Human Communication Research Articles

Enhancements in Immediate Speech Emotion Detection: Harnessing Prosodic and Spectral Characteristics

Speech is essential to human communication for expressing and understanding feelings. Emotional speech processing has challenges with expert data sampling, dataset organization, and computational complexity in large-scale analysis. This study aims to reduce data redundancy and high dimensionality by introducing a new speech emotion recognition system. The system employs Diffusion Map to reduce dimensionality and includes Decision Trees and K-Nearest Neighbors(KNN)ensemble classifiers. These strategies are suggested to increase voice emotion recognition accuracy. Speech emotion recognition is gaining popularity in affective computing for usage in medical, industry, and academics. This project aims to provide an efficient and robust real-time emotion identification framework. In order to identify emotions using supervised machine learning models, this work makes use of paralinguistic factors such as intensity, pitch, and MFCC. In order to classify data, experimental analysis integrates prosodic and spectral information utilizing methods like Random Forest, Multilayer Perceptron, SVM, KNN, and Gaussian Naïve Bayes. Fast training times make these machine learning models excellent for real-time applications. SVM and MLP have the highest accuracy at 70.86% and 79.52%, respectively. Comparisons to benchmarks show significant improvements over earlier models.

STORYTELLING TO IGNITE LEARNING

Storytelling is as old as human communication. It is how communities kept and passed down their histories before the written word. For as long as there have been classrooms, teachers who wanted to connect with students have incorporated stories into their lessons. Today, teachers are navigating the complex realities of trying to keep students’ attention in the digital age. They are competing with the influence of social media and ubiquitous use of smartphones. In addition to these struggles, the effects of the worldwide pandemic and the realities of countries like Ukraine living through wartime on the state of global education, make it even more challenging to engage students. Teachers need additional tools to help students achieve critical outcomes and life skills. There is significant qualitative confirmation regarding the efficacy of storytelling in learning. The article presents research findings of educators in various fields who have studied the efficacy of storytelling as a pedagogy. This article also details observed outcomes and advantages of digital filmmaking as a type of storytelling that is an effective teaching framework. Digital storytelling is a dynamic and immersive tool that can help teachers foster lifelong learning skills in students. These include problem-solving, critical thinking, empathy and active listening, accountability, ethics, and integrity. Once learned, these skills and behaviors create the capacity to achieve further learning engagement, outcomes, such as leadership, community and innovation. Further, a consideration of how digital storytelling can help overcome recent challenges to the education system, such as COVID-19, is also included.

The Degree of Human Relations Practice in Leadership among Kindergarten Supervisors and Its Relationship to Job Performance in light of the Corona Crisis Management of Nannies inside the Green Line

The study aimed to discover thet degree of practicing the human relations of kindergartens’ supervisors and their relationship with job performance during the Corona crisis among nannies within the green line. The Researchers used the descriptive correlational survey methodology. The study sample consisted of (386) educators, they were randomly selected. Two questionnaires were used to collect information. The first was used to measure the degree of human relations use in leadership. It consisted of (24) items divided into (3) domains, which were (human interaction, moral stimulation, and effective communication). The second was used to measure the level of job performance. It consisted of (21) items divided into (3) domains of (planning and executing tasks, decision-making, performance, and continuity assessment). The results showed a strong relationship between kindergarten supervisors and their relationship with job performance. According to these results, the researchers recommended organizing training courses to enhance the human relations leadership of the supervisors.

Music Recommendation System by Detection of Emotions

Abstract: Expressing emotions through facial expressions is a fundamental aspect of human communication, offering insight into an individual's mood and well-being. The subtle nuances of the eyes, cheeks, forehead, and even the curve of a smile convey a wealth of emotional information. Music, as an art form, holds a unique power to relax and comfort both body and mind. Our project capitalizes on the synergy between these two elements by developing a system capable of discerning emotions from facial expressions and selecting appropriate music in response. This innovative approach not only aids in calming individuals and improving their mood but also streamlines the music selection process, saving valuable time. Furthermore, we aim to create software that offers this functionality universally, enabling users to access emotional music responses anytime, anywhere.

The Role of Personal Branding in Increasing Generation Z Career Success

The rapid development of human communication and interaction contributes significantly to intergenerational differences. Today's workplace is occupied by mostly generation Z, a generation that is skilled in technology. The aim of this research is to describe and analyze the relationship between personal branding and increasing career success for generation Z through the use of social media, Islamic human values, and skills among final students at the Faculty of Economics and Business, Muria Kudus University. The research population was 3846 students with 116 students being used as research samples. The data collection technique uses a questionnaire distributed via a digital platform, namely Google Form. The analysis technique uses Structural Equation Modeling (SEM) which is operated through the Partial Least Squares (PLS) program. The results of this research show that the use of social media, Islamic human values, and skills have a significant positive effect on personal branding. Personal branding has a significant positive effect on generation Z's career success.

