Reviewerly/exHarmony huggingface dataset:
ВИКОРИСТАННЯ КОМП’ЮТЕРНО ОРІЄНТОВАНИХ ЗАСОБІВ ГЕОМЕТРІЇ У ПРОЦЕСІ ФОРМУВАННЯ КРИТИЧНОГО МИСЛЕННЯ МАЙБУТНІХ УЧИТЕЛІВ МАТЕМАТИКИ

The article proved that critical thinking is one of the most important and universal competences of a modern teacher of mathematics. It has been established that geometric disciplines, in particular constructive and projective geometries, have the greatest potential for this. The pedagogical technology of critical thinking formation of future teachers of mathematics by means of geometry has been developed. This technology consists of a target, content and legal, and control component. It was found out that the essential element of the technology at all stages (substantive and procedural, control and evaluation etc.) is the purposeful computer support, first of all, the use of computer tools for mathematical activity and communication. The possibilities of information and communication technologies have been disclosed. An expediency of computer support within the process formation of critical thinking of future teachers of mathematics has been proved. There are systems of dynamic geometry and cloud services, which help to make the process of critical thinking formation of future teachers of mathematics more effective by means of geometry. This is the visualization of geometric objects, concepts, connections (in particular, expressed by analytical constructs), statements, proofs; dynamic drawings; computer experiment for research (nomination and hypothesis testing); control of analytical transformations; fast and high-quality execution of necessary images that saves time; interactive tasks generation; providing unlimited communication. The implementation analysis of the developed technology by means of mathematical methods, in particular, on the basis of the theory of fuzzy sets, has been carried out. The effectiveness of computer support of the formation of critical thinking of future mathematics teachers by means of geometry has been revealed.
Linguistic Information in Neural Semantic Parsing with Multiple Encoders

Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks.However, they often do not exploit available linguistic resources, while these, when employed correctly, are likely to increase performance even further.Research in neural machine translation has shown that employing this information has a lot of potential, especially when using a multi-encoder setup.We employ a range of semantic and syntactic resources to improve performance for the task of Discourse Representation Structure Parsing.We show that (i) linguistic features can be beneficial for neural semantic parsing and (ii) the best method of adding these features is by using multiple encoders.
Deep learning–based prediction model of occurrences of major adverse cardiac events during 1-year follow-up after hospital discharge in patients with AMI using knowledge mining

Abstract Traditional regression-based approaches do not provide good results in diagnosis and prediction of occurrences of cardiovascular diseases (CVD). Therefore, the goal of this paper is to propose a deep learning–based prediction model of occurrence of major adverse cardiac events (MACE) during the 1, 6, 12 month follow-up after hospital admission in acute myocardial infarction (AMI) patients using knowledge mining. We used the Korea Acute Myocardial Infarction Registry (KAMIR) dataset, a cardiovascular disease database registered in 52 hospitals in Korea between 1 January, 2005, and 31 December, 2008. Among 14,885 AMI patients, 10,813 subjects in age from 20 to 100 years with the 1-year follow-up traceability without coding errors were finally selected. For our experiment, the training/validation/test dataset split is 60/20/20 by random sampling without replacement. The preliminary deep learning model was first built by applying training and validation datasets and then a new preliminary deep learning model was generated using the best hyperparameters obtained from random hyperparameter grid search. Lastly, the preliminary prediction model of MACE occurrences in AMI patients is evaluated by test dataset. Compared with conventional regression-based models, the performances of machine/deep learning–based prediction models of the MACE occurrence in patients with AMI, including deep neural network (DNN), gradient boosting machine (GBM), and generalized linear model (GLM), are also evaluated through a matrix with sensitivity, specificity, overall accuracy, and the area under the ROC curve (AUC). The prediction results of the MACE occurrence during the 1, 6, and 12-month follow-up in AMI patients were the AUC of DNN (1 M 0.97, 6 M 0.94, 12 M 0.96), GBM (0.96, 0.95, 0.96), and GLM (0.76, 0.67, 0.72) in machine learning–based models as well as GRACE (0.75, 0.72, 0.76) in regression model. Compared with previous models, our deep learning–based prediction models significantly had the accuracy of 95% or higher and outperformed all machine learning and regression-based prediction models. This paper was the first trial of deep learning–based prediction model of the MACE occurrence in AMI clinical data. We found that the proposed prediction model applied different risk factors except the attribute “age” by using knowledge mining and directly used the raw data as input.
A New Perspective on Score Standardization

