Reviewerly/exHarmony huggingface dataset:
The productivity of Dutch diminutives

Abstract This study reports on an investigation of the productivity of the Dutch diminutive paradigm, which gives rise to five suffixal allomorphs, against the background of Yang’s (2016) Tolerance Principle. It shows how, by studying the frequency of the allomorphs and the environments in which they occur, we can use the Tolerance Principle to determine if a productive rule system can be found for the Dutch diminutives. In doing so, we also describe how we collected the necessary data by setting up a corpus study on Dutch diminutives, including a specific one with data from child directed speech.
A machine learning approach for optimizing heuristic decision‐making in Web Ontology Language reasoners

Abstract Description logics (DLs) are formalisms for representing knowledge bases of application domains. The Web Ontology Language (OWL) is a syntactic variant of a very expressive DL. OWL reasoners can infer implied information from OWL ontologies. The performance of OWL reasoners can be severely affected by situations that require decision‐making over many alternatives. Such a nondeterministic behavior is often controlled by heuristics that are based on insufficient information. This article proposes a novel OWL reasoning approach that applies machine learning (ML) to implement pragmatic and optimal decision‐making strategies in such situations. Disjunctions occurring in ontologies are one source of nondeterministic actions in reasoners. We propose two ML‐based approaches to reduce the nondeterminism caused by dealing with disjunctions. The first approach is restricted to propositional DL while the second one can deal with standard DL. Both approaches speed up our ML‐based reasoner by up to two orders of magnitude in comparison to the non‐ML reasoner. Another source of nondeterministic actions is the order in which tableau rules should be applied. On average, our ML‐based approach achieves a speedup of two orders of magnitude when compared to the most expensive rule ordering of the non‐ML reasoner.
Distributed Multi-Objective Cooperative Coevolution Algorithm for Big-Data-Enabled Vessel Schedule Recovery Problem

During a maritime voyage, delays due to disruptive events could result in financial and reputation losses. The vessel schedule recovery problem (VSRP) aims at adjusting vessel speeds to mitigate the negative impact of such delays. The granulated speed-based vessel schedule recovery problem (G-S-VSRP) is a big-data-enabled VSRP. It is a multiobjective optimization problem defined by dividing the trajectory between ports into regions (encoded by geohashed system) and mining the speed profiles in these regions from Automatic Identification System (AIS) data. The G-S-VSRP minimizes delay and financial loss of a vessel voyage; it also maximizes the speed compliance with the historical navigational patterns. Using geohash-based speed mining on AIS data in the G-S-VSRP gives rise to a large-scale optimization problem, where the number of speed variables in geohashed regions grows to the order of thousands. Due to the complexity of such a problem, traditional multiobjective evolutionary algorithms (MOEAs) would stop improving or showing steady behavior. We improve the MOEA's performance using a cooperative coevolution algorithm based on a divide-and-conquer approach to deal with large-scale optimization problems. We introduce a novel Distributed Multiobjective Cooperative Coevolutionary Algorithm (DMOCCA) to improve the performance of MOEAs.
Solar Tracker with Self-Deploying System

The disadvantages of fossil fuels are presented. The way to improve the efficiency of solar panels through the use of solar tracking systems (STS) was analyzed. Wind loads have been calculated, examples of destruction of solar power plants by natural disasters are presented. A concept for a new type of solar tracker was described. In developing the algorithm for operation of STS, astronomical formulas for motion of the Sun were used. To create a control system, MatLab Simulink software was used. As a result of the work, was created an imitation model that provides high precision tracking of the Sun. A comparison with the high-precision NREL algorithm was also made. Conclusions are drawn on the ways of new type of tracker use.
A Comparative Study of Filter and Wrapper Methods on EDHS – HIV/AIDS Dataset

Machine learning is currently recognized as an extremely valuable aspect for feature selection. It is a subject often addressed in machine learning tasks particularly when there are many features. It can be a key step towards the successful discovery of knowledge in those issues, where the set of features is massive. Several features selection techniques have been suggested over the last few decades with their unique advantages and drawbacks. Despite the intensive study and the huge amount of effort, the different forms of feature selection methods have not yet been evaluated in the HIV/AIDS situation. The proposed article tries to address the void. Hence, this article presents the empirical comparison of the feature selection method of filter and wrapper in EDHS dataset. This paper examines the adaptation of an existing wrapper and filter methods to perform experiments with six machine learning algorithms and test their performance using accuracy and ROC metrics. The result of this work confirms the effectiveness of feature selection for this type of problem and wrapper method reveals superiority, when compared to filters. In wrapper, the classifiers such as LR, SVM, DT and RF scored a better accuracy than filter, whereas KNN and GB achieved same results in both the methods. In ROC metrics, the wrapper method also scores better result than filter. The final result confirms that, wrapper method has scored better results compared to filter method and the original feature set.
Training Artificial Neural Networks by Generalized Likelihood Ratio Method: An Effective Way to Improve Robustness

