Occurrence Of Words Research Articles

Corpus-based age of word acquisition: Does it support the validity of adult age-of-acquisition ratings?

Age of acquisition (AoA) is presumed to reflect the age or relative order in which words are learned, but is often measured using adult ratings or adult-reported observations and might thus reflect more about the adult language than about the acquisition process. Objective AoA estimates are often limited to words whose referents can be shown in pictures. We created a corpus-derived AoA estimate based on first word occurrences in a longitudinal corpus of child English, and evaluated its reliability and validity against other measures of AoA. Then we used these different measures as concurrent predictors of adult lexical decision times. Our results showed adequate reliability and good relations with other AoA measures, especially with parent-reported AoA (r = 0.56). Corpus AoA did not predict unique variance in lexical decision times, while adult AoA ratings and parent-reported AoA did. We argue that this pattern is due to two factors. First, the adult AoA ratings and parent-reported AoA are confounded with adult memory, lexical processing and reading difficulty variables. Second, the adult AoA ratings are related to actual age of acquisition only for words acquired during later preschool and school age. Our analyses support the utility of corpus-derived AoA estimates as an objective measure of acquisition age, especially for early-acquired words.

Language statistics as a window into mental representations

Large-scale linguistic data is nowadays available in abundance. Using this source of data, previous research has identified redundancies between the statistical structure of natural language and properties of the (physical) world we live in. For example, it has been shown that we can gauge city sizes by analyzing their respective word frequencies in corpora. However, since natural language is always produced by human speakers, we point out that such redundancies can only come about indirectly and should necessarily be restricted cases where human representations largely retain characteristics of the physical world. To demonstrate this, we examine the statistical occurrence of words referring to body parts in very different languages, covering nearly 4 billions of native speakers. This is because the convergence between language and physical properties of the stimuli clearly breaks down for the human body (i.e., more relevant and functional body parts are not necessarily larger in size). Our findings indicate that the human body as extracted from language does not retain its actual physical proportions; instead, it resembles the distorted human-like figure known as the sensory homunculus, whose form depicts the amount of cortical area dedicated to sensorimotor functions of each body part (and, thus, their relative functional relevance). This demonstrates that the surface-level statistical structure of language opens a window into how humans represent the world they live in, rather than into the world itself.

New bag-of-feature for histopathology image classification using reinforced cat swarm algorithm and weighted Gaussian mixture modelling

The progress in digital histopathology for computer-aided diagnosis leads to advancement in automated histopathological image classification system. However, heterogeneity and complexity in structural background make it a challenging process. Therefore, this paper introduces robust and reliable new bag-of-feature framework. The optimal visual words are obtained by applying proposed reinforcement cat swarm optimization algorithm. Moreover, the frequency of occurrence of each visual words is depicted through histogram using new weighted Gaussian mixture modelling method. Reinforcement cat swarm optimization algorithm is evaluated on the IEEE CEC 2017 benchmark function problems and compared with other state-of-the-art algorithms. Moreover, statistical test analysis is done on acquired mean and the best fitness values from benchmark functions. The proposed classification model effectively identifies and classifies the different categories of histopathological images. Furthermore, the comparative experimental result analysis of proposed reinforcement cat swarm optimization-based bag-of-feature is performed on standard quality metrics measures. The observation states that reinforcement cat swarm optimization-based bag-of-feature outperforms the other methods and provides promising results.

