7712 research outputs found
Sort by
Exploring User Intention to Use Generative AI in Music Composition: An SEM-ANN Methodology
No full textGenerative AI has emerged as a powerful tool in the creative industry, particularly in music composition, offering musicians new ways to enhance their creative processes. However, understanding the factors that influence user intention to adopt these AI-driven tools remains underexplored. This study investigates the user intention to use generative AI in music composition by integrating components from the Technology Acceptance Model (TAM) with perceived creativity and personal innovativeness. This paper addresses the need to identify the key factors which influence the adoption of generative AI for creative tasks, with a specific focus on its application in music composition. To achieve this, data was collected from 843 musicians, students, and music enthusiasts. A hybrid methodology combining Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) were employed to analyze both linear and non-linear relationships among the variables. The results highlight that perceived creativity, usefulness, and ease of use are significant predictors of user intention to adopt generative AI for music composition. Furthermore, user satisfaction and flow experience play a crucial role in enhancing adoption. The study’s implications are both theoretical and practical. Theoretically, it offers a comprehensive framework for understanding the adoption of AI technologies in creative fields, particularly music. The findings can practically guide AI tool developers and music professionals in designing user-centric tools that improve creativity and satisfaction. The integration of SEM and ANN methodologies also demonstrates their effectiveness in examining complex user behavior patterns
Determinants of Russia’s probability of default: evidence from domestic and global indicators
No full textThis study investigates the determinants of Russia’s probability of default (PD) within the context of several internal and external factors related to monetary policy (market-based interest rate, currency exchange rate), current account deficit (oil prices, natural gas prices), and global risk perception (gold prices, VIX index). Using Dynamic Conditional Correlation–Exponential Generalized Autoregressive Conditional Heteroskedasticity (DCC-EGARCH) and Time-Varying Parameter Vector Autoregression (TVP-VAR) analyses, we find that shifts in Russia\u27s monetary policy exert a stronger influence on PD than commodity prices or global financial indicators. Spillovers from and to PD are further explored within the context of geopolitical risk and sovereign credit ratings. Our results reveal asymmetric spillovers between PD and the selected variables. Monetary policy indicators, particularly the market-based interest rate and exchange rate, significantly influence PD under both positive and negative returns. Conversely, Russia’s PD exhibits a spillover effect on gold prices in positive and negative returns, and natural gas in negative returns, highlighting Russia’s influence on safe-haven asset demand and on global energy markets. During the Ukraine conflict, we observe persistent and pronounced spillovers from the exchange rate to PD in negative returns. Additionally, spillovers between PD and geopolitical risk suggests potential credit rating adjustments for Russia, offering early-warning signals for investors and policymakers
Bio-polymerized Sulfur for Sustainable Practice in Applied Sciences and Engineering
No full textBio-polymerized Sulfur for Sustainable Practice in Applied Sciences and Engineering explores innovative approaches in sustainable chemistry by leveraging renewable resources and sulfur as foundational elements for creating sustainable functional materials. The book highlights the potential of bio-polymeric materials derived from sulfur and renewable sources, offering new avenues for environmentally-friendly manufacturing. Additionally, the text delves into lifecycle assessment studies and the principles of a circular economy, emphasizing the importance of sustainability in modern engineering. The work emphasizes the criticality of sustainable practