CBN) and the National Bureau of Statistics (NBS) for the period 2008-2017. The study adopted the Co-integration test, Vector Autoregressive (VAR) and Vector Error Correction Model (VECM). The data collected passed the stationary test at p-value of p&lt;0.05 which implies that the data is fit for Granger Causality Model (GSM) in a VAR environment. The co-integration test at 5% confidence level shows the existence of a partial long-run relationship at p-values of 0.0003, 0.0292 and 0.0297 respectively at the first three orders, while the Max-Eigen rank test was significant at the first order (none) with a p-value of 0.005. The results of the VAR and VEC models showed that CPS, PLR and TBR, with chi-square values of 11.748; 16.589; and 34.778 respectively, significantly affected the performance of N-REITs, while the PLR (4.5798) had a long-run significant effect. The findings provide decision caution for investors, analysts and capital market players when considering investment on securitised real estate assets.