Heart Rate Fragmentation in Rats with Streptozotocin‐induced Diabetes

Heart rate fragmentation (HRF) is a recently proposed approach to evaluate sino‐atrial instability characterized by the presence of numerous inflections points in a series of successive values of RR intervals (RRi). HRF has been shown to be increased in aging and coronary diseases and seems to be linked to the risk of life‐threatening cardiovascular events and death. We recently demonstrated that cardiac autonomic modulation in involved in HRF in rats. Moreover, HRF is increased in a rat model of heart failure. Diabetes mellitus (DM) markedly alters HRV indices and might affect HRF as well. Therefore, we hypothesize that HRF is altered in streptozotocin‐ (STZ) induced diabetic rats. Male Sprague‐Dawley rats were injected with STZ (50 mg/kg), or vehicle (citrate buffer) into penile vein. Development of diabetes was confirmed 72h after STZ by the presence of hyperglycemia (> 350 mg/dL). Electrocardiographic (ECG) recordings were performed in conscious rats, 7 days (acute) or 4 weeks (chronic) after STZ or vehicle administration. Series of RR intervals were generated and processed by the customized software PyBios as follows: RRi values were symbolized as “‐1”, “0,” or “1” when the differences between successive RRi were negative, null, or positive, respectively. Transitions between symbols “‐1” and “1” were labeled as “hard” (H) inflection points, while those between “‐1” (or “1”) and “0” were labeled as “soft” (S) inflection points. The percentage of inflection points (PIP) was quantified, as well as sequences of 4 consecutive symbols, so‐called “words”, computing the occurrence of words with zero (W0), one (W1), two (W2), or three (W3) inflections points. The occurrence of words with only hard (WH) or only soft transitions (WS) were also quantified. Both groups of diabetic rats were bradycardic as compared to their control counterparts. Data of HRF are shown in the table below. Occurrences (%, mean ± SEM) of PIP, words with 0 to 3 inflection points, and words with only soft or hard transitions of symbols. As hypothesized, diabetes affects HRF, increasing fragmented indices (PIP, W3 and WH) and decreasing fluent ones (W1 and WS). Moreover, our results showed that changes in HRF due to diabetes in this model is time‐dependent. In addition to traditional methods of analyzing heart rate variability, HRF is a new method that can contribute to the diagnosis and prognosis of cardiovascular diseases in diabetic patients. Further studies are necessary to understand better the mechanisms involved in our findings.

Heart Rate Fragmentation in Patients with Type 2 Diabetes Mellitus

Heart rate fragmentation (HRF) is a recently proposed approach to evaluate ultra‐rapid variability of heart rate. HRF has been shown to be increased in the aging process and in conditions such as coronary artery disease. Moreover, it proved to be a strong biomarker of adverse cardiovascular events, mortality and was an independent predictor of atrial fibrillation. Type 2 diabetes mellitus (DM) is a disease known to affect heart rate variability (HRV) indices markedly; however, HRF in diabetic patients is to be studied. We aimed to evaluate HRF in patients with DM, without autonomic cardiovascular neuropathy, compared to their healthy counterparts. Electrocardiographic (ECG) recordings collected from patients with diabetes (n=102) and healthy subjects (n=96), aged 44 to 59 years, were retrospectively analyzed from the Cardiovascular Physical Therapy Laboratory database. ECG were recorded at rest (supine) for 10 to 15 minutes. RR interval (RRi) series were processed by the customized software PyBios. RRi values were symbolized as “‐1”, “0,” or “1” when the differences between successive RRi were negative, null, or positive, respectively. The percentage of inflection points (PI) was quantified and sequences of 4 consecutive symbols, so‐called “words”, were analyzed computing the occurrence of words with zero (W0), one (W1), two (W2), or three (W3) inflections points. The occurrence of words with only hard (‐1 to 1: WH), soft (‐1 to 0 or 0 to 1: WS), or mixed (WM) (i.e., both H and S) transitions were also quantified. Patients with diabetes presented higher PIP (58±1 vs 56±1, p=0.033), W3(17±1 vs14±1, p<0.001), WS(25±2 vs16±1, p<0.001) and WM(22±1 vs 17±1, p<0.001). On the other hand, patients with diabetes showed a lower percentage of W1 (31±1 vs 35±1, p=0.024) and WH (47±2 vs 61±2, p<0.001). The occurrence of W0 and W2 were similar between groups. In conclusion, diabetics patients without autonomic cardiovascular neuropathy have augmented fragmentation of heart rate as compared to healthy subjects. Clinical applicability: In the future, studies with this approach might be useful to assess heart rate dynamic abnormalities in order to detect cardiac risks in patients with diabetes mellitus.

Coping with systemic lupus erythematosus in patients' words.

ObjectivePrevious research on coping strategies of patients with SLE showed that there are no absolute adaptive or maladaptive strategies and that the range of potential coping strategies is large and heterogeneous. In this paper, we aimed to identify, in a large sample of patients with SLE (N=3222), the most frequent words used by patients to describe their coping strategies, to group them into significant themes and to test their possible association with specific patient characteristics.MethodsOur analyses were based on the data set of the European survey ‘Living with Lupus in 2020’ (N=3222). Through the T-LAB software, we analysed the answers that adult participants gave to an open-ended question about how they cope with the disease. We identified the most frequent words, and with hierarchical cluster analysis we grouped them into semantic clusters (ie, themes) that were characterised by specific patterns of words. Finally, we tested the possible association between clusters and illustrative variables (sociodemographics, disease characteristics, quality of life).ResultsFive coping strategies were identified, each of them constituting an important percentage of the total word occurrences: positive attitude (22.58%), social support (25.46%), medical treatments (10.77%), healthy habits (20.74%) and avoid stress (20.45%). Each strategy was statistically associated with specific patient characteristics, such as age and organ involvement.ConclusionsLearning to adapt to a lifetime of having SLE may require replacing old coping strategies with more effective ones. Investigating patients’ coping strategies in relation to different patient characteristics represents a useful starting point for developing more targeted and efficacious interventions.

