Textual Indicators Research Articles

Text and Sound-Based Feature Extraction and Speech Emotion Classification for Korean

Embracing the complexities of human emotions conveyed through speech, this study ventures into Speech Emotion Recognition (SER) within the human-computer interaction domain, leveraging cutting-edge artificial intelligence technologies. Focusing on the auditory attributes of speech, such as tone, pitch, and rhythm, the research introduces an innovative approach that amalgamates deep learning techniques with the A Learnable Frontend for Audio Classification (LEAF) algorithm and wav2vec 2.0 pre-trained on a large corpus, specifically targeting Korean voice samples. This methodology underlines the capacity of these technologies to process and decipher complex vocal expressions, aiming to elevate emotion classification precision notably. The exploration extends the horizons of SER by accentuating auditory emotion cues and aspires to enrich machine interactions to be more intuitive and empathetic across various applications like healthcare and customer service. The outcomes underscore the efficacy of transformer-based models, particularly wav2vec 2.0 and LEAF, in capturing the subtle emotional states expressed in speech, thereby affirming the importance of auditory cues over conventional visual and textual indicators. The study's implications for further research herald a promising trajectory for evolving AI systems adept at nuanced emotion detection, thereby forging pathways toward more natural and human-centric interactions between individuals and machines. This advancement is crucial for developing empathetic AI that can seamlessly integrate into our daily lives, understanding and reacting to human emotions in a way that mirrors human understanding and compassion.

Integrating Financial and Textual Indicators for Enhanced Financial Risk Prediction: A Deep Learning Approach

The study evaluates the effectiveness of financial indicators in financial risk prediction and develops a framework using financial and textual data. It emphasises the importance of both data types in risk assessment and prioritises liquidity and industry specific metrics. The analysis of the existing literature affirmed the significance of both data types in risk assessment. The findings of the study revealed a strong correlation between financial and textual indicators. The selection of deep learning was based on its adeptness in handling diverse unstructured data, justifying its application. This innovative methodology enhances financial risk prediction and supports strategic decision-making.

An Algorithm for Finding Optimal k-Core in Attribute Networks

As a structural indicator of dense subgraphs, k-core has been widely used in community search due to its concise and efficient calculation. Many community search algorithms have been expanded on the basis of k-core. However, relevant algorithms often set k values based on empirical analysis of datasets or require users to input manually. Once users are not familiar with the graph network structure, they may miss the optimal solution due to an improper k setting. Especially in attribute social networks, characterizing communities with only k-cores may lead to a lack of semantic interpretability of communities. Consequently, this article proposes a method for identifying the optimal k-core with the greatest attribute score in the attribute social network as the target community. The difficulty of the problem is that the query needs to integrate both structural and textual indicators of the community while fully considering the diversity of attribute scoring functions. To effectively reduce computational costs, we incorporate the topological characteristics of the k-core and the attribute characteristics of entities to construct a hierarchical forest. It is worth noting that we name tree nodes in a way similar to pre-order traversal and can maintain the order of all tree nodes during the forest creation process. In such an attribute forest, it is possible to quickly locate the initial solution containing all query vertices and reuse intermediate results during the process of expanding queries. We conducted effectiveness and performance experiments on multiple real datasets. As the results show, attribute scoring functions are not monotonic, and the algorithm proposed in this paper can avoid scores falling into local optima. With the help of the attribute k-core forest, the actual query time of the Advanced algorithm has improved by two orders of magnitude compared to the BaseLine algorithm. In addition, the average F1 score of our target community has increased by 2.04 times and 26.57% compared to ACQ and SFEG, respectively.

Class‐imbalanced financial distress prediction with machine learning: Incorporating financial, management, textual, and social responsibility features into index system

AbstractFinancial distress prediction (FDP) is centrally imported to reduce potential losses of companies and investors. This paper combines social responsibility indicators with financial, management, and textual indicators to construct a multi‐dimensional FDP index system. To increase prediction accuracy, the difference in the number of samples between special treatment and health companies is actively considered, and the synthetic minority oversampling technique is adopted to deal with class‐imbalanced datasets. Moreover, a feature extraction method based on the genetic algorithm is employed to select features as input of machine learning models for predicting financial distress in Chinese listed companies. Experimental results show that ensemble classifiers are the better choice for predicting financial distress, of which gradient boosting decision tree outperforms other classifiers. Textual indicators play the most significant role in complementing traditional financial indicators of FDP, and their financial distress signals emerge earlier compared with management and social responsibility indicators.