In this work, we proposed a generalized likelihood ratio method capable of training the artificial neural networks with more flexibility: (a)training with discrete activation and loss functions, while the traditional back propagation method cannot train the artificial neural networks with such activations and loss; (b)involving neuronal noises during training and prediction, which will improve the freedom of the model and make it more adaptable to the real environment, especially when environmental noises exist. Numerical results show that the robustness of various artificial neural networks trained by the new method is significantly improved when the input data is affected by both the natural noises and adversarial attacks.
Learning to Consider Relevance and Redundancy Dynamically for Abstractive Multi-document Summarization

As one of the most essential tasks for information aggregation, multi-document summarization is faced with information redundancy of source document clusters. Recent works have attempted to avoid redundancy while generating summaries. Most state-of-the-art multi-document summarization systems are either extractive or abstractive with an external extractive model. In this paper, we propose an end-to-end abstractive model based on Transformer to generate summaries, considering relevance and redundancy dynamically and jointly. Specifically, we employ sentence masks and design a sentence-level transformer layer for learning sentence representations in a hierarchical manner. Then we use a dynamic Max Marginal Relevance (MMR) model to discern summary-worthy sentences and modify the encoder-decoder attention. We also utilize the pointer mechanism, taking the mean attention of all transformer heads as the probability to copy words from the source text. Experimental results demonstrate that our proposed model outperforms several strong baselines. We also conduct ablation studies to verify the effectiveness of our key mechanisms.
Dynamic encoding, a lightweight combined countermeasure against hardware attacks

With the Internet of Things (IoT) an increasing amount of sensitive data have to be communicated and hence encrypted. Low-cost hardware attacks such as fault analysis (FA) or side-channel analysis (SCA) threaten the implementation of cryptographic algorithms. Many countermeasures have been proposed against either of these attacks, however, only a few countermeasures protect efficiently an implementation against both attacks. These combined countermeasures usually have a prohibitive area and power overhead, and require up to thousands of bits of fresh randomness at each encryption. Therefore, they may not be suited to protect lightweight algorithms in resource-constrained devices. In this paper, we propose a new combined countermeasure, which is particularly adapted to protect lightweight algorithms based on shift registers. It achieves an efficient power balancing at algorithmic level, and provides an inherent fault detection with a better coverage than most existing combined countermeasures. Furthermore, it has a smaller power and area overhead than existing combined countermeasures, and requires at most 8 random bits at each encryption.
Assessment of the Intercompany Relationship Between Business Interests and Human Capital

The profitability of human capital at the microeconomic level depends on a harmonious combination of meeting the interests of wage labor and business. In other words, the level of wages must be equitably combined with the level of profit. In a formalized form, the profitability of a business appears to be a point on the vector of addition of many forces, including the potential and real effectiveness of human capital. Effective motivation of wage labor requires a comprehensive consideration of all its significant results, which include not only the volume of manufactured products, work performed and services. It is also important to consider the indirect results of labor affecting profits in the form of initial material resources saved.
Deep Learning-Based Part-of-Speech Tagging of the Tigrinya Language

Deep Neural Networks have demonstrated the great efficiency in many NLP task for various languages. Unfortunately, some resource-scarce languages as, e.g., Tigrinya still receive too little attention, therefore many NLP applications as part-of-speech tagging are in their early stages. Consequently, the main objective of this research is to offer the effective part-of-speech tagging solutions for the Tigrinya language having rather small training corpus.In this paper the Deep Neural Network classifiers (i.e., Feed Forward Neural Network, Long Short-Term Memory, Bidirectional LSTM and Convolutional Neural Network) are investigated by applying them on a top of trained distributional neural word2vec embeddings. Seeking for the most accurate solutions, DNN models are optimized manually and automatically. Despite automatic hyper-parameter optimization demonstrates a good performance with the Convolutional Neural Network, the manually tested Bidirectional Long Short – Term Memory method achieves the highest overall accuracy equal to 0.91%.
Robust transfer joint matching distributions in semi-supervised domain adaptation for hyperspectral images classification

