Reviewerly/exHarmony huggingface dataset:
Simulating Markov Random Fields With a Conclique-Based Gibbs Sampler

For spatial and network data, we consider models formed from a Markov random field (MRF) structure and the specification of a conditional distribution for each observation. Fast simulation from such MRF models is often an important consideration, particularly when repeated generation of large numbers of data sets is required. However, a standard Gibbs strategy for simulating from MRF models involves single-site updates, performed with the conditional univariate distribution of each observation in a sequential manner, whereby a complete Gibbs iteration may become computationally involved even for moderate samples. As an alternative, we describe a general way to simulate from MRF models using Gibbs sampling with "concliques" (i.e., groups of non-neighboring observations). Compared to standard Gibbs sampling, this simulation scheme can be much faster by reducing Gibbs steps and independently updating all observations per conclique at once. The speed improvement depends on the number of concliques relative to the sample size for simulation, and order-of-magnitude speed increases are possible with many MRF models (e.g., having appropriately bounded neighborhoods). We detail the simulation method, establish its validity, and assess its computational performance through numerical studies, where speed advantages are shown for several spatial and network examples.
Assessing the quantumness of the annealing dynamics via Leggett Garg’s inequalities: a weak measurement approach

Abstract Adiabatic quantum computation (AQC) is a promising counterpart of universal quantum computation, based on the key concept of quantum annealing (QA). QA is claimed to be at the basis of commercial quantum computers and benefits from the fact that the detrimental role of decoherence and dephasing seems to have poor impact on the annealing towards the ground state. While many papers show interesting optimization results with a sizable number of qubits, a clear evidence of a full quantum coherent behavior during the whole annealing procedure is still lacking. In this paper we show that quantum non-demolition (weak) measurements of Leggett Garg inequalities can be used to efficiently assess the quantumness of the QA procedure. Numerical simulations based on a weak coupling Lindblad approach are compared with classical Langevin simulations to support our statements.
Machine Intelligence for Management of Acute Coronary Syndromes: Neural or Nervous Times?

On May 24, 1844, Samuel F. B. Morse sent the first telegram 38 miles from Washington to Baltimore. Varying pulses and patterns of electricity travelled overhead through a single copper wire from here to there … a nearly instantaneous signal … a single message, “What Hath God Wrought?” (see Numbers 23:23 King James Bible). This scientific event, in apparent service to a Higher Being, ushered in the telecommunications age. By adapting Edinburgh mathematician James Clark Maxwell’s brilliant equations, the 19th century’s greatest power grew a global network of undersea telegraphy cables into the “nervous system” of the British Empire. 1 Hunt B.J. Imperial science: Victorian cable telegraphy and the making of “Maxwell’s equations.”. Proc IEEE. 2018; 106: 1458-1465 Crossref Scopus (1) Google Scholar Eminent British scientists—Charles Wheatstone, Michael Faraday, William Thomson Kelvin, Oliver Lodge, and Oliver Heaviside—contributed the electromagnetic theories and physical applications necessary to move undistorted information from “point A to point B.” By the early 20th century, diplomatic missives, military commands, and commercial orders flowed from and to London. Artificial Intelligence for Diagnosis of Acute Coronary Syndromes: A Meta-analysis of Machine Learning ApproachesCanadian Journal of CardiologyVol. 36Issue 4PreviewMachine learning (ML) encompasses a wide variety of methods by which artificial intelligence learns to perform tasks when exposed to data. Although detection of myocardial infarction has been facilitated with introduction of troponins, the diagnosis of acute coronary syndromes (ACS) without myocardial damage (without elevation of serum troponin) remains subjective, and its accuracy remains highly dependent on clinical skills of the health care professionals. Application of a ML algorithm may expedite management of ACS for either early discharge or early initiation of ACS management. Full-Text PDF
Polygamy of multiparty <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>q</mml:mi></mml:math> -expected quantum entanglement

We characterize the polygamy nature of quantum entanglement in multi-party systems in terms of $q$-expectation value for the full range of $q\geq 1$. By investigating some properties of generalized quantum correlations in terms of $q$-expectation value and Tsallis $q$-entropy, we establish a class of polygamy inequalities of multi-party quantum entanglement in arbitrary dimensions based on $q$-expected entanglement measure. As Tsallis $q$-entropy is reduced to von Neumann entropy, and $q$-expectation value becomes the ordinary expectation value when $q$ tends to $1$, our results encapsulate previous results of polygamy inequalities based on von Neumann entropy as special cases.
Lessons Learned from Journey Mapping in Health Care