In test collection based evaluation of IR systems, score standardization has been proposed to compare systems across collections and minimize the effect of outlier runs on specific topics. The underlying idea is to account for the difficulty of topics, so that systems are scored relative to it. Webber et al. first proposed standardization through a non-linear transformation with the standard normal distribution, and recently Sakai proposed a simple linear transformation. In this paper, we show that both approaches are actually special cases of a simple standardization which assumes specific distributions for the per-topic scores. From this viewpoint, we argue that a transformation based on the empirical distribution is the most appropriate choice for this kind of standardization. Through a series of experiments on TREC data, we show the benefits of our proposal in terms of score stability and statistical test behavior.
A Novel Framework to Automatically Generate Executable Web Services From BPMN Models

Enterprise resource planning (ERP) is a business process management system in which integrated applications are used to manage business processes in a shared data environment.ERP systems usually deal with the two types of business processes, i.e., exchange and conversion.In the exchange process, economic resource, such as product, exchanges to another economic resource, such as the sales process.In a conversion process, an enterprise consumes resources in order to produce new resources, such as the distribution process.Generally, the communication between ERP applications, based on the conversion and exchange processes, is accomplished through Web services.In this context, the implementation of Web services in ERP systems is a complex task.To manage this, the business process model and notation (BPMN) are frequently utilized to simplify the development of ERP applications.However, state-of-the-art BPMN approaches usually deal with the modeling of exchange processes without considering the conversion process.Furthermore, the model transformation solution to automatically generate Web services from the BPMN models are hard to find in the literature.Therefore, in this paper, a novel framework is proposed that supports the modeling of both exchanges as well as conversion processes through BPMN.Particularly, a modeling approach is introduced to represent the ERP processes through BPMN concepts.Subsequently, the rules are developed to convert source BPMN models into target Service-oriented architecture Modeling Language (SoaML) models.Finally, transformation rules are developed to generate fully functional executable Java Web services from SoaML models.As a part of the research, a complete open-source BPMN to Web services transformation (B2W) tool is developed to automatically generate the Web services from the high-level BPMN models.The proposed framework is validated through multiple case studies.The experimental results prove that the proposed framework accurately generates Web services from the BPMN models, which eventually helps in developing the ERP systems with simplicity.INDEX TERMS Model-driven engineering (MDE), business process model and notation (BPMN), service oriented architecture (SOA), ERP systems, service generation.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data

In recent years, deep learning models have resulted in a huge amount of progress in various areas, including computer vision. By nature, the supervised training of deep models requires a large amount of data to be available. This ideal case is usually not tractable as the data annotation is a tremendously exhausting and costly task to perform. An alternative is to use synthetic data. In this paper, we take a comprehensive look into the effects of replacing real data with synthetic data. We further analyze the effects of having a limited amount of real data. We use multiple synthetic and real datasets along with a simulation tool to create large amounts of cheaply annotated synthetic data. We analyze the domain similarity of each of these datasets. We provide insights about designing a methodological procedure for training deep networks using these datasets.
Embedded multimodal interfaces in robotics: applications, future trends, and societal implications

research-article Embedded multimodal interfaces in robotics: applications, future trends, and societal implications Share on Authors: Elsa A. Kirchner View Profile , Stephen H. Fairclough View Profile , Frank Kirchner View Profile Authors Info & Claims The Handbook of Multimodal-Multisensor Interfaces: Language Processing, Software, Commercialization, and Emerging DirectionsJuly 2019 Pages 523–576https://doi.org/10.1145/3233795.3233810Published:01 July 2019 3citation134DownloadsMetricsTotal Citations3Total Downloads134Last 12 Months8Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A stretch-sensing soft glove for interactive hand pose estimation

We present a stretch-sensing soft glove to interactively capture full hand poses with high accuracy and without requiring an external optical setup. Our device can be fabricated with simple tools available in most fabrication labs. The pose is reconstructed from a capacitive sensor array embedded in the glove. We propose a data representation that allows deep neural networks to exploit the spatial layout of the sensor itself. The network is trained only once, using an inexpensive off-the-shelf hand pose reconstruction system to gather the training data. The per-user calibration is then performed on-the-fly using only the glove.
Evolutionary Game for Consensus Provision in Permissionless Blockchain Networks with Shards

