Search

Aiming at the insufficient sample size and unbalanced category of the metal workpiece surface defect dataset in industrial production. But traditional image augmentation methods based on generative adversarial network (GAN) do not effectively correlate the dependency between local feature and global feature, and the loss function is not rational, which causes the problem of insufficient image diversity and poor quality. A relative mean generative adversarial network (TARGAN), driven by a twin attention mechanism, is proposed to generate high-quality defect images. This method employs the twin-attention mechanism that integrates the spatial attention module and the channel attention module. Moreover, it has validated the ability of the twin attention mechanism to improve the refinement of defective features by ablation study. Meanwhile, the loss function during training is improved from the absolute relationship judgment to the relative mean value of the different relationships to improve the diversity of the generated images. Compared with current GANs, this model is superior in generating high-quality and diverse metal workpiece surface defect images thus effectively improving the performance of the MobileNetV2-based classifier. By extended MGSD and NEU-UB based on this method, the accuracy of the defect classification model reached 99% and 98.25% respectively, which is 9.5% and 13.25% higher than the model without TARGAN expansion.

We studied the extent to which bitcoin blockchain security permanently depends on the underlying distribution of cryptocurrency market outcomes using daily blockchain and bitcoin data for 2014–2019...

Multivariate models to estimate environmental load on an offshore structure are an essential consideration. A reliable approach is necessary to capture the dependency among parameters and to be flexible in handling the statistical characteristics of ocean parameters. Previous studies on marine engineering usually assumed symmetric dependence when considering multivariate models. In this paper, both symmetric and asymmetric copula functions are constructed for modelling ocean parameters and the estimation of total environmental load. Results are compared with the traditional joint probability approach to demonstrate the advantage of using copula functions. The results reveal that environmental loads strongly depend on the significance of dependence, which is defined as the copula function’s correlation. The probability of failure, estimated using copula functions, is higher than the traditional joint probability estimate. In addition to this, Root-mean-square errors (RMSE) and the mean absolute errors using copula functions are lower than those of the traditional joint probability modeling. Thus, the use of copula functions provides a more conservative approach to safety. Therefore, the use of copula functions to capture both dependence types, while estimating ocean environmental loads provide a better understanding of the environmental load and its contribution to the failure probability.

The aim of this paper is to presents a theoretical analysis on squeeze-film characteristics of a rough porous circular stepped plate in the vicinity of pressure-dependent viscosity and lubrication by micropolar fluids. A closed-form expression for non-dimensional pressure, load, and squeezing time is derived based on Eringen’s theory, Darcy’s equation, and Christensen’s stochastic approach. Results indicate that the effects of pressure-dependent viscosity, surface roughness, and micropolar fluids play an important role in increasing the load-carrying capacity and squeezing time, whereas the presence of porous media decreases the load-carrying capacity and squeezing time of the rough porous circular stepped plates.

Remaining useful life (RUL) prediction under time-varying operating conditions is critical to the prognostics and health management of rotating machinery. In the literature, both the degradation rate and variation of a machinery component are often assumed to be solely dependent on operating conditions. However, this strong assumption is usually violated in many industrial applications. In this article, a systematic method for RUL prediction for a rotating machinery component is developed by considering the joint dependency of degradation rate and variation on time-varying operating conditions. In particular, a system state function and an observation function are utilized to characterize the component's degradation process. A quantitative relationship between the drift and diffusion parameters is established to reflect their joint dependency on the operating conditions. A two-stage hybrid approach that jointly implements maximum likelihood estimation and least squares estimation methods is proposed to facilitate parameter estimation in model development based on offline degradation data, and a Bayesian algorithm based on online condition monitoring data is utilized for RUL prediction in online implementation. A simulation study and a real application to rolling element bearings are provided to illustrate the effectiveness of the proposed method in practice.

Target-oriented Opinion Words Extraction (TOWE) aims to identify opinion words toward a specific target given the sentence. Syntax structure, which contains dependency relationships among words, is a vital clue for this task. With the help of syntax structure as a constraint, the model could remove irrelevant words and focus on tokens that are relevant to the given target. Directly adapting existing syntactic-based methods faces the problem that these models do not explicitly learn target-centric representations. Another challenge is that prior works only learn fixed order dependency relations, while context words require syntactic information in different scales. To handle these issues, we propose Target-Specific Graph Convolutional Network (TS-GCN) to explicitly integrate dependency structure. The proposed method could build high-quality syntax-aware representations by propagating target information to syntactically related words via graph convolution. Furthermore, we design a memory-based module to dynamically learn multi-granularity syntactic knowledge and infuse local features. Experimental results demonstrate the effectiveness of our method, and we achieve state-of-the-art performances on four SemEval datasets.