This paper presents a novel Semi-Supervised Domain Adaptation (SSDA) method for hyperspectral image classification. Although, SSDA methods are useful when the number of the training samples are limited, but still encounter some problems. First, the traditional SSDA methods based on kernel prediction model consider a predefined kernel for both domains without using the target samples into classifier structure, which makes the challenges for classification of the noisy and complex dataset in the target domain. Second, the previous SSDA methods only measured and decreased the Maximum Mean Discrepancy (MMD) between source and target domains in order to decrease the distribution discrepancy, which ignores the discriminative information in both domains. To solve these issues, we propose a Robust Transfer Joint Matching Distribution (RTJMD) based on both the classification error and the distribution discrepancy minimization principle. We present a generalized multi-kernel model by incorporating two Fredholm integral to find an optimal kernel. Then, we propose a Regularized Extended Maximum Distribution Discrepancy (REMDD) metric in Reproduced Kernel Hilbert Space (RKHS), which considers both the extended maximum mean discrepancy and the extended maximum variance discrepancy with Multiple Kernel Learning (MKL) between two domains. The experimental results with two benchmark datasets show that the proposed RTJMD improves the classification accuracy and generalization capabilities compared to conventional SSDA approaches even in noisy and complex cases.
Unsupervised Segmentation of Image Using Novel Curve Evolution Method

Here, a novel algorithm for unsupervised field-depth (DOF) image segmentation is defined. To detect the object of interest (OOI) in the saliency space, a multi-scale re-blurring technique is used first. Firstly blurring is carried out to remove artifacts, later reblurring procedure. Thereafter, an active contour model based upon hybrid energy system is suggested to evaluate the OOI boundary. A global energy element relevant to the saliency map is implemented in this model to find the globally minimum, and a local energy term about the low DOF picture is used to increase the precision of segmentation. Additionally, this model is equipped with an elastic parameter to offset the weight of global and local resources. In addition, an unsupervised approach for initializing curves is intended to reduce the amount of iterations for evolution. More the iterations, the complexity and computation time to obtain the results may hike up leading to slow up the process of acquiring precise contours. Lastly, we perform experiments on different low DOF pictures, and the resultant demonstrates the high precision and robustness of the proposed method.
Pairwise Causality Structure: Towards Nested Causality Mining on Financial Statements

Causality mining, which aims to find cause-effect relations in text, is an important yet challenging problem in natural language understanding. The extraction of causal relations is beneficial to practitioners in document-intensive industries. For instance, it enables investors and regulators in financial industries to quickly understand the correlation between events in financial statements. However, this problem is difficult since the expression of causality is diverse, and more importantly, nested. Specifically, causality often has a nested structure, where a pair of cause-effect can be the cause of another higher-level causality. Recent works deal with this problem by a bottom-up relation extraction solution, but it performs worse for relations on higher levels. In this study, we find that the nested causality structure can be transformed into a graph of pairwise causality between sentence segments. Then we propose a two-step solution: first, a segmenter disassembles a sentence into segments by detecting causality connectives; second, a relation classifier predicts whether a pair of segments has cause-effect relation or not. Two modules above are trained jointly in our proposed Causality Detection Network (CDNet). On a large dataset we collect, the precision of our model reaches 92.11% and the recall reaches 93.07% for this task. Compared with the existing state-of-the-art solution, the precision of our model is improved by 3.28% and 3.03% for recall. We also observe that the percentage of exactly correct sentences from prediction is 74.26% without post-processing, indicating the hardness of our problem and space for improvement.
Knowledge-Based Generation of the UML Dynamic Models from the Enterprise Model Illustrated by the Ticket Buying Process Example

The main scope of this paper is to introduce knowledge-based Enterprise model as sufficient data storage for different Unified Modelling Language (UML) models generation, by using all collected data. UML models can be generated from the Enterprise Model by using certain transformation algorithms presented in previous researches. Generation process from the Enterprise model is illustrated by a particular Ticket Buying example. Generated UML dynamic Use Case, Sequence, State and Activity models of the Ticket buying process demonstrate fullness of stored information in the Enterprise model.
Exploiting knowledge graph and text for ranking entity types

Most of the online queries target entities and the type of an entity is a key piece of information. Entity type helps us to understand what an entity is and how it relates to other entities mentioned in a document. Search engine result pages (SERPs) often surface facts and entity type information from a background Knowledge Graph (KG) in response to queries that carry a semantic information need. In a KG, an entity usually holds multiple type properties. For example, popular types attached to the entity `Donald Trump' via rdfs:type statements might be Person, Businessman, and Leader. However, other types of this entity, e.g., Solicitor, Restaurateur, and Writer might also be interesting to some users. Unpopular entity types can be useful for tail queries like, for example, `Is Donald Trump an American Television Producer' or `Is Donald Trump an American Television Actor'. It is then important to, given an entity in a KG, rank entity types attached to the entity by relevance to a certain user and information need as not always the most popular type is the most informative within a textual context. In this paper we address the entity type ranking problem by means of KG embedding models. In our work, we show that entity type ranking can be seen as a special case of the KG completion problem. Embeddings can be learned from the structural, probabilistic and contextual description information of the entities. We propose and evaluate our methods to find the most relevant entity type based on collection statistics and on the graph structure interconnecting entities and types. Experimental results show that our proposed approaches outperform the state-of-the-art type ranking models while, at the same time, being more efficient and scalable. Our approach focuses on the task of ranking a set of types associated to an entity in a background knowledge graph to select the most relevant types.
Unsupervised Segmentation of Images using CNN