Using Newspapers for Textual Indicators: Guidance Based on Spanish- and Portuguese-Speaking Countries

Using Newspapers for Textual Indicators: Guidance Based on Spanish- and Portuguese-Speaking Countries

Metaphorical Indicators of the Hyperthemes of “Dune: Part One” (2021): A Multimodal Cognitive-Linguistic Case Study

In Denis Villeneuve’s Dune: Part One, the 2021 film adaptation of Frank Herbert’s seminal science-fiction novel from 1965, the general themes, or “hyperthemes”, of the fi lmic story are often represented metaphorically, by means of more or less transparent textual indicators. The present article explores the metaphorical indicators of three hyperthemes of Villeneuve’s film—(1) “The power hierarchy Spacing Guild > Imperium > Great Houses > Fremen”, (2) “The feuding Great Houses: House Atreides and House Harkonnen”, and (3) “The colonization of Arrakis by the Imperium and Great Houses”—in an analytical framework combining the tenets and tools of multimodal analysis and cognitive linguistics. Specifically, the article explores (1) the relations between the non-metaphorical and metaphorical indicators of the hyperthemes of Dune: Part One, (2) the relations between different metaphorical indicators of the same hyperthemes of the film, (3) the relations between the verbal and non-verbal metaphorical indicators of the film’s hyperthemes, and (4) the variation among the metaphors sanctioning the hyperthematic indicators with regard to their interpretability.

Pistis Christou als open plek. Is er een hermeneutische uitweg uit de exegetische impasse over de betekenis van pistis Christou?

The debate about the meaning of the Pauline phrase pistis Christou focuses on the syntax. If the genitive case is subjective then it is rendered “faith(fulness) of Christ”, if objective, then “faith in Christ”. This article examines the occurrence of pistis Christou in Philippians 3:9 from a reader-response perspective to determine if the difference between the subjective and the objective genitive reading could have led to a different understanding by the Philippians. The phrase and meaning of pistis Christou will be treated as an open space in the text, which the first readers could fill by following the letter’s authorial instructions and textual indications. The article concludes that a reader-response approach does not provide a way out of the exegetical debate on the meaning of pistis Christou, but that it does shift the reader’s focus from the grammar to the person of Christ.

Textual Indicators of Deliberative Dialogue: A Systematic Review of Methods for Studying the Quality of Online Dialogues

High-quality online dialogues help sustain democracy. Deliberative theory, which predates the internet, provides the primary model for assessing the quality of online dialogues. It conceptualizes high-quality online dialogue as civil, rational, constructive, equal, interactive, and for the common good. More recently, advances in computation have driven an upsurge of empirical studies using automated methods for operationalizing online dialogue and measuring its quality. While related in their aims, deliberative theory and the wider empirical literature generally operate independently. To bridge the gap between the two literatures, we introduce Textual Indicators of Deliberative Dialogue (TIDDs). TIDDs are defined as text-based measures of online dialogue quality under a deliberative model (e.g., disagreement, incivility, and justifications). In this study, we identified 123 TIDDs by systematically reviewing 67 empirical studies of online dialogue. We found them to have mid-low reliability, low criterion validity, and high construct validity for measuring two deliberative dimensions (civility and rationality). Our results highlight the limitations of deliberative theory for conceptualizing the variety of ways online dialogues can be operationalized. We report the most promising TIDDs for measuring the quality of online dialogue and suggest deliberative theory would benefit from altering its models in line with the broader empirical literature.

Analyzing gender clues in war-time letters

Abstract Many historians struggle with their information needs which cannot be directly served by the information access systems. Satisfying these needs often requires reasoning and interpretation of pieces of information in context, from user-specific viewpoints. One common need in studying historical phenomena is what indicates gender in historical text. We call such textual indicators ‘gender clues’ because they help satisfy information needs regarding the concept of gender. In this article, we analyze gender clues qualitatively and present a typology of them based on a set of private letters from the Second World War in Finland. We also discuss the general need to create metadata to support the historian’s explorations from specific viewpoints, especially in small and noisy collections that are common in the historical domain.

Measuring political and economic uncertainty: a supervised computational linguistic approach.

In this paper, we develop a computational linguistic approach based on supervised machine learning using the People’s Daily to measure Chinese official relations and political uncertainty towards the US. In the first step, we create training samples by asking experts to manually annotate news articles. In the second step, we use supervised machine learning algorithms to adjust our single neural network and support vector machine classifiers to better fit our training data. Finally, we combine our two individual classifiers and a dictionary approach to automatically detect whether an article in the newspaper sample is relevant. Using all of the relevant textual data, we then apply the computational linguistic approach to generate state-of-the-art indices and show that our indices outperform similar current textual indicators in some situations, particularly in the financial market.Supplementary InformationThe online version contains supplementary material available at 10.1007/s43546-022-00209-2.

Using Newspapers for Textual Indicators: Which and How Many?

Using Newspapers for Textual Indicators: Which and How Many?