Nowadays, bike-sharing is available in many cities, solving the problem of the last mile, and it is an environmental-friendly way to commute. However, there is a tidal phenomenon in the bike-sharing system, and the rents/returns of bikes at different stations are unbalanced. Thus, bikes at different stations need to be rebalanced regularly and station-level demand prediction plays an essential role in bike-sharing rebalancing. In this paper, a novel deep graph convolutional network (GCN) model with temporal attention (TAGCN) is proposed for bike check-out/in number prediction of each station. TAGCN can not only model the spatial and temporal dependency between varying stations, but also reflect the influence of different time granularity, which are hour-level, day-level and week-level time periodicity. With the help of well-designed temporal attention mechanism, our model can capture the dynamical temporal correlations and comprehensive spatial patterns in bike check-out/in flow effectively. The proposed model consistently outperforms state-of-the-art methods on four real-world bike-sharing datasets that are four seasons data of Divvy Bike System in Chicago.

We explored the dependency of strength anisotropy on loading rate in coal both experimentally and numerically. An improved numerical modeling method was developed for this investigation to reduce the random error caused by the mechanical differences of coal specimens. After that, specimens with different anisotropy angles (the angle between the coring direction and bedding plane orientation) were processed (0°, 15°, 30°, 45°, 60°, and 90°). These specimens were then scanned by the X‐ray Computed Tomography (CT) and tested by unconfined loading frame. Uniaxial compressive simulations were performed on 15 numerical models with different anisotropy angles at various loading rates (1.0 × 10−6, 5.0 × 10−6, 10.0 × 10−6, and 20.0 × 10−6 mm/step). The results indicate that the uniaxial compressive strength (UCS) of coal generally first decreases and then increases as the anisotropy angle increases from 0° to 90° for all loading rates. It is also found that the UCS increases significantly with the increasing loading rate at anisotropic angles of 30° and 45°, while it is less obvious at anisotropic angles of 0° and 90°. This results in a reduced USC anisotropy at high loading rates. Meanwhile, a negative exponential equation is obtained to describe the variation of UCS with the loading rate at different anisotropic angles. And a unified equation is then developed and verified to define the UCS as a function of the loading rate and anisotropic angle.

Hydrological models have proliferated in the past several decades prompting debates on the virtues and shortcomings of various modelling approaches. Rather than critiquing individual models or modelling approaches, the objective here is to address the critical issues of scaling and hydrological process representation in various types of models with suggestions for improving these attributes in a parsimonious manner that captures and explains their functionality as simply as possible. This discussion focuses mostly on conceptual and physical/process-based models where understanding the internal catchment processes and hydrologic pathways is important. Such hydrological models can be improved by using data from advanced remote sensing (both spatial and temporal) and derivatives, applications of machine learning, flexible structures, and informing models through nested catchment studies in which internal catchment processes are elucidated. Incorporating concepts of hydrological connectivity into flexible model structures is a promising approach for improving flow path representation. Also important is consideration of the scale dependency of hydrological parameters to avoid scale mismatch between measured and modelled parameters. Examples are presented from remote high-elevation regions where water sources and pathways differ from temperate and tropical environments where more attention has been focused. The challenge of incorporating spatially and temporally variable water inputs, hydrologically pathways, climate, and land use into hydrological models requires modellers to collaborate with catchment hydrologists to include important processes at relevant scales—i.e. develop smarter hydrological models.

Published Jun 2022 Although research in sentence comprehension has suggested that processing long-distance dependencies involves maintenance between the elements that form the dependency, studies on maintenance of long-distance subject–verb (SV) dependencies are scarce. The few relevant studies have delivered mixed results using self-paced reading or phoneme-monitoring tasks. In the current study, we used eye tracking during reading to test whether maintaining a long-distance SV dependency results in a processing cost on an intervening adverbial clause. In Experiment 1, we studied this question in Spanish and found that both go-past reading times and regressions out of an adverbial clause to the previous regions were significantly increased when the clause interrupts a SV dependency compared to when the same clause doesn’t interrupt this dependency. We then replicated these findings in English (Experiment 2), observing significantly increased go-past reading times on a clause interrupting a SV dependency. The current study provides the first eye-tracking data showing a maintenance cost in the processing of SV dependencies cross-linguistically. Sentence comprehension models should account for the maintenance cost generated by SV dependency processing, and future research should focus on the nature of the maintained representation. This research was partially funded by Ministerio de Economía y Competitividad, Agencia Estatal de Investigación & Fondo Europeo de Desarrollo Regional Grants PSI2015-65694-P, and RTI2018-096311-B-I00 to Nicola Molinaro, and RYC-2017–22015 and FFI2016-76432-P_LAMPT to Simona Mancini; by Eusko Jaurlaritza Grants PI_2016_1_0014 to Nicola Molinaro, PRE_2018_2_0074 and EP_2018_1_0042 to Bojana Ristic; and by Agencia Estatal de Investigación’s Severo Ochoa excellence program Grant SEV2015– 0490 to the Basque Center on Cognition, Brain and Language.