Face and Speech Emotion Recognition System

Abstract: Emotions serve as the cornerstone of human communication, facilitating the expression of one's inner thoughts and feelings to others. Speech Emotion Recognition (SER) represents a pivotal endeavour aimed at deciphering the emotional nuances embedded within a speaker's voice signal. Universal emotions such as neutrality, anger, happiness, and sadness form the basis of this recognition process, allowing for the identification of fundamental emotional states. To achieve this, spectral and prosodic features are leveraged, each offering unique insights into the emotional content of speech. Spectral features, exemplified by the Mel Frequency Cepstral Coefficient (MFCC), provide a detailed analysis of the frequency distribution within speech signals, while prosodic features encompass elements like fundamental frequency, volume, pitch, speech intensity, and glottal parameters, capturing the rhythmic and tonal variations indicative of different emotional states. Through the integration of these features, SER systems can effectively simulate and classify a diverse range of emotional expressions, paving the way for enhanced human-computer interaction and communication technologies

Strengthening AI via ToM and MC dimensions

Theory of Mind (ToM) highlights the social-cognitive ability of the individual to communicate and interact effectively with the members of each social group. Essentially, it is the cornerstone of social knowledge that allows the recognition and understanding of the thoughts, intentions, and feelings of all involved, promoting social interaction and engagement. Metacognition (MC) is a higher mental ability of the biological mind and is characterized by the observation, control, evaluation, differentiation, and readjustment of the cognitive mechanism, aiming at its optimal performance and maintaining the homeostasis of mental, social, and emotional becoming of an organism. The rapid development of technology in recent decades has promoted the development of Artificial Intelligence (AI) intertwined with the need to integrate ToM and MC capabilities, enriching human communication. This paper investigates how the above-described human cognitive functions are involved in the conception and development of an artificial agent and their influence on human society. The conclusions suggest the importance of being able to read beliefs, emotions, and other factors, but also introspection by an intelligent system for social benefit, including the necessary ethical constraints.

Facial Expression Recognition Using Convolutional Neural Network

Understanding others' intentions through nonverbal cues like facial emotions is crucial in human communication. To design and train Deep Learning Models, this paper describes in detail how Convolutional Neural Network Models are developed using tf. Keras. The aim is to Sort facial photos into one of the seven face detection classifiers, our model is developed in such a manner that it learns hidden nonlinearity from the entered facial images, which is vital for discriminating the form of emotion someone is expressing. The model proposed on the Lenet-5 architecture by Yann LeCun uses the subsampling, feature map, and activation function (ReLu) in between the convolutional layer and fully connected layer for the output soft-max activation function will be used. The FER-2013 dataset, which consists of 35,887 structured 48x48 pixel grayscale images, was used to train the CNN models. The training dataset has 28,709 elements, while the testing dataset has 3,589 elements, and while validation has 3,589 elements. Train and test are the two folder names used to organize the FER dataset. separated even further into distinct files, each holding a different kind of FER dataset class. To mitigate the overfitting of the dropout, batch normalization and the model are employed. Since this is a multiclass classification problem, we are utilizing the Soft-max activation function and the Rectified linear unit for non-linear operation (ReLu). We are training a categorical cross- entropy and matrix for accuracy based on the parameters to assess the constructed CNN model's performance by examining the training epoch history and we have used optimizer Adam (Adaptive Moment Estimation) with the learning rate of 0.0001. We obtained the accuracy of the LeNet-5 model on training at 95.49% and testing at 49.47% [13].

Kurdish Sign Language Recognition Using Pre-Trained Deep Learning Models

In the tapestry of rich human communication, sign language gleams like one of the basic threads of this art, giving voice to hundreds of deaf and hard-of-hearing individuals in the region. The technology for recognizing and translating sign language fell far behind what these communities needed. Therefore, the present investigation is going to compare the performance of three top-performing deep learning algorithms in recognizing the signs created from the database of Kurdish Sign Language. The models are going to be put to a rigorous test, with a variety of signs drawn. All the 3 models give a performance that is—from all indications—good or even excellent, this is MobileNetV2, a very good candidate that manages to walk an amazing line between the requirements of high accuracy, low space complexity, and acceptable time complexity. We conclude by looking at some exciting opportunities for future research, including integrating our models into hardware devices and expanding our study to a larger variety of sign languages. And just as any good journey would, it throws up as many questions as it answers, leaving us inspired by the many possibilities that will need to be explored to enhance communication for all.