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The aim of the article was to substantiate the cognitive foundations of critical thinking that provide recognition of manipulative technologies. The substantiation of cognitive foundations of critical thinking, aimed at recognition of manipulative technologies, is carried out on the basis of available scientific achievements in the field of general matters of critical thinking, which are projected on the sphere of manipulative influence. The paper gives the definition of cognitive foundations that include knowledge, skills and abilities and personal attributes required for effective intellectual analysis. The following types of cognitive foundations of critical thinking are modeled and substantiated in the article: 1) knowledge; 2) skills and abilities; 3) personality traits that are actualized in the process of intellectual activity (intellectual traits). The first type of cognitive foundations are both awareness of the social context and awareness of the generally accepted value priorities of civilized society. Cognitive foundations of the second type include 10 skills, namely: finding of logical contradictions and errors; lie recognition; distinguishing between real and fictional facts; distinguishing between correct and erroneous statements; mastery of general scientific methods of analysis; establishment of the source base; argumentation; refutation of misconceptions; recognition of indicators of manipulative influence; modeling of solution options. Cognitive supports of the third type are as follows: observation, ability to perform analytical operations. The article demonstrates how skills and abilities are used in detection of textual and non-textual indicators of manipulation. Textual indicators include those that are contained in the very text of the message (logical contradictions, ambiguity of the statement, violation of the clarity of information and its completeness). Non-textual indicators are those for the recognition of which it is necessary to correlate the narrative with real events, with the logic of the relationship of events and facts as well as with ideological attitudes of the authors of the messages.

A New Demotic Horoscope from Medinet Habu

The relatively small set of Demotic astrological texts from Medinet Habu may be increased by a newly identified horoscope recorded on an ostracon held at the Cairo Museum. Despite the lack of pertinent data in the official records, the origin of this sherd seems to be certain thanks to some telling palaeographical and textual indications. The date recorded on this potsherd indicates the late first century AD and refers explicitly to the Alexandrian calendar. With regard to the content, it preserves the general format and layout of the Demotic horoscopes on ostraca from Medinet Habu, albeit with some small differences in the type and arrangement of the data. This horoscope leaves out some astronomical data common to other Medinet Habu horoscopes which, along with some other Theban horoscopic ostraca, might confirm a trend among Theban astrologers going from more detailed and complicated compositions to less elaborate and more simplified ones.

Integrating Deep and Shallow Models for Multi-Modal Depression Analysis—Hybrid Architectures

At present, although great progress has been made in automatic depression assessment, most of the recent works only concern the audio and video paralinguistic information, rather than the linguistic information from the spoken content. In this work, we argue that beside developing good audio and video features, to build reliable depression detection systems, text-based content features are also of importance to analyse depression-related textual indicators. Furthermore, to improve the performance of automatic depression assessment systems, powerful models, capable of modelling the characteristics of depression embedded in the audio, visual and text descriptors, are also required. This paper proposes new text and video features and hybridizes deep and shallow models for depression estimation and classification from audio, video and text descriptors. The proposed hybrid framework consists of three main parts: 1) A Deep Convolutional Neural Network (DCNN) and Deep Neural Network (DNN) based audio-visual multi-modal depression recognition model for estimating the Patient Health Questionnaire depression scale (PHQ-8); 2) A Paragraph Vector (PV) and Support Vector Machine (SVM) based model for inferring the physical and mental conditions of the individual from the transcripts of the interview; 3) A Random Forest (RF) model for depression classification from the estimated PHQ-8 score and the inferred conditions of the individual. In the PV-SVM model, PV embedding is used to obtain fixed-length feature vectors from transcripts of the answers to the questions associated with psychoanalytic aspects of depression, which are subsequently fed into the SVM classifiers for detecting the presence/absence of the considered psychoanalytic symptoms. To our best knowledge, this approach is the first attempt to apply PV for depression analysis. Besides, we propose a new visual descriptor - Histogram of Displacement Range (HDR) to characterize the displacement and velocity of the facial landmarks in the video segment. Experiments have been carried out on the Audio Visual Emotion Challenge (AVEC2016) depression dataset, they demonstrate that: 1) The proposed hybrid framework effectively improves the accuracies of both depression estimation and depression classification, with an average F1 measure up to 0.746, which is higher than the best result (0.724) of the depression sub-challenge of AVEC2016. 2) HDR obtains better depression recognition performance than Bag-of-Words (BoW) and Motion History Histogram (MHH) features.