Image segmentation literally means to classify each object or element in an image to different classes. Thus, we must cluster each pixels of the image to the appropriate class it should belong to. The segmentation of image is accomplished through unsupervised learning. Therefore, any training data or labels is not provided beforehand. A Convolutional Neural Network is used to assign each pixel in the input image to the appropriate cluster. The present system is considered as a Parent-Child track. Initially the Parent track deals with prediction of cluster labels with initialized network parameters. The Child track refines the cluster and calculates the SoftMax loss between predicted and refined labels. It is propagated back to parent track to adjust the network parameters such that the loss will decrease with each iteration.
Edge Detection using Hybrid Dynamic Binary Threshold

This paper suggests a novel hybrid adaptive edge detection algorithm. The proposed hybrid adaptive edge detection algorithm has the least PSNR (dB) and highest MSE, which indicates that as a better edge detection algorithm than the existing non-learning edge detection algorithms. Since, kernel convolution and thresholding are only involved in this hybrid edge detection algorithm where it becomes comparatively faster than the conventional non-learning edge detection operators.
ISSE: a new iterative sentence scoring and extraction scheme for automatic text summarization

An ever-increasing volume of contextual data appearing on various types of media overwhelms users sharing information, as well as bringing up storage concerns. Automatic text summarization mitigates these challenges and helps users cope more efficiently by generating more accurate and representative summaries. In this paper, we propose a new extractive, multi-document summarization method based on a new iterative sentence scoring and extraction scheme which uses the lexical chain and clustering concepts. The evaluation is done by a well-known standard evaluator in terms of conventional measures of performance, Recall, Precision, and F-score. The results show definite improvement at reasonable compression rates of around 10%, compared to other major benchmark summarizers in the literature.
Experimental Analysis Between Exploration and Exploitation

There exist hundreds of metaheuristic methods that can be employed to obtain the optimal value in an optimization problem. To present a good performance, every metaheuristic scheme requires to achieve an adequate balance between exploration and exploitation of the search space. Even though exploration and exploitation are considered two important concepts in metaheuristics computation, the main implications with this equilibrium have not yet been completely understood. Most of the existent studies consider only the comparison of their final results, which cannot appropriately evaluate the existent balance between both concepts. This chapter conducts an experimental study where it is analyzed the balance between exploration and exploitation on several of the most popular metaheuristic schemes. In the analysis, a diversity measurement for each dimension is employed to evaluate the equilibrium of each metaheuristic approach, considering a representative set of different optimization problems.
Straight Line Detection Method for Images Based on the Properties of Curvature

A straight lines detection and parameters estimation method for contour images was proposed. The algorithm is based on the property of straight lines as curves with zero curvature, which is the same as equality to zero of the second derivative of the curve function at each contour point. The second derivative at each contour pixel was estimated in sliding mode by convolving vector of pixel coordinates with finite length using a special mask. This algorithm also finds the endpoints of the segment. The line parameters were computed by the method of least squares. The algorithm has linear complexity, multiple of number of contour pixels on the binary image.
Problems of forming marketing competencies in the digital economy

Abstract The spread of digital technology is transforming society and the economy. These changes affect the labor market. Modern employers need flexible marketing staff who can think creatively and make decisions in conditions of high environmental turbulence. For this, it is necessary to change the approach in training future specialists, namely, to use those tools and methods in teaching marketing disciplines that will allow their creative professional competencies to be developed as efficiently as possible. The object of the study was the online marketing training technology in terms of the formation of creative competencies. The aim of the study was to identify the problems of the formation of marketing competencies in the digital economy. The research methodology was based on the presentation of the structural components of marketing activity, characterized by varying degrees of demand for creative competencies. The existing approaches and strategies to stimulate creativity in the context of online marketing training were identified, the current state of the online marketing toolkit was analyzed. A correlation matrix has been developed for the tools for obtaining and implementing creative ideas and emerging marketing competencies for an online marketing course. A matrix has also been developed for correlating job types and emerging marketing competencies for an online marketing course. The selection of suitable tools and types of tasks will help to create professional competencies of a marketer at a high level of their creative component.