Every Thing Can Be a Hero! Narrative Visualization of Person, Object, and Other Biographies

Knowledge communication in cultural heritage and digital humanities currently faces two challenges, which this paper addresses: On the one hand, data-driven storytelling in these fields has mainly focused on human protagonists, while other essential entities (such as artworks and artifacts, institutions, or places) have been neglected. On the other hand, storytelling tools rarely support the larger chains of data practices, which are required to generate and shape the data and visualizations needed for such stories. This paper introduces the InTaVia platform, which has been developed to bridge these gaps. It supports the practices of data retrieval, creation, curation, analysis, and communication with coherent visualization support for multiple types of entities. We illustrate the added value of this open platform for storytelling with four case studies, focusing on (a) the life of Albrecht Dürer (person biography), (b) the Saliera salt cellar by Benvenuto Cellini (object biography), (c) the artist community of Lake Tuusula (group biography), and (d) the history of the Hofburg building complex in Vienna (place biography). Numerous suggestions for future research arise from this undertaking.

Artificial Intelligence in Social Media Forensics: A Comprehensive Survey and Analysis

Social media platforms have completely revolutionized human communication and social interactions. Their positive impacts are simply undeniable. What has also become undeniable is the prevalence of harmful antisocial behaviors on these platforms. Cyberbullying, misinformation, hate speech, radicalization, and extremist propaganda have caused significant harms to society and its most vulnerable populations. Thus, the social media forensics field was born to enable investigators and law enforcement agents to better investigate and prosecute these cybercrimes. This paper surveys the latest research works in the field to explore how artificial intelligence (AI) techniques are being utilized in social media forensics investigations. We examine how natural language processing can be used to identify extremist ideologies, detect online bullying, and analyze deceptive profiles. Additionally, we explore the literature on GNNs and how they are applied in social network modeling for forensic purposes. We conclude by discussing the key challenges in the field and suggest future research directions.

Bridging the Gap: Human and AI Interpersonal Communication Capacities for Veterinary Care Management

Bridging the Gap: Human and AI Interpersonal Communication Capacities for Veterinary Care Management

Linguistic means of expressing emotionality in English and Japanese

Emotion serves as a cornerstone in human communication, influencing the dynamics of interactions and fostering connections between individuals. Regardless of cultural backgrounds and linguistic differences, humans employ a diverse array of linguistic strategies to effectively convey their feelings. English and Japanese, as distinct languages rooted in rich linguistic traditions, offer compelling avenues for exploring the expression of emotion. This paper embarks on an examination of the linguistic means utilized in English and Japanese to articulate emotionality, shedding light on the intricate interplay of linguistic structures, vocabulary choices, and cultural influences that mold emotional expression within each language.

Susceptibility to poor arguments: The interplay of cognitive sophistication and attitudes

Despite everyday argumentation being crucial to human communication and decision-making, the cognitive determinants of argument evaluation are poorly known. This study examined how attitudes and aspects of cognitive sophistication, i.e., thinking styles and scientific literacy, relate to people’s acceptance of poorly justified arguments (e.g., unwarranted appeals to naturalness) on controversial topics (e.g., genetically modified organisms (GMOs)). The participants were more accepting of poorly justified arguments that aligned with their attitudes compared to those that opposed their attitudes, and this was true regardless of one’s thinking styles or level of scientific literacy. Still, most of the examined aspects of cognitive sophistication were also positively related to fallacy detection. The strongest cognitive predictors of correctly recognizing the fallacies were one’s scientific reasoning ability and active open-mindedness. The results thus imply that decreasing misleading attitude effects, and increasing certain aspects of analytic and scientific thinking, could improve argumentation.

A Proposed Approach for AI Doodle Generation with a Hybrid Intelligent Agent

Doodles are a form of human expression and communication that capture the essence of concepts and emotions in a simple and intuitive way. However, generating doodles from natural language descriptions is a challenging task that requires both understanding the meaning and context of the input, and producing the appropriate strokes and shapes of the output. In this paper, we present our approach to tackle this problem. Our proposed system leverages the vector-based nature of doodles and the semantic relationships of words. We propose use of a dataset of arrays of coordinates as drawn by humans (stored as vectors) to train our agent. There are several mechanisms that enable the agent to handle different aspects of the task, such as input word filtering, semantic mapping and keyword weighing, finding out closest words to input in the agent’s dictionary using vector embeddings, running one or multiple deep learning models in parallel depending on the number of objects, using the semantic mappings to train the assembler to linearly translate, transform or scale objects as per their semantic positional relationship, and using the turtle to draw the objects on a canvas. We identify and outline possible benchmarks, tests to evaluate the performance and quality of the agent using various metrics and user feedback. We also discuss the potential applications and implications of such an agent, such as art and design, education, and therapy.