The Department of Veterans Affairs’ Human Factors Engineering team recognizes the value of journey maps as a means for communication among stakeholder groups and develops maps to showcase the experience of users with health services and technology systems. The uniqueness of health care environments caused difficulties in following available trade guidance for creating journey maps. Anticipating that other Human Factors Engineers working in health care settings will encounter similar challenges, this paper showcases our lessons learned while creating two distinct journey maps and offers a process for constructing journey maps in health care environments. We learned to selectively limit the content of journey maps, ensure design quality by utilizing a template and rubric, and apply alternate approaches for data gathering. Our improved process includes steps to partner with stakeholders, produce a journey map framework and confirm it with user research, and visualize findings in the completed journey map.
International Ratings of Higher Education Institutions: Analysis of Results Taking Into Account the Profile of Their Educational and Scientific Activities

International university rankings outcomes published in 2019 were analyzed by groups in accordance with university’s education and scientific activities. The authors used the methodology applied in the previous work, where they identified seven core activities: classical universities, technical universities, universities of economics, pedagogical universities, medical universities, agricultural universities, universities of culture and arts. The current analysis gained from the data insight and data comparison for 2013, 2014 and 2019 illustrates systematic dependence of the average grades on the university profile. Leading positions in the ranking are occupied by classical and technical universities. Next in the list are groups of pedagogical, economic and medical universities followed by groups of agrarian universities and universities of culture and arts that remain at similar level. According to the Webometrics Rankings, the universities of the group “Culture and Arts” have dramatically dropped since 2013 and 2014, owing to the increasing weight of science indicators. There was carried out the analysis of the positions taken by the top universities of related groups from all over the world including the European Union, the Eurasian Economic Union and the Republic of Belarus. It is illustrated that Russian universities are in the top of their profile groups within the EAEU. National Research University “Higher School of Economics” which is significantly ahead of other economic universities. Rating results in accordance with the profiles of educational and scientific activities of universities were proposed to submit on an equal basis with the classic world and subject ranking of universities.
An estimation of causal structure based on Latent LiNGAM for mixed data

Abstract The linear non-gaussian acyclic model (LiNGAM) has been proposed as a method for estimating causal structures using structural equation modeling (SEM). LiNGAM is useful as an exploratory estimation method for a causal structure. However, the assumptions that all observed variables in LiNGAM are continuous is not applicable in case of mixed data (i.e., when discrete variables are also included in the dataset). Therefore, we propose the Latent LiNGAM (L-LiNGAM), where each variable corresponds to a continuous latent variable and is observed as data through transformation via a link function. In the numerical study, when mixing discrete variables, the estimation of causal structure using L-LiNGAM is proven useful in terms of sum of squared error and path recovery. Moreover, from real-world data applications, the causal structure estimated by L-LiNGAM is shown to be the best for evaluation under SEM. The model fit is also superior to that of existing methods.
THE EFFECTS OF WHATSAPP IN TEACHING NARRATIVE WRITING: A CASE STUDY

Purpose of the study: This study aims to identify the effects of WhatsApp in teaching narrative writing to Grade 6of primary school pupils in the aspect of vocabulary and grammar. Methodology: Eight pupils of Grade 6 from one primary school in Negeri Sembilan were involved and data were collected through document analysis, focus group interviews and field notes. Main Findings: The findings of the post-test revealed that the vocabulary aspect has significantly improved as 75% of the participants showed an increase of one and two marks while the aspect of grammar was not found to have any effect as only one participant who represented 12% of the participants showed slight improvement from 12 to 13 marks. Applications of this study: The findings of this study are hoped to shed some light on how the educators could manipulate the functions of mobile messaging apps in teaching English particularly the skill of writing. Novelty/Originality of study: The pupils’ motivation to learn narrative writing could be enhanced through the use of WhatsApp rather than writing conventionally using pencil and paper.
Intelligent Human-Computer Interaction: A Perspective on Software Engineering