Multi-class Classification of Twitter Sentiments using Frequency based, LSTM and BERT Methods

Abstract: Twitter is one of the most popular social media applications used by people of diverse age groups. Tweet has become an important mode of communication. Tweets are unstructured and often consist of different kinds of data tokens like mentions, hash tags, emoticons etc. Analysing the sentiment of this kind of unstructured data is challenging. In this paper, the experimentation of multiclass twitter sentiment analysis is presented, using three different approaches. Starting with the simple approach of frequency based feature extraction method, that helps capture the features from a text document by counting the frequency of the occurrence of words, to more advanced approaches like LSTM and BERT have been used. Different preprocessing techniques and different classifiers are explored in this paper. The performance of all the three methods is evaluated using metrics (precision, recall and f1 score). At the end the results of all three approaches are discussed and compared against one another. Keywords: NLP, BERT, LSTM, MLP Classifier, Multiclass Classification, Embeddings, Tweets.

FINANCIAL CITIZENSHIP AND FINANCIAL CONSUMER PROTECTION: A SYSTEMATIC LITERATURE REVIEW

Using the Web of Science and Scopus databases, we conducted a systematic literature review to contribute to studies that broaden the discussions on financial consumer protection and analyze this financial citizenship pillar. Based on the Tranfield, Denyer, and Smart (2003) protocol, a database composed of 20 articles was analyzed, aiming to synthesize the research corpus through relationship networks, journal reputation, and the occurrence of words. The results point to a field of study still considered embryonic due to the low number of articles published in indexed databases and because the studies are still paying attention to the effects of the financial crisis of 2008, as well as to the corresponding regulations of this crisis. Also, there is an unimpressive relationship between the authors who study the subject. It is challenging to identify research centers and journals specialized in financial consumer protection studies or subjects about financial citizenship. We hoped that this study could serve as a basis to position researchers and governments on the stage of development of high-impact research and that it could promote the international research agenda on the topic.

Multiple regression techniques for modelling dates of first performances of Shakespeare-era plays

The creation of new computational methods to provide fresh insights on literary styles is a hot topic of research. There are particular challenges when the number of samples is small in comparison with the number of variables. One problem of interest to literary historians is the date of the first performance of a play of Shakespeare’s time. Currently this must usually be guessed with reference to multiple indirect external sources, or to some aspect of the content or style of the play. This paper highlights a dating technique with a wider potential, using this particular problem as a case study.In this contribution, we introduce a novel dataset of Shakespeare-era plays (181 plays from the period 1585–1610), annotated by the best-guess dates for them from a standard reference work as metadata. We introduce a memetic algorithm-based Continued Fraction Regression (CFR) which delivered models using a small number of variables, leading to an interpretable model and reduced dimensionality, applied for the first time here in a problem of computational stylistics.Our independent variables are the probabilities of occurrences of individual words in each one of the plays. We studied the performance of 11 widely used regression methods to predict the dates of the plays at an 80/20 training/test split. An in-depth analysis of the most commonly occurring 20 words in the CFR models in 100 independent runs helps explain the trends in linguistic and stylistic terms. The use of the CFR has helped us to reveal an interesting mathematical model that links the variation in the use of the words through time, which helps to provide estimates of the dates of plays of the Shakespeare-era. We check for genre effects as a possible confounding variable.

Depressive Language on a Virtual Common: A Critical Discourse Analysis

Language use is important in a community such as composed of people living with mental health conditions. The researcher aimed to understand depressive language more deeply by looking at the linguistic features, unravelling the process of language consumption as evidenced in the language experiences of depressed people, and determining the dominant ideological representations. Using the 3-Dimensional Framework of Critical Discourse Analysis, the researcher made a thorough investigation on depressive language on Facebook. In the textual analysis, it was revealed that depressive language contains certain linguistic features such as lexical items belonging to different categories, use of self-references, presence of positive and emotions words, and occurrence of words related to status, dominance, and social hierarchy. In the interview with seven depressed people, language experiences include the use of language for various purposes such as for expressing, fighting stigma, getting help, and educating other people. The participants also shared that language could be a reason for triggers and stigma. Based on the linguistic features and the language experiences, it was also found out that certain ideologies are dominant in depressive language such as the existence of stigma in society, death as an escape from pain, inevitability of sadness, and continuous seeking of help and attention. This study is deemed important in advancing more studies in the Philippine context focusing on mental health and language use.