The antecedents and consequences of university brand: a systematic review

University brand is becoming a research topic that is increasingly of interest to researchers and university administrators because of its great role in the context of the strong development of higher education. This research aims to review and synthesize current studies on definitions, antecedents and consequences of university brand. The study applied a systematic synthesis method of 76 articles from reputable database sources in the period 2000-2023. The research findings indicate that the antecedents of university brand are divided into tangible group and intangible group with 08 factors including brand design, facilities and equipment, human resources, training programs, financial policies, communication activities, reputation and brand personality. Furthermore, from the standpoint of approaching university stakeholders, the consequences of university brand are highlighted in this study. Finally, the study points out some policy implications for institutions and suggests some future research directions.

Attention to Entropic Communication

AbstractThe concept of attention has proven to be very relevant in artificial intelligence. Relative entropy (RE, aka Kullback‐Leibler divergence) plays a central role in communication theory. Here, these concepts, attention, and RE are combined. RE guides optimal encoding of messages in bandwidth‐limited communication as well as optimal message decoding via the maximum entropy principle. In the coding scenario, RE can be derived from four requirements, namely being analytical, local, proper, and calibrated. Weighted RE, used for attention steering in communications, turns out to be improper. To see how proper attention communication can emerge, a scenario of a message sender who wants to ensure that the receiver of the message can perform well‐informed actions is analyzed. In case only the curvature of the utility function maxima are known, it becomes desirable to accurately communicate an attention function, in this case a by this curvature weighted and re‐normalized probability function. Entropic attention communication is here proposed as the desired generalization of entropic communication that permits weighting while being proper, thereby aiding the design of optimal communication protocols in technical applications and helping to understand human communication. It provides the level of cooperation expected under misaligned interests of otherwise honest communication partners.

Satisfaction with human resource practices, job dedication and job performance: the role of incentive gamification

PurposeWhile the potential of human resource practices (HRPs) for promoting performance is widely recognized, even though crucial, employees’ assessment of HRPs remains under-researched, especially in emerging economies. Hence, the purpose of this research is to examine the influence of employee satisfaction with HRPs on job performance through the mediating role of job dedication (JD) and the moderating role of incentive gamification.Design/methodology/approachThe current research adopts a quantitative method. Specifically, using a questionnaire survey, 418 valid responses collected (through purposive sampling) via cross-sectional method from the employees of Nigerian Information and Communication Technology (ICT) firms were used to test the research hypotheses empirically.FindingsThe results revealed that satisfaction with HRPs has a positive influence on job performance. Satisfaction with human resource practices has a positive influence on job dedication. Job dedication has a positive influence on job performance. The link between employees’ satisfaction with human resource practices and job performance is mediated by job dedication. The link between satisfaction with human resource practices and job dedication is moderated by incentive gamification, that is, the positive link is stronger when incentive gamification is high. The link between satisfaction with human resource practices and job performance is moderated by incentive gamification, that is, the positive link is stronger when incentive gamification is high.Originality/valueThe current study highlights the importance of employees’ assessments of human resource practices, which may be used to promote employee dedication, which in turn results in improved performance. The findings are helpful to open the black box of the impact of satisfaction with HRPs on job performance. The results also offer important new valuable guidance for practitioners and will aid the management of human resource practice design, audits, and communication.

Decoding the Script: The Logographic Writing System of the Oldest Language

This study explores into the logographic writing system employed by the oldest language known to humanity. Through meticulous examination of ancient textual artifacts and linguistic analysis, it seeks to unravel the intricacies of this unique script. By exploring the symbolic representations utilized to convey meaning, the study sheds light on the cognitive and cultural dimensions of early human communication. Additionally, it investigates the enduring influence of this logographic system on subsequent linguistic traditions and its role in shaping the evolution of written expression. Through this exploration, the study contributes to a deeper understanding of the linguistic heritage of ancient civilizations and the enduring legacy of their written language systems.

LINGUOCULTUROLOGY AND CONCEPT

The article is devoted to the study of linguistics and culture and concept. The intersection between language and culture has become increasingly important in various academic fields such as linguistics, anthropology, psychology, and philosophy. This relationship is crucial as language serves as the primary means of human communication and is deeply intertwined with cultural contexts. Linguoculturology, a relatively new discipline, has emerged to explore the intricate connections between language and culture. This interdisciplinary field focuses on the cultural semantics of linguistic signs and aims to identify culturally relevant language units across various linguistic phenomena. Concepts play a central role in linguistic and cultural studies, representing complex cognitive structures that shape language and cultural representations. While there are different interpretations of the terms "concept" and "notion" within linguistic discourse, they are fundamental to understanding how language reflects cultural perspectives. Cognitive research highlights the role of concepts as storage units of human knowledge, shaping linguistic representations and cultural understanding.