The design of traditional human-computer interaction courses is facing new challenges due to the breakthrough of the third generation of AI technology. New human-computer interaction scenarios, such as smart home and driverless cars, keep on emerging. More natural and efficient intelligent interaction methods are widely used in these scenarios, generating brand-new user experience. Combined with an example on the interactive design of intelligent products and the previous experience on teaching. In this article, an innovative design of human-computer interaction courses is introduced from the perspective on innovative content, cultivation of talents, and practice of software engineering.
Overcoming Limitations of the HoloLens for Use in Product Assembly

Augmented Reality (AR) is an emerging technology that could greatly increase training efficiency and effectiveness for assembly processes. In fact, studies show that AR may reduce time and errors by as much as 50%. While many devices are available to display AR content, the use of Head Mounted Displays (HMDs) is the most interesting for manufacturing and assembly as they free up the user&#x2019;s hands. Due to the emerging nature of this technology, there are many limitations including input, field of view, tracking and occlusion. The work presented in this paper explores the use of the Microsoft HoloLens to deliver AR work instructions for product assembly. An AR assembly application was developed to guide a trainee through the assembly of a mock aircraft wing. To ensure accurate instructions were displayed, novel techniques were developed to mitigate the HoloLens&#x2019; tracking and display limitations. Additionally, a data visualization application was developed to validate the training session and explore trends through data collected from the HoloLens along with wearable physiological sensor data.
Calculating Texture Features from Mammograms and Evaluating Their Performance in Classifying Clusters of Microcalcifications

In this work, 2432 texture features were calculated from microcalcification clusters presented on 190 images from the Digital Database for Screening Mammography. Mutual information technique was used to rank texture features. Then, an incremental procedure adds top ranked features to the Fisher discriminant analysis to determine the best set of texture features in classifying benign or malignant microcalcification clusters. The result was achieved using 13 texture features (AUC.632+ = 0.945 ± 0.019). However, to assure a consistent statistical analysis, at least 30 sample images for each feature added was assumed. The best performance was achieved by a set with 5 texture features (AUC.632+ = 0.884 ± 0.025), which is comparable to the ones presented in literature.
Changes in Scholarly Reading in Finland Over a Decade: Influences of E-Journals and Social Media

Abstract Nationwide surveys of researchers in Finland in 2007 and 2016 distributed with the assistance of FinELib, the Finnish national consortium, show that researchers use a growing range of sources to find and access scholarly articles and that some reading patterns are changing. The percentage of articles found by searching and browsing are decreasing, while researchers are using more social ways to locate articles. Research social networking sites are rated as important to their work. They read more on-screen, although still print some material out for final reading. Reading patterns for books are different, as researchers still rely more on printed books than e-books, in spite of an increase in e-book collections through their libraries. This study shows a continuous process of incremental change, enabled by changes in scholarly publishing, social networking, and library collection decisions. More changes can be expected, as researchers adopt systems and patterns that fit with their work patterns and make the finding, locating, and reading of scholarly materials easier. Libraries must both lead and adapt to these new reading patterns by providing links and access to a variety of journal services and by maintaining a balance of print and e-book collections.
Faster saddle-point optimization for solving large-scale Markov decision processes

We consider the problem of computing optimal policies in average-reward Markov decision processes. This classical problem can be formulated as a linear program directly amenable to saddle-point optimization methods, albeit with a number of variables that is linear in the number of states. To address this issue, recent work has considered a linearly relaxed version of the resulting saddle-point problem. Our work aims at achieving a better understanding of this relaxed optimization problem by characterizing the conditions necessary for convergence to the optimal policy, and designing an optimization algorithm enjoying fast convergence rates that are independent of the size of the state space. Notably, our characterization points out some potential issues with previous work.
Pattern Recognition Through Digital Image Processing for Unmanned Aerial Vehicles

This paper describes the implementation of two digital image processing methods for pattern recognition, by color boundary method and the Haar Cascade Classifier to detect objects in a video stream, both methods implemented on Python 3 and OpenCV. Patterns detection of images obtained from drones has advantages over traditional video recording drones. The drone has a streaming video system, based on the Raspberry Pi 3 minicomputer, which is sent by wireless communication to the base station where a pattern recognition algorithm performs operations on the video source coming from the drone. This proposed system has excellent performance based on an integrated streaming video system with 5.8GHz Wi-Fi connection acceptable to the base station. Both methods proved to be valid for certain types of patterns and objects under different light conditions.
A semantic textual similarity measurement model based on the syntactic-semantic representation

