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INDONESIA
Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 903 Documents
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Detecting Genuine Versus Fake Emotions: A Dual-Task Deep Learning Approach Using Facial Expression Analysis Sarah Tasnim Diya; Most. Jannatul Ferdos; Md. Mizanur Rahman; Yadab Sutradhar; Zahura Zaman; Suman Ahmmed; Ohidujjaman
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-018

Abstract

Facial expression recognition (FER) is a relevant field of study with applications in human-computer interaction, healthcare, and security. Although recent approaches demonstrate excellent outcomes on the recognition of basic emotions, the authenticity of expressions (genuine versus fake) remains unexplored. In this work, we propose a dual-task deep learning framework based on EfficientNet-B0, enhanced with a lightweight squeeze-and-excitation (SE) attention mechanism, to collaboratively work on multiclass emotion recognition (seven categories: angry, disgust, fear, happy, neutral, sad and surprise) and authenticity classification (genuine vs fake). The architecture leverages a shared backbone for representing feature, followed by task-dedicated branches trained using categorical cross-entropy and focal loss, respectively. To overcome the lack of publicly available benchmarks incorporating authenticity labels, we designed a curated dataset annotated with both emotional categories and authenticity information. Experimental evaluation demonstrates that the proposed dual-task model with the SE attention mechanism achieves 98.5% accuracy for emotion recognition and 92.2% accuracy for authenticity prediction, emphasizing both the effectiveness of the framework and the inherent challenges of authenticity detection. Moreover, we present a deployable real-time system demonstrating the feasibility of integrating authenticity-aware FER into practical applications such as e-learning analytics, security surveillance, and affective computing.
Academic Dishonesty Among University Students: Gender, Semester Differences, and Influencing Factors Renya Rosari; Anis Chariri; Dwi Cahyo Utomo
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-019

Abstract

This study examines differences in academic dishonesty among university students based on gender and semester level and identifies factors influencing such behavior using an explanatory sequential mixed-methods design. Quantitative data were collected from 405 undergraduate students across five semester levels (II, IV, VI, VIII, and X) using the Academic Dishonesty Scale (ADS) and analyzed with non-parametric statistical tests. The results show significant differences in examination-related cheating across semesters (p = 0.012) and significant gender differences across several indicators (p < 0.05), with male students and those in early semesters displaying higher levels of dishonest behavior. To further explain these findings, qualitative data were obtained through in-depth interviews with seven informants and analyzed thematically. The qualitative results indicate that academic dishonesty is influenced by pressure to achieve high grades, insufficient study preparation, permissive peer environments, and limited understanding of academic ethics. The novelty of this study lies in combining a validated measurement instrument with qualitative follow-up to provide contextual explanations of academic dishonesty in Indonesian higher education. The findings highlight the need for stricter supervision, strengthened academic ethics education, improved time management skills, and clearer institutional policies to foster an academic culture that promotes integrity.
From Awareness to Action: Mindfulness Brief Interventions Shaping Positive Affect and Decision Certainty Yani Duan; Nor Akmar Bt. Nordin; Siti Aisyah Panatik; Huayi Liu
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-027

Abstract

Purpose: This study aims to explore the effect of a five-minute mindfulness audio intervention on improving state positive emotion and decision-making effectiveness under uncertainty, and to examine whether trait maximization moderates these effects among Chinese university students. Method: A randomized between-subjects experiment (N = 320) was conducted, in which participants were assigned to either a brief mindfulness exercise or a time-matched neutral audio control. State positive emotion was measured immediately after the manipulation using the PANAS positive affect scale. Participants then completed five worst-case scenario tasks (least-worst decision scenarios). Decision time, perceived decision difficulty, and the percentage of approach choices were recorded. Structural equation modeling was used to test mediation effects, and interaction modeling was applied to examine moderation. Findings: Participants in the mindfulness condition reported higher levels of positive emotion and demonstrated more effective decision-making patterns, characterized by faster decisions, lower perceived difficulty, and a higher proportion of approach-oriented choices. Positive emotion partially mediated the relationship between mindfulness and decision effectiveness. However, the benefits of mindfulness on approach choices were reduced among individuals with higher maximization tendencies. Originality/Implications: This study contributes to the literature on least-worst decision making by incorporating an affective mechanism and an individual difference moderator within a Chinese sample. The findings suggest that brief, scalable mindfulness interventions can support approach-oriented decision behavior under uncertainty, while also indicating that such interventions may need to be tailored for individuals with high maximization tendencies.

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