This paper argues that training GANs on local and non-local dependencies in speech data offers insights into how deep neural networks discretize continuous data and how symbolic-like rule-based morphophonological processes emerge in a deep convolutional architecture. Acquisition of speech has recently been modeled as a dependency between latent space and data generated by GANs in Begu\v{s} (2020b; arXiv:2006.03965), who models learning of a simple local allophonic distribution. We extend this approach to test learning of local and non-local phonological processes that include approximations of morphological processes. We further parallel outputs of the model to results of a behavioral experiment where human subjects are trained on the data used for training the GAN network. Four main conclusions emerge: (i) the networks provide useful information for computational models of speech acquisition even if trained on a comparatively small dataset of an artificial grammar learning experiment; (ii) local processes are easier to learn than non-local processes, which matches both behavioral data in human subjects and typology in the world's languages. This paper also proposes (iii) how we can actively observe the network's progress in learning and explore the effect of training steps on learning representations by keeping latent space constant across different training steps. Finally, this paper shows that (iv) the network learns to encode the presence of a prefix with a single latent variable; by interpolating this variable, we can actively observe the operation of a non-local phonological process. The proposed technique for retrieving learning representations has general implications for our understanding of how GANs discretize continuous speech data and suggests that rule-like generalizations in the training data are represented as an interaction between variables in the network's latent space., Comment: In press at Computer Speech & Language

Dependency parsing is a complex process in natural language text processing, text to semantic transformation. The efficiency improvement of dependency parsing is a current and an active research area in the NLP community. The paper presents four transition-based dependency parser models with implementation using DL4J classifiers. The efficiency of the proposed models were tested with Hungarian language corpora. The parsing model uses a data representation form based on lightweight embedding and a novel morphological-description-vector format is proposed for the input layer. Based on the test experiments on parsing Hungarian text documents, the proposed list-based transitions parsers outperform the widespread stack-based variants.

In this paper the site categorization criteria and the corresponding site amplification factors proposed in the 2021 draft of Part 1 of Eurocode 8 (2021-draft, CEN/TC250/SC8 Working Draft N1017) are first introduced and compared with the current version of Eurocode 8, as well as with site amplification factors from recent empirical ground motion prediction equations. Afterwards, these values are checked by two approaches. First, a wide dataset of strong motion records is built, where recording stations are classified according to 2021-draft, and the spectral amplifications are empirically estimated computing the site-to-site residuals from regional and global ground motion models for reference rock conditions. Second, a comprehensive parametric numerical study of one-dimensional (1D) site amplification is carried out, based on randomly generated shear-wave velocity profiles, classified according to the new criteria. A reasonably good agreement is found by both approaches. The most relevant discrepancies occur for the shallow soft soil conditions (soil category E) that, owing to the complex interaction of shear wave velocity, soil deposit thickness and frequency range of the excitation, show the largest scatter both in terms of records and of 1D numerical simulations. Furthermore, 1D numerical simulations for soft soil conditions tend to provide lower site amplification factors than 2021-draft, as well as lower than the corresponding site-to-site residuals from records, because of higher impact of non-linear (NL) site effects in the simulations. A site-specific study on NL effects at three KiK-net stations with a significantly large amount of high-intensity recorded ground motions gives support to the 2021-draft NL reduction factors, although the very limited number of recording stations allowing such analysis prevents deriving more general implications. In the presence of such controversial arguments, it is reasonable that a standard should adopt a prudent solution, with a limited reduction of the site amplification factors to account for NL soil response, while leaving the possibility to carry out site-specific estimations of such factors when sufficient information is available to model the ground strain dependency of local soil properties.

High-level structure (HLS) extraction recovers 3D elements on human-made surfaces (objects, buildings, ground, etc.). There are several approaches to HLS extraction. However, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds extracted from the camera images. In general, 3D point cloud and multiple views approaches have good performance for certain scenes with video sequences or image sequences, but they need sufficient parallax in order to guarantee accuracy. To address this problem, an alternative is to process a single RGB image seeking to interpret areas of the images where the human-made structure may be observed, thus removing parallax dependency, but adding the challenge of having to interpret image ambiguities correctly. Motivated by the latter, we propose a methodology for 3D volumetric structure extraction from a single image. Our strategy is to divide and simplify the 3D structure extraction process. For that, our methodology has three steps. First, the structure recognition step provides the segmentation, location, and delimitation of the urbanized structures in the scene. Second, we propose a graph analysis to classify and locate the boundaries between the different urbanized structures in the scene. Third, we use a proposed CNN and the pinhole camera model to extract the 3D volumetric structure. On the other hand, we evaluate this methodology in synthetic and public datasets.

Hydrological and climatic data at finer temporal resolutions are considered essential to model hydrological processes, especially for short duration flood events. Parameter transferability is an essential approach to obtain sub-daily hydrological simulations at many regions without sub-daily data. In this study, the objective is to investigate temporary dependency of parameter sensitivity for different flood types, which contributes to research into parameter transferability. This study is conducted in a medium-sized basin using a distributed hydrological model, DHSVM. Thirty-six flood events in the period of 04/12/2006–07/01/2013 in the Jinhua River basin, China, are classified into three flood types (FF: flash flood, SRF: short rainfall flood and LRF: long rainfall flood) by using the fuzzy decision tree method. The results show that SRF is the dominant flood type in the study area, followed by LRF and FF. Runoff simulations of FF and SRF are more sensitive to parameter perturbations than those of LRF. Sensitive parameters are highly dependent on temporal resolutions. The temporary dependency of LRF is the highest, followed by SRF and FF. More attention should be payed to sensitive and highly temporal dependent parameters in a subsequent parameter transfer process. Further study into this result is required to test the applicability. HIGHLIGHTS Runoff simulations of FF and SRF are more sensitive to parameter perturbations than those of LRF.; Sensitive parameters are highly dependent on temporal resolutions.; The temporary dependency of LRF is the highest, followed by SRF and FF.; This study contributes to further reduce uncertainty in parameter estimation by considering parameter temporary dependency.