Measuring semantic textual similarity (STS) lies at the core of many applications in natural language processing (NLP). Recently, most models have considered semantic information or syntactic information, but seldom an unified model to make full use of these two kinds of information. Based on the k nowledge from the trained word vectors, this paper proposes a semantic-embedded dependency tree (SEDT) model based on word2vec and glove, which can be treated as a syntactic-semantic representation. In consideration of the words in a sentence for the contribution of the semantic are different, this model extends the semantic-embedded dependency tree model to an enhanced semantic-embedded dependency tree (ESEDT). And a modified partial tree kernel (MPTK) is proposed to automatically extract the syntactic-semantic patterns in this tree. Because the syntactic information, semantic knowledge, and the contribution distribution of the word attention model are all considered in this model, it can measure more comprehensive sentence semantics to improve the accuracy of STS results. Finally, SEDT/E-SEDT is applied to SemEval semantic textual similarity tasks and evaluate its performance through two widely used benchmarks: the Pearson correlation coefficient and the Spearman correlation coefficient. The experimental results show that SEDT/E-SEDT can effectively improve the accuracies of sentence similarity judgments. Compared with the other similar methods to calculate the semantic similarity, such as some neural network models, SEDT/E-SEDT can obtain better performance on most dataset.
Large Vocabulary Arabic Continuous Speech Recognition using Tied States Acoustic Models

The Hidden Markov Model (HMM) lies at the heart of the modern speech recognition systems as it provides a simple, effective and straight forward frame work to model the time varying acoustic features of the speech signals.The basic process of building HMM based speech recognition systems is a straight forward process.Nevertheless, the proper parameter estimation of such models requires large training data.Therefore, parameter tying techniques were developed to reduce the parameters of HMMs without affecting the overall system performance.This study proposes an Arabic phonetic decision tree necessary to build Tied State tri-phone HMMs.Experimental results show promising word correctness when compared with both data driven tri-phone models and phoneme based models.The maximum word correctness achieved by the proposed approach was 95.13%.Whereas it reached 78.03 and 58.45% using data driven tri-phones and phoneme based HMMs, respectively, when tested on the same benchmark database.
Performance Analysis of Supervised Learning Models for Product Title Classification

&lt;p&gt;Online business development through e-commerce platforms is a phenomenon which change the world of promoting and selling products in this 21&lt;sup&gt;st&lt;/sup&gt; century. Product title classification is an important task in assisting retailers and sellers to list a product in a suitable category. Product title classification is apart of text classification problem but the properties of product title are different from general document. This study aims to evaluate the performance of five different supervised learning models on data sets consist of e-commerce product titles with a very short description and they are incomplete sentences. The supervised learning models involve in the study are Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM) and Random Forest. The results show KNN model is the best model with the highest accuracy and fastest computation time to classify the data used in the study. Hence, KNN model is a good approach in classifying e-commerce products.&lt;/p&gt;
Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems

As a result of the analysis of dispatcher intelligence centers and aerial, land, underground, underwater, universal, and functionally focused artificial intelligence robotics systems, the problems of rational control, due to be performed under specific conditions of uncertainties, are chosen for probabilistic study.The choice covers the problems of planning the possibilities of functions performance on the base of monitored information about events and conditions and the problem of robot route optimization under limitations on risk of "failure" in conditions of uncertainties.These problems are resolved with a use of the proposed probabilistic approach.The proposed methods are based on selected probabilistic models (for "black box" and complex systems), which are implemented effectively in wide application areas.The cognitive solving of problems consists in improvements, accumulation, analysis, and use of appearing knowledge.The described analytical solutions are demonstrated by practical examples.
Connecting the museum to the city environment from the visitor’s perspective

Recommendation systems are widely used in tourism in order to provide people personalized suggestions that would make their trip memorable. Nowadays, mobile assisted guided tours based on recommendation services are used in museums to enhance visitors ’ experience. However, all those systems have been designed to target indoor or outdoor museum visits. Is it feasible to design a system that supports mobile services that connect a museum visit to artworks situated outdoor in the city environment? Is it possible to connect the artworks of a city center to the exhibits of a museum? In this work, we attempt to give a first answer to such questions proposing and implementing a set of services that connects the museum to the city public space. In order to show the strength of the implemented services, we present a basic usage scenario along with a first system evaluation showing positive results.