With the development of decentralized consensus protocols, permissionless blockchains have been envisioned as a promising enabler for the general-purpose transaction-driven, autonomous systems. However, most of the prevalent blockchain networks are built upon the consensus protocols under the crypto-puzzle framework known as proof-of-work. Such protocols face the inherent problem of transaction-processing bottleneck, as the networks achieve the decentralized consensus for transaction confirmation at the cost of very high latency. In this paper, we study the problem of consensus formation in a system of multiple throughput-scalable blockchains with sharded consensus. Specifically, the protocol design of sharded consensus not only enables parallelizing the process of transaction validation with sub-groups of processors, but also introduces the Byzantine consensus protocols for accelerating the consensus processes. By allowing different blockchains to impose different levels of processing fees and to have different transaction-generating rate, we aim to simulate the multi-service provision eco-systems based on blockchains in real world. We focus on the dynamics of blockchain-selection in the condition of a large population of consensus processors. Hence, we model the evolution of blockchain selection by the individual processors as an evolutionary game. Both the theoretical and the numerical analysis are provided regarding the evolutionary equilibria and the stability of the processors' strategies in a general case.
SCA-Resistant GCM Implementation on 8-Bit AVR Microcontrollers

Galois/counter mode (GCM) is one of the most widely used authenticated encryptions.To date, even though some works have investigated the security against side channel analysis (SCA) in the process of GCM computation, especially GHASH function, they failed to present comprehensive SCA security in consideration of both SPA/TA and DPA/CPA aspects simultaneously.In this paper, we present a secure GCM implementation on 8-bit AVR microcontroller environments.The proposed implementation provides comprehensive SCA security in consideration of not only SPA/TA but also DPA/CPA.In order to defeat SPA/TA, we introduce the concepts of dummy XOR with garbage registers and instruction level atomicity (ILA) and also present secure binary field (BF) multiplication method using them, which runs in a constant-time and fixed pattern.We also propose an efficient multiplicative masking method which can prevent DPA/CPA when computing GHASH function in the GCM process.Through actual implementation of the proposed method on an 8-bit AVR ATmega128 microcontroller, we show that the proposed method outperforms existing alternatives while providing comprehensive SCA security.With respect to the performance of secure binary field multiplication, the proposed multiplication method outperforms the related work by around 51.86% when computing a 128-bit binary field multiplication.Regarding the overhead of the multiplicative masking method, the proposed method requires only one additional BF multiplication and negligible amount of field additions regardless of the number of input blocks, while the related work consumes around the {log(m + n + 1) + 2} number of additional BF multiplications when there are (m + n + 1) input blocks.Through SCA-related experiments, we prove the SCA security of the proposed methods.
Linear Predictive Coefficients-Based Feature to Identify Top-Seven Spoken Languages

Speech recognition in multilingual scenario is not trivial in the case when multiple languages are used in one conversation. Language must be identified before we process speech recognition as such tools are language-dependent. We present a language identification system (or AI tool) to distinguish top-seven world languages namely Chinese, Spanish, English, Hindi, Arabic, Bangla and Portuguese [G. F. Simons and C. D. Fennig (eds.), Ethnologue: Laguage of the Americas and the Pacific, Twentieth Edn. (SIL Internatinal, 2017)]. The system uses linear predictive coefficients-based feature, i.e. the line spectral pair–grade ratio (LSP–GR) feature, and ensemble learning for classification. Experiments were performed on more than 200[Formula: see text]h of real-world YouTube data and the highest possible accuracy of 96.95% was received. The results can be compared with other machine learning classifiers.
Who Killed Albert Einstein? From Open Data to Murder Mystery Games

This paper presents a framework for generating adventure games from open data. Focusing on the murder mystery type of adventure games, the generator is able to transform open data from Wikipedia articles, OpenStreetMap, and images from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves around the murder of a person with a Wikipedia article, and populates the game with suspects who must be arrested by the player if guilty of the murder or absolved if innocent. Starting from only one person as the victim, an extensive generative pipeline finds suspects, their alibis, and paths connecting them from open data, transforms open data into cities, buildings, nonplayer characters, locks and keys, and dialog options. This paper describes in detail each generative step, provides a specific playthrough of one WikiMystery where Albert Einstein is murdered, and evaluates the outcomes of games generated for the 100 most influential people of the 20th century.
Mixture Modeling on Related Samples by ψ-Stick Breaking and Kernel Perturbation