Using the definition of the velocity of vacuum particles or streamlines from the Schrödinger equation, it was possible to determine the dependence of the radius and two angles on time. In the general case, several complex values of the radius and two angles were obtained as a function of time. But using continuous coordinates, it was possible to determine the change in the complex radius and two angles for the hydrogen atom. The resulting total kinetic energy of the atom differs from its own electrical energy, which provides the radiation of the atom.

With the recent advancement in smart grid technology, real-time monitoring of grid is utmost essential. State estimation-based solutions provide a critical tool in monitoring and control of smart grids. Recently there has been an increased focus on false data injection attacks which can circumvent the traditional statistical bad data detection algorithm. Most of the research methodologies focus on the presence of FDIA in measurement set, whereas their exact locations remain unknown. To cater this issue, this paper proposes a deep learning architecture for detection of the exact locations of data intrusions in real-time. This deep learning model in association with traditional bad data detection algorithms is capable of detecting both structured as well as unstructured false data injection attacks. The deep learning architecture is not dependent on statistical assumptions of the measurements, it emphasizes on the inconsistency and co-occurrence dependency of potential attacks in measurement set, thus acting as a multilabel classifier. Such kind of architecture remains model free without any prior statistical assumptions. Extensive research work on IEEE test-bench shows that this scheme is capable of identifying the locations for intrusion under varying noise scenarios. Such kind of an approach shows potential results also in detection of presence of falsified data.

Women’s dependency has been a widely debated topic all over the world. A woman is always expected to do only what is appropriate in contemporary society since infancy, and they are not prepared for independence or self-sufficiency from the moment they are born. The idea of female dependency began to cause confusion and discontent among the ‘new independent women’. The fear of being independent then termed as the Cinderella Complex. Cinderella Complex refers to the fear of being independent, causes unconscious desire to be taken care of by others (C. Dowling, 1981). Here the investigator planned to go through the studies conducted in India as well as outside to have a deep understanding on the concept of Cinderella Complex, its dimensions, method of study, related concepts, implications etc. The investigators used meta-analysis as the method for approaching the problem. Six studies which met the inclusion criteria were selected for this study. Findings of the study show that it is interpreted that women having high scores in Cinderella Complex are bound to show negative motivation towards personal growth. Furthermore, an interventional approach is being developed to motivate and train young women towards personal growth. KEYWORDS: Cinderella complex, Meta-analysis, Women, Dependency

PurposeThis paper analyses the relation between occupational characteristics and the probability that a worker in the Netherlands has a false self-employed arrangement instead of an employee arrangement. These are arrangements in which self-employed workers perform tasks in the hierarchy of the firm as if they were employees.Design/methodology/approachData from the Dutch Labour Force Survey is used to analyse the relationship between occupational skill, routine and wage level and the probability to be a false self-employed or a standard or non-standard employee.FindingsThe results show that the probability to be false self-employed decreases slightly with the skill level of the occupation, but there is no evidence that false self-employment is more likely in low paid, routine occupations. Workers in the lowest paid occupations are more likely to have a non-standard contract as an employee. False self-employment arrangements are more likely in the (lower) middle paid occupations. Finally, the results show that working in the highest paid occupations increases the probability of being in a false self-employed arrangement, but only in arrangements that are characterised by economic and organizational dependency. These are arrangements with financial dependency on one client for income combined with dependency on this client on when and where to work.Originality/valueThis study makes an important contribution to the literature on identifying vulnerable self-employed workers as well as to the literature on mechanisms behind the growth of solo self-employment.

In early-phase requirements engineering, modeling stakeholder goals and intentions helps stakeholders understand the problem context and evaluate tradeoffs, by exploring possible “what if” questions. Prior research allows modelers to make evaluation assignments to desired goals and generate possible selections for task and dependency alternatives, but this treats models as static snapshots, where the evaluation of the fulfillment of an intention remains constant once it has been determined. Using these techniques, stakeholders are unable to reason about possible evolutions, leaving questions about project viability unanswered when the fulfillment of goals or availability of components is not guaranteed in the future. In this article, we formalize the Evolving Intentions framework for specifying, modeling, and reasoning about goals that change over time. Using the Tropos language, we specify a set of functions that define how intentions and relationships evolve, and use path-based analysis for asking a variety of “what if” questions about such changes. We illustrate the framework using the Bike Lanes example and prove correctness of the analysis. Finally, we demonstrate scalability and effectiveness, enabling stakeholders to explore model evolution.