On Ransomware Family Attribution Using Pre-Attack Paranoia Activities

Ransomware attacks are among the most disruptive cyber threats, causing significant financial losses while impacting productivity, accessibility, and reputation. Despite their end goals (encryption/locking), ransomware are often designed to evade detection by executing a series of pre-attack API calls, namely “paranoia” activities, for determining a suitable execution environment. In this work, we present a first-of-a-kind effort to utilize such paranoia activities for characterizing ransomware distinguishable behaviors. To this end, we draw-upon more than 3K samples from recent/prominent ransomware families to fingerprint their uniquely leveraged paranoia activities. Specifically, by leveraging techniques rooted in Natural Language Processing (NLP) such as Occurrence of Words (OoW), we model ransomware-generated evasion API calls while tailoring various machine and deep learning algorithms to perform ransomware classification. The thoroughly conducted evaluations demonstrate the effectiveness of the implemented approach, with the Random Forest (RF) and OoW techniques producing an optimal classification accuracy (94.92%). The insights/findings from this work not only shed light on contemporary ransomware-specific evasion methods, but also (i) indicates that such tactics could be employed effectively as features for ransomware family attribution while (ii) laying the foundation for implementing proactive and portable countermeasures for further ransomware attack detection/mitigation by solely utilizing ransomware-generated paranoia activities.

Pupillary entrainment reveals individual differences in cue weighting in 9-month-old German-learning infants

Young infants can segment continuous speech with statistical as well as prosodic cues. Understanding how these cues interact can be informative about how infants solve the segmentation problem. Here we investigate how German-speaking adults and 9-month-old German-learning infants weigh statistical and prosodic cues when segmenting continuous speech. We measured participants' pupil size while they were familiarized with a continuous speech stream where prosodic cues were pitted off against transitional probabilities. Adult participants' changes in pupil size synchronized with the occurrence of prosodic words during the familiarization and the temporal alignment of these pupillary changes was predictive of adult participants' performance at test. Further, 9-month-olds as a group failed to consistently segment the familiarization stream with prosodic or statistical cues. However, the variability in temporal alignment of the pupillary changes at word frequency showed that prosodic and statistical cues compete for dominance when segmenting continuous speech. A follow-up language development questionnaire at 40 months of age suggested that infants who entrained to prosodic words performed better on a vocabulary task and those infants who relied more on statistical cues performed better on grammatical tasks. Together these results suggest that statistics and prosody may serve different roles in speech segmentation in infancy.

A quantitative study of policy-driven changes and forecasts in the development of the hydrogen transportation industry

Through data mining and analysis of the word frequency and occurrence position of industrial policy keywords, the main policy parameters affecting industrial development are determined, and the functional relationship between industrial policy and industrial development is obtained through multi-parameter non-linear regression: Yit−1y1,y2,y3,y4,y5=β1itX1+β2itlnX2+β3itlnX3+β4itX1it∗lnX3+ɛit. The time series function of the industrial development index: Y(t)=0.174∗e(0.256∗t) is established and the industrial development under the influence of next year’s policy is predicted. It is concluded from the mathematical expression of the statistical model that there is a certain coupling effect between different policies, and that industrial development is influenced by the joint effect on the parent and sub-industries. This ultimately proves that there is a clear correlation between policy and industry development.

A stochastic model of word occurrences in hierarchically structured written texts

In previous studies, we have treated real written texts as time series data and have tried to investigate dynamic correlations of word occurrences by utilizing autocorrelation functions (ACFs) and also by simulation of pseudo-text synthesis. The results showed that words that appear in written texts can be classified into two groups: a group of words showing dynamic correlations (Type-I words), and a group of words showing no dynamic correlations (Type-II words). In this study, we investigate the characteristics of these two types of words in terms of their waiting time distributions (WTDs) of word occurrences. The results for Type-II words show that the stochastic processes that govern generating Type-II words are superpositions of Poisson point processes with various rate constants. We further propose a model of WTDs for Type-I words in which the hierarchical structure of written texts is considered. The WTDs of Type-I words in real written texts agree well with the predictions of the proposed model, indicating that the hierarchical structure of written texts is important for generating long-range dynamic correlations of words.