La construcción estratégica del discurso en comunicación

El proceso de comunicación se ha descrito con atención a sus componentes: actores, expresiones, representaciones e instrumentos. Esta perspectiva teórica supone una visión estratégica del proceso de la comunicación social, en tanto los discursos resultantes deben ser observados en la interrelación de los indicadores textuales y de los aspectos relativos al co(n) texto del discurso. A la par, sería oportuno asumirlo en la instancia de emisión, en la que se aprecie lo estratégico desde la propia construcción discursiva: construcción estratégica del discurso. Mediante la metodología de análisis estratégico de la construcción del discurso, el artículo analiza un discurso de la Oficina Nacional Tributaria (ONAT) acerca del pago de impuestos en Cuba. Este discurso fundamenta sus estrategias discursivas en la finalidad de convertir a los contribuyentes en parte activa del desarrollo social cubano; así como afianzar su imagen pública como entidad para el bien social.

Interactive tool for learning propagation in single‐mode optical fibers in telecommunication engineering

AbstractSingle‐mode optical fiber is one of the most extended wired transmission media providing broadband high‐speed communications all over the world whose performance highly depends on chromatic dispersion and modulation's effects. The motivation of this study is to offer a graphical analyzer of the propagation of Gaussian and super‐Gaussian light pulses directly modulated in a single‐mode optical fiber subject to chromatic dispersion of first and second‐order. The abstract nature of this electromagnetic phenomenon makes any tool that helps learners of optical fiber its understanding a useful resource. Through interactive learning, it is intended to facilitate the comprehension of the behavior of the fiber and feature its capacity in terms of bandwidth and length depending on different parameters and conditions. Also, the study and recreation of directly modulated pulses propagating in the dispersive fiber cannot only be used to teach the students how to avoid its negative impact but to use it as a mechanism to improve the properties of the whole optical communications system. The proposed analyzer consists of a MATLAB graphical user interface integrating the visualization of pulses in time, frequency and carrier's behavior together with additional graphs to interpret and correct chirp and numerical and textual indicators, menus and dialogs to understand the final outcome.

Oral Aspects: A Performative Approach to 1QM

AbstractAs the product of a textual community imbedded in an oral culture, the War Scroll can be rewardingly approached as a composition intended for a community of hearers. Indeed, this article demonstrates that 1QM retained an orally fluid textuality and preserves a variety of textual indicators of performativity: hints of oral engagement, accumulation of imitable practices, and reliance on rhetorical techniques suited to the ear. In examining the performative potentials in the War Scroll’s prescriptive (cols. 1–9), prayer (cols. 10–14), and dramatic (cols. 15–19) material, I argue that 1QM can be understood as a spoken text, one which lends itself to performance and embodiment.

A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health records

BackgroundDisease prediction based on Electronic Health Records (EHR) has become one hot research topic in biomedical community. Existing work mainly focuses on the prediction of one target disease, and little work is proposed for multiple associated diseases prediction. Meanwhile, a piece of EHR usually contains two main information: the textual description and physical indicators. However, existing work largely adopts statistical models with discrete features from numerical physical indicators in EHR, and fails to make full use of textual description information.MethodsIn this paper, we study the problem of kidney disease prediction in hypertension patients by using neural network model. Specifically, we first model the prediction problem as a binary classification task. Then we propose a hybrid neural network which incorporates Bidirectional Long Short-Term Memory (BiLSTM) and Autoencoder networks to fully capture the information in EHR.ResultsWe construct a dataset based on a large number of raw EHR data. The dataset consists of totally 35,332 records from hypertension patients. Experimental results show that the proposed neural model achieves 89.7% accuracy for the task.ConclusionsA hybrid neural network model was presented. Based on the constructed dataset, the comparison results of different models demonstrated the effectiveness of the proposed neural model. The proposed model outperformed traditional statistical models with discrete features and neural baseline systems.

Paraphrasing tools, language translation tools and plagiarism: an exploratory study

In a recent unit of study in an undergraduate Health Sciences pathway course, we identified a set of essays which exhibited similarity of content but demonstrated the use of bizarre and unidiomatic language. One of the distinct features of the essays was the inclusion of unusual synonyms in place of expected standard medical terminology.We suspected the use of online paraphrasing tools, but were also interested in investigating the possibility of the use of online language translation tools. In order to test the outputs of these tools, we used as a seed document a corpus of text which had been provided to the students as prompt for the essay. This document was put through six free online paraphrasing tools and six separate iterative language translations through the online Google Translate™ tool.The results demonstrated that free online paraphrasing tools did not identify medical terminology as standardised or accepted nomenclature and substituted synonyms, whereas Google Translate™ largely preserved medical terminology.We believe that textual indicators such as the absence of standard discipline-based terminology may be of assistance in the identification of machine paraphrased text.

Notes on the redacted fragments of Lidai fabao ji, an early Chan Buddhist chronicle

Thirteen ancient Chinese manuscripts have been seen as texts of a long lost 8th-century early Chan Buddhist chronicle, the Lidai fabao ji. A number of textual indications, however, suggest that they cannot be taken as the Tang sources, because their texts have been redacted between 907 and the early 11th century. The original Tang source, Lidai fabao ji, remains mysterious to date.