The work presented in this paper investigates the ability of BERT neural language model pretrained in Italian to embed syntactic dependency relationships into its layers, by approximating a Dependency Parse Tree. To this end, a structural probe, namely a supervised model able to extract linguistic structures from a language model, has been trained leveraging the contextual embeddings from the layers of BERT. An experimental assessment has been performed using an Italian version of BERT-base model and a set of datasets for Italian labelled with Universal Dependencies formalism. The results, achieved using standard metrics of dependency parsers, have shown that a knowledge of the Italian syntax is embedded in central-upper layers of the BERT model, according to what observed in literature for the English case. In addition, the probe has been also used to experimentally evaluate the BERT model behaviour in case of two specific syntactic phenomena in Italian, namely null-subject and subject-verb-agreement, showing better performance than an Italian state-of-the-art parser. These findings can open a path for the development of new hybrid approaches, exploiting the probe to integrate or improve limits or weaknesses in analysing articulated constructions of Italian syntax, traditionally complex to be parsed.

In this introductory paper to the inaugural volume of the journalInteractional Linguistics, we raise the question of what a theory of language might look like once we factor time into explanations of regularities in linguistic phenomena. We first present a historical overview that contextualises interactional approaches within the broader field of linguistics, and then focus on temporality as a key dimension of language use in interaction. By doing so, we discuss issues of emergence and its consequences for constituency and dependency, and of projection and its relation to action formation within and across languages. Based on video-recorded conversational data from French and Garrwa (Australian), we seek to illustrate how the discipline of linguistics can be enriched by attending to the temporal deployment of patterns of language use, and how this may in turn modify what we understand to be language structure.

PurposeThe purpose of this study is to provide a new way to optimize a portfolio and to show that combining the Hurst exponent and wavelet analysis may help to increase portfolio returns.Design/methodology/approachThe authors use the Hurst exponent and wavelet analysis to study the long-term dependencies between sovereign bonds and sectoral indices of India. The authors further construct and evaluate the performance of three portfolios constructed on the basis of Hurst standard deviation (SD) – global minimum variance (GMV), most diversified portfolio (MDP) and equal risk contribution (ERC).FindingsThe authors find that an ERC portfolio generates positive superior return as compared other two. Since our sample includes periods of two crisis – post-2007 financial crisis and the ongoing pandemic, this study reveals that combining government bond with equities and gold provides a higher returns when the portfolios are constructed using the risk exposures of each asset in the overall portfolio risk.Practical implicationsThe findings provide guidance to portfolio managers by helping them to select assets using the Hurst approach and wavelet analysis thereby increasing the portfolio returns.Originality/valueIn this study, the authors use a combination of Hurst exponent and wavelet analysis to understand the long-term dependencies among various assets and provide a new methodology to optimize a portfolio. As far as the authors’ knowledge, no study in the past has attempted to provide a joint framework for portfolio optimization and therefore this study is the first to apply this methodology.

In transfer learning, two major activities, i.e., pretraining and fine-tuning, are carried out to perform downstream tasks. The advent of transformer architecture and bidirectional language models, e.g., bidirectional encoder representation from transformer (BERT), enables the functionality of transfer learning. Besides, BERT bridges the limitations of unidirectional language models by removing the dependency on the recurrent neural network (RNN). BERT also supports the attention mechanism to read input from any side and understand sentence context better. It is analyzed that the performance of downstream tasks in transfer learning depends upon the various factors such as dataset size, step size, and the number of selected parameters. In state-of-the-art, various research studies produced efficient results by contributing to the pretraining phase. However, a comprehensive investigation and analysis of these research studies is not available yet. Therefore, in this article, a systematic literature review (SLR) is presented investigating thirty-one (31) influential research studies published during 2018–2020. Following contributions are made in this paper: (1) thirty-one (31) models inspired by BERT are extracted. (2) Every model in this paper is compared with RoBERTa (replicated BERT model) having large dataset and batch size but with a small step size. It is concluded that seven (7) out of thirty-one (31) models in this SLR outperforms RoBERTa in which three were trained on a larger dataset while the other four models are trained on a smaller dataset. Besides, among these seven models, six models shared both feedforward network (FFN) and attention across the layers. Rest of the twenty-four (24) models are also studied in this SLR with different parameter settings. Furthermore, it has been concluded that a pretrained model with a large dataset, hidden layers, attention heads, and small step size with parameter sharing produces better results. This SLR will help researchers to pick a suitable model based on their requirements.

Motivation Cancer dependencies provide potential drug targets. Unfortunately, dependencies differ among cancers and even individuals. To this end, visible neural networks (VNNs) are promising due to robust performance and the interpretability required for the biomedical field. Results We design Biological visible neural network (BioVNN) using pathway knowledge to predict cancer dependencies. Despite having fewer parameters, BioVNN marginally outperforms traditional neural networks (NNs) and converges faster. BioVNN also outperforms an NN based on randomized pathways. More importantly, dependency predictions can be explained by correlating with the neuron output states of relevant pathways, which suggest dependency mechanisms. In feature importance analysis, BioVNN recapitulates known reaction partners and proposes new ones. Such robust and interpretable VNNs may facilitate the understanding of cancer dependency and the development of targeted therapies. Availability and implementation Code and data are available at https://github.com/LichtargeLab/BioVNN Supplementary information Supplementary data are available at Bioinformatics online.