Study of english borrowing word in indonesian as found in habibie & ainun movie

This research concerns with borrowing words in Indonesian as found in the movie. This research aims at (1) to identify the types of borrowing word; and (2) to identify the adaptations of borrowing words used in Habibie & Ainun movie. The method used in this research is descriptive qualitative for analyzing the data. This study used the theory of Haugen in (Grosjean 1982) that was the type and the adaptation of borrowing words. The results showed that there were 2 types of borrowing words found in the data source namely loanword and loan shift. For the adaptation process, there are 2 kinds of adaptation processes found here, namely phonological adaptation and morphological adaptation. The occurrence of pure loan words was dominant compared to the other types. Borrowing words are often found in online and print media such as television, newspapers, magazines, internet, etc. Borrowing words are analyzed in this study to provide knowledge about the existence of word borrowing in Indonesian. The Indonesian borrows many words in English, of course increasing the number of vocabularies in Indonesian.

Wpływ pandemii na polską, bułgarską i czeską leksykologię w roku 2020 i na początku 2021

Przedmiotem uwagi są szybko następujące podczas pandemii konsekwencje językowe – leksykalne (nowe słowa, zwielokrotniona częstotliwość użycia istniejących słów) i komunikacyjne, stanowiące dla językoznawców ciekawą podstawę do badań współczesnych języków, z których trzy: polski, bułgarski, czeski stały się fundamentem analizy i badań frekwencyjnych. Mimo że omawiane procesy językowe mają charakter globalny, to tendencje leksykalne w poszczególnych językach, jak i funkcje mowy realizowane są w różny sposób, z heterogenicznym natężeniem, dlatego warto przyjrzeć im się bliżej. Prezentowany w artykule materiał został wyekscerpowany z dostępnych w sieci i na bieżąco notujących jednostki języka źródeł. Były nimi korpusy językowe: Narodowy korpus języka polskiego, Bułgarski narodowy korpus, Czeski narodowy korpus, autorski Polsko-bułgarsko-rosyjski korpus równoległy, którego autorka artykułu jest współautorką, i przeglądarki typu: frazeo.pl czy slowanaczasie.pl oraz słowniki elektroniczne, na przykład miejski.pl. Pomocne stały się polskie, bułgarskie i czeskie programy publicystyczne i informacyjne, a także najmłodsza, choć nieliczna, literatura naukowa.

The indication of creativity and its effect on the probability of success of crowdfunding projects

The growing importance of crowdfunding as a fundraising option alongside traditional forms of financing resulted in an emerging stream of literature that examines the mechanisms for increasing the probability of success of crowdfunding propositions. A significant part of this literature focuses on the examination of signals that entrepreneurs can provide to reduce information asymmetries. We extend this research by investigating the proactive communication of the creativity within crowdfunding projects as a potential signal for success. Using 39,718 campaigns from Kickstarter, we investigate the occurrence of words associated with creativity and the resulting influence on the probability of success. Our results show that the proactive communication of the creativity of the projects can have a positive influence on the probability of a successful fundraising campaign. We also provide evidence that the communication of creativity can be influenced by other signals, depending on the context in which these signals are used.

Authorship Behavior in the Articles Appeared in Library Philosophy and Practice

This article presents a study about the authorship behavior and the collaboration pattern for the articles that appeared in the journal Library Philosophy and Practice during 2011-2016. The Zipf’s law of word occurrence and Lotka’s law are tested for this research. Further, the collaboration index, degree of collaboration, average paper per author, average author per paper, national and international collaboration is investigated for the period of study.

Sentiment Analysis of Public Transportation Services on Twitter Social Media Using the Method Naïve Bayes Classifier

Public transportation services in Indonesia, especially Jabodetabek, have used social media, especially Twitter, as a way to improve services. Currently, the use of online transportation services is like a need; it is necessary to conduct a sentiment analysis of online transportation to find out how people respond to these online transportation services. This research was made to analyze community responses with data analysis in the form of tweets that filtered with a public transportation-related keyword then classified into positive and negative classes using the Naïve Bayes Classifier method. Based on the system built, the total sentiment results for the percentage of the occurrence of positive words were 0.507843137, and the sentiment results for the percentage of negative word occurrences were 1.4132493. The results show that the level of negative sentiment from public tweets is greater than the level of positive sentiment.

(Non)pandemic Youth Speak. On Lexical-Semantic Tendencies in the Polish Language of the Youth

This article presents the latest lexical tendencies in the language of contemporary Polish youth. The directions of the dominance of certain meanings were analysed on the basis of the submissions for the Youth Word of the Year contest (2020), as well as the online slang dictionary miejski.pl. The data obtained comes from natural users of the language and is based on their linguistic awareness and intuition. Dominant semantic fields were distinguished, namely human, interpersonal relationships, attitudes towards life, cultural preferences, etc. Coining new terms in these areas is accompanied by expressiveness, humour, and playing with language norms. Despite the occurrence of new words connected with the Covid-19 pandemic, they have not been widely represented among those lexical and semantic units considered interesting and worth mentioning.