There has been great interest recently in applying nonparametric kernel mixtures in a hierarchical manner to model multiple related data samples jointly.In such settings several data features are commonly present: (i) the related samples often share some, if not all, of the mixture components but with differing weights, (ii) only some, not all, of the mixture components vary across the samples, and (iii) often the shared mixture components across samples are not aligned perfectly in terms of their kernel parameters such as the location and spread in Gaussian kernels, but rather display small misalignments either due to systematic cross-sample difference or more often due to uncontrolled, extraneous causes.Properly incorporating these features in mixture modeling will enhance the efficiency of inference, whereas ignoring them not only reduces efficiency but can jeopardize the validity of the inference due to issues such as confounding.We propose to use two techniques for incorporating these features in modeling related data samples using kernel mixtures.The first technique, called ψ-stick breaking, is a joint generative process for the mixing weights through the breaking of both a stick shared by all the samples for the components that do not vary in size across samples and an idiosyncratic stick for each sample for those components that do vary in size.The second technique is to imbue random perturbation into the kernels, thereby accounting for cross-sample misalignment.These techniques can be used either separately or together in both parametric and nonparametric kernel mixtures.We derive efficient Bayesian inference recipes based on Markov Chain Monte Carlo (MCMC) sampling for models featuring these techniques, and illustrate their work through both simulated data and a real flow cytometry data set in prediction/estimation and testing multi-sample differences.
A novel clustering algorithm for time-series data based on precise correlation coefficient matching in the IoT

In smart environments based on the Internet of Things (IoT), almost all of the object information that is collected by various sensors is time series data, which records the behavior of the objects. Analyzing the correlation between different time series data, other than those in the same time series, is more helpful to discovering their behavioral relations. This has become one of the important current issues in the IoT. To analyze the correlation, a clustering algorithm named the CPCCM (clustering algorithm based on precise correlation coefficient matching) is presented. First, each initial sequence is split into a set of subsequences by adopting a preset sliding window. Then, the correlation coefficients between any pair of subsequence sets from two sequences are resolved. Those pairs that pass some preset Pearson correlation coefficient threshold are clustered. In the CPCCM, a cross-traversal strategy is introduced to improve the search efficiency. The cross-traversal strategy alternatively searches the subsequences in two subsequence sets. To improve the clustering efficiency, in each initial sequence, adjacent subsequences are merged into longer subsequences and replaced by it if they appear in the same subsequence set. Finally, by analyzing practical electric power consumption data, the CPCCM is shown to be promising and able to be applied in similar scenarios. By comparison with the agglomerative hierarchical clustering algorithm, the major contributions of this work is that the clustering quality is improved by using the strategy of precise matching and cross-traversal, and complexity of the algorithm is reduced by merging adjacent subsequences. Therefore, CPCCM can be applied to analyze behavior between different objects in smart environments.
Do Linguistic Style and Readability of Scientific Abstracts Affect their Virality?

Reactions to textual content posted in an online social net- work show different dynamics depending on the linguistic style and readability of the submitted content. Do similar dy- namics exist for responses to scientific articles? Our intuition, supported by previous research, suggests that the success of a scientific article depends on its content, rather than on its linguistic style. In this article, we examine a corpus of sci- entific abstracts and three forms of associated reactions: ar- ticle downloads, citations, and bookmarks. Through a class- based psycholinguistic analysis and readability indices tests, we show that certain stylistic and readability features of ab- stracts clearly concur in determining the success and viral ca- pability of a scientific article.
Normalized time-domain parameters for electroglottographic waveforms

The electroglottographic waveform is of interest for characterizing phonation non-invasively. Existing parameterizations tend to give disparate results because they rely on somewhat arbitrary thresholds and/or contacting events. It is shown that neither are needed for formulating a normalized contact quotient and a normalized peak derivative. A heuristic combination of the two resolves also the ambiguity of a moderate contact quotient, with regard to vocal fold contacting being firm versus weak or absent. As preliminaries, schemes for electroglottography signal preconditioning and time-domain period detection are described that improve somewhat on similar methods. The algorithms are simple and compute quickly.
A QUIC Look at Web Tracking

Abstract QUIC has been developed by Google to improve the transport performance of HTTPS traffic. It currently accounts for approx. 7% of the global Internet traffic. In this work, we investigate the feasibility of user tracking via QUIC from the perspective of an online service. Our analysis reveals that the protocol design contains violations of privacy best practices through which a tracker can passively and uniquely identify clients across several connections. This tracking mechanisms can achieve reduced delays and bandwidth requirements compared to conventional browser fingerprinting or HTTP cookies. This allows them to be applied in resource- or time-constrained scenarios such as real-time biddings in online advertising. To validate this finding, we investigated browsers which enable QUIC by default, e.g., Google Chrome. Our results suggest that the analyzed browsers do not provide protective measures against tracking via QUIC. However, the introduced mechanisms reset during a browser restart, which clears the cached connection data and thus limits achievable tracking periods. To mitigate the identified privacy issues, we propose changes to QUIC’s protocol design, the operation of QUIC-enabled web servers, and browser implementations.