This article deals with the design and practical evaluation of a wireless charging system for e-scooters. As wireless charging undertakes popularity, initially, the state of the art within application area is realized. Consequently, due to variability of the configuration of the whole system, several alternatives are discussed considering the utilization of the power converter stages. High attention is given to the construction of the coupling elements and calculation of the main circuit components of the considered power converters. The experimental part of the paper is supported by the measurements of key properties, i.e., the operational characteristics of the whole converter system together with coupling coils. Here, the efficiency characteristics, together with the output power characteristics in dependency on the mutual distance between the transmitting and receiving coils, are evaluated. Received results represent an adequate design approach and practical use of the proposed Wireless Power Transfer (WPT) e-scooter charging configuration.

Static malware detection approaches are time-consuming and cannot deal with code obfuscation techniques. Dynamic malware detection approaches, on the other hand, address these two challenges, however, suffer from behavioral ambiguity, such as the system calls obfuscation. In this paper, we introduce Markhor, a dynamic and behavior-based malware detection approach. Markhor uses system call data dependency and system call control dependency sequences to create a weighted list of malicious patterns. The list is then used to determine the malicious processes. Next, the similarity of a file system call sequences to a malicious pattern is extracted based on a fuzzy algorithm and the file nature is determined. The evaluation results reveal the efficiency of Markhor in terms of accuracy (0.982), precision (0.976), and F-measure (0.982).

Fuzzy-rough set theory is an efficient method for attribute reduction. It can effectively handle the imprecision and uncertainty of the data in the attribute reduction. Despite its efficacy, current approaches to fuzzy-rough attribute reduction are not efficient for the processing of large data sets due to the requirement of higher space complexities. A limited number of accelerators and parallel/distributed approaches have been proposed for fuzzy-rough attribute reduction in large data sets. However, all of these approaches are dependency measure based methods in which fuzzy similarity matrices are used for performing attribute reduction. Alternative discernibility matrix based attribute reduction methods are found to have less space requirements and more amicable to parallelization in building parallel/distributed algorithms. This paper therefore introduces a fuzzy discernibility matrix-based attribute reduction accelerator (DARA) to accelerate the attribute reduction. DARA is used to build a sequential approach and the corresponding parallel/distributed approach for attribute reduction in large data sets. The proposed approaches are compared to the existing state-of-the-art approaches with a systematic experimental analysis to assess computational efficiency. The experimental study, along with theoretical validation, shows that the proposed approaches are effective and perform better than the current approaches.

Temporal knowledge graphs (TKGs) have become useful resources for numerous Artificial Intelligence applications, but they are far from completeness. Inferring missing events in temporal knowledge graphs is a fundamental and challenging task. However, most existing methods solely focus on entity features or consider the entities and relations in a disjoint manner. They do not integrate the features of entities and relations in their modeling process. In this paper, we propose TPmod, a tendency-guided prediction model, to predict the missing events for TKGs (extrapolation). Differing from existing works, we propose two definitions for TKGs: the Goodness of relations and the Closeness of entity pairs. More importantly, inspired by the attention mechanism, we propose a novel tendency strategy to guide our aggregated process. It integrates the features of entities and relations, and assigns varying weights to different past events. What is more, we select the Gate Recurrent Unit (GRU) as our sequential encoder to model the temporal dependency in TKGs. Besides, the Softmax function is employed to generate the final decreasing group of candidate entities. We evaluate our model on two TKG datasets: GDELT-5 and ICEWS-250. Experimental results show that our method has a significant and consistent improvement compared to state-of-the-art baselines.

Dependency functions as a keyword in care theory. However, care theorists have spelled out the ontological and moral ramifications of dependency in different and often conflicting ways. In this article, I argue that conceptual disputes about dependency betray a fundamental discordance among authors, rooted in the empirical premises of their arguments. Hence, although authors appear to share a vocabulary of dependency, they are not writing about quite the same phenomenon. I seek to elucidate these differences by teasing out and comparing different conceptions of dependency found in the literature. Borrowing a phrase from Eva Kittay, I trace four “paradigm cases” of dependency: the infant, the physically disabled person, the profoundly intellectually disabled person, and the refugee. These paradigm cases serve as the empirical touchstone from which theorists extract their conceptions of dependency. Each paradigm case, moreover, permits (or even implores) a particular ethical sensibility toward care. How we understand and value dependency thus seems to determine how we understand and value care, and vice versa. In this way, I contend, our normative orientation toward care might influence what sorts of dependency we see—and, by extension, which forms of dependency we fail to notice.

We present a numerical attribute dependency method for massive datasets based on the concepts of direct and inverse fuzzy transform. In a previous work, we used these concepts for numerical attribute dependency in data analysis: Therein, the multi-dimensional inverse fuzzy transform was useful for approximating a regression function. Here we give an extension of this method in massive datasets because the previous method could not be applied due to the high memory size. Our method is proved on a large dataset formed from 402,678 census sections of the Italian regions provided by the Italian National Statistical Institute (ISTAT) in 2011. The results of comparative tests with the well-known methods of regression, called support vector regression and multilayer perceptron, show that the proposed algorithm has comparable performance with those obtained using these two methods. Moreover, the number of parameters requested in our method is minor with respect to those of the cited in the above two algorithms.

The current article discusses the use of the dependency graph method to design and analyze automatic transmissions. Different goals may be served by modeling an automatic transmission using graphs. The most important of them are: determining the gear ratios for gears and the analysis of speed and acceleration of the rotating elements. Game tree-structure methods can be used to analyze the functional schemes of the selected gears. This paper describes the method of generating a system of kinematics equations for signal dependency graphs. This allows the generalization and extension of the algorithmic approach, and also further analyses and syntheses, such as checking the isomorphism of the proposed solutions and determining the validity of the structure and the operational parameters of the analyzed gears.

Deep Learning approach using probability distribution to natural language processing achieves significant accomplishment. However, natural languages have inherent linguistic structures rather than probabilistic distribution. This paper presents a new graph-based representation of syntactic structures called syntactic knowledge graph based on dependency relations. This paper investigates the valency theory and the markedness principle of natural languages to derive an appropriate set of dependency relations for the syntactic knowledge graph. A new set of dependency relations derived from the markers is proposed. This paper also demonstrates the representation of various linguistic structures to validate the feasibility of syntactic knowledge graphs.

The fields of data science and data mining are enduring high-dimensionality issues because of a high volume of data. Conventional machine learning techniques give disgruntled responses to high-dimensional datasets. Feature selection is used to get the appropriate information from the dataset to reduce the dimensionality of the data. The recently proposed Salp Swarm Algorithm (SSA) is a population-based meta-heuristic optimization technique inspired by the Sea Salps Swarming technique. SSA failed to converge initial random solutions to the global optimum owing to its complete dependency on the number of iterations for the process of exploration and exploitation. The proposed improved SSA (iSSA) aims to enhance the ability of Salps to explore divergent areas by randomly updating its location. Randomizing the Salps location via Levy flight enriches the exploitation potential of SSA resulting in it converging the model toward the global optima. The performance of the proposed iSSA is investigated using six different high-dimensional microarray datasets. While comparing the ability to converge, it is understood that the proposed model outperforms SSA providing 0.1033% more confidence in the selected features. The results of the simulation revealed that the iSSA can provide better competitive and significant results compared to SSA.

This paper carries on a reliability sensitivity analysis on the non-ablative thermal protection system (TPS) of spacecraft during the conceptual design. In the previous work on probabilistic estimation of TPS, the temperature dependency of material properties has not yet been investigated. In this paper, however, the temperature dependency of material properties is characterized and considered during the thermal analysis and reliability sensitivity analysis. Compared to general black-box problems, three special challenges of uncertainty analysis for TPS in real practice are a generally high dimension and multiple outputs on massive meshing nodes, a high level of reliability design target, and a fast evaluation process due to the requirement of the conceptual design. In order to cope with these challenges, a unified reliability sensitivity analysis methodology including multi-input and multi-output support vector machines (MIMO-SVMs), a space-partition (SP) method, and a generalized subset simulation (GSS) is proposed for the conceptual design of TPS with temperature-dependent materials. MIMO-SVMs are used to approximate the thermal responses to save calculation costs. The variance-based global sensitivity indices are calculated by SP to make full use of the information within samples. Based on the sensitivity indices, a dimension reduction process is introduced. In the reduced space, GSS is used to simultaneously evaluate all the failure probabilities by fully exploring the correlation among all the LSFs. Two application examples including a lifting body vehicle model and a spacecraft model are used to demonstrate the performance of the proposed methodology.

Link prediction has received a great deal of attention from researchers. Most of the existing researches are based on the network topology but ignore the importance of its preference; for aggregating multiple pieces of information, they normally sum up them directly. In this paper, a path-based probabilistic model is proposed to estimate the potential connectivity between any two nodes. It takes carefully the effective influence of nodes and the dependency among paths between two fixed nodes into account. Furthermore, we formulate the connectivity of two inner-community nodes and that of two inter-community nodes. The qualitative analysis shows that the links between inner-community nodes are more likely to be predicted by the proposed model. The performance is verified on both the multi-barbell network and Lesmis network. Considering the proposed model’s practicability, we develop an algorithm that iterates over the adjacent matrix to simulate paths of different lengths, with the parameters automatically grid-searched. The results of the experiments show that the proposed model outperforms competitive methods.

Groundwater models have evolved to encompass more aspects of the water cycle, but the incorporation of realistic boundary conditions representing surface water remains time‐consuming and error‐prone. We present two Python packages that robustly automate this process using readily available hydrography data as the primary input. SFRmaker creates input for the MODFLOW SFR package, while Linesink‐maker creates linesink string input for the GFLOW analytic element program. These programs can reduce weeks or even months of manual effort to a few minutes of execution time, and carry the added advantages of reduced potential for error, improved reproducibility and facilitation of step‐wise modeling through reduced dependency on a particular conceptual model or discretization. Two real‐world examples at the county to multi‐state scales are presented.

Smart contracts are decentralized applications running on Blockchain. A very large number of smart contracts has been deployed on Ethereum. Meanwhile, security flaws of contracts have led to huge pecuniary losses and destroyed the ecological stability of contract layer on Blockchain. It is thus an emerging yet crucial issue to effectively and efficiently detect vulnerabilities in contracts. Existing detection methods like Oyente and Securify are mainly based on symbolic execution or analysis. These methods are very time-consuming, as the symbolic execution requires the exploration of all executable paths or the analysis of dependency graphs in a contract. In this work, we propose ContractWard to detect vulnerabilities in smart contracts with machine learning techniques. First, we extract bigram features from simplified operation codes of smart contracts. Second, we employ five machine learning algorithms and two sampling algorithms to build the models. ContractWard is evaluated with 49502 real-world smart contracts running on Ethereum. The experimental results demonstrate the effectiveness and efficiency of ContractWard. The predictive Micro-F1 and Macro-F1 of ContractWard are over 96% and the average detection time is 4 seconds on each smart contract when we use XGBoost for training the models and SMOTETomek for balancing the training sets. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Aim Separating the biotic and abiotic factors controlling species distributions has been a long-standing challenge in ecology and biogeography. Joint species distribution models (JSDMs) have emerged as a promising statistical framework towards this objective by simultaneously modelling the environmental responses of multiple species and approximating species associations based on patterns in their (co-)occurrences. However, the signature of biotic interactions should be most evident at fine spatial resolutions. Here, we test how the resolution of input data affects the inferences from JSDMs. Location Switzerland. Taxon Birds. Methods Using standardized survey data of 43 woodland bird species and 8 climatic, topographic and vegetation structural predictors, we fit JSDMs at different spatial resolutions (125–1000 m) and sampling periods (1 and 5 years). In addition, we calculate functional similarity among all species as an independent proxy of biotic interactions, specifically competition. We then assess how JSDM performance and estimates vary with the spatial resolution of the input data and test whether species associations are consistent across grain sizes and with the alternative approach based on functional similarity. Results Our results show better model performance at coarser spatial resolutions and for longer sampling periods. Although pairwise species associations estimated in JSDMs were generally shifted towards positive values, we found a higher proportion of negative associations at fine spatial resolutions. Strikingly, estimates were not consistent across spatial scales and frequently switched between positive and negative values. Moreover, estimated species associations tended to be more positive for functionally similar species. Main conclusions Our results show that species associations are more differentiated, that is, cover a broader range of values, at finer spatial resolutions. Yet, their positive correlation with functional similarity and the general over-representation of positive associations suggest that shared responses to unobserved environmental predictors rather than biotic interactions underlie these scaling effects, cautioning against a naive interpretation of species associations estimated by JSDMs at any spatial scale. ISSN:0305-0270 ISSN:1365-2699

Frequency response analysis (FRA) is a widely accepted diagnostic method to assess the mechanical integrity of the active parts within a power transformer. Recent studies in the field of FRA have led to the standardization of its measurement procedure as described in the IEC and IEEE standards. However, the interpretation of FRA results is still limited to the analysis of the experts in the field. Using numerical indices is one of the methods introduced for the FRA interpretation, which quantifies the differences in FRA signatures. There are numerous indices proposed in the literature. However, there is no comprehensive comparison study to assess the sensitivity of these numerical indices against different faults. This contribution is an effort in this direction, which collects all indices on a single platform, compares and categories them based on their characteristics, to announce more appropriate indices as the standard ones. This paper defines the criteria for an appropriate index such as monotonicity, linearity, sensitivity, data-points dependency and index ratio. The evaluation of the indices is performed using 10 case studies from an experimental setup, molded transformer, and real power transformer cases. The results of this paper reveal that some indices stand out from the others in terms of performance and can be considered as the most appropriate ones that can satisfactorily calculate the changes between FRA signatures. The results also provide useful facts and guidelines for the standardization process of numerical indices for their implementation in the FRA studies.

This paper studies the real-time dynamic energy management (DEM) of multi microgrids (MMGs) considering active and reactive power flow constraints, voltage constraints, battery operational characters, and uncertainties in the renewable generation and load. A stochastic spatiotemporal decomposition decision-making framework is proposed based on approximate dynamic programming (ADP) to make decentralized decisions in both spatial and temporal dimensions. The tie-line power and the state of charge (SOC) of the battery are coordinated in real time to deal with the uncertainties in the upcoming future, preserving the decision independence of MGs and periods. And the shift factors are derived to consider active and reactive power flow constraints of MMGs in the stochastic spatiotemporal decomposition framework. Moreover, the historical information is utilized offline to avoid the dependency on forecast information and iterative calculation, while near-optimal solutions can be obtained in real time. Case studies on several MMG test systems and a real MMG system demonstrate the effectiveness of the proposed approach.

In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring the structure of Gene Regulatory Networks (GRNs). The Path Consistency (PC) algorithm is one of the popular methods to infer the structure of GRNs. However, this group of methods still has limitations and there is a potential for improvements in this field. For example, the PC-based algorithms are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different orders of CMI tests. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while other well-known PC-based methods only consider the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with other well-known PC-based methods. According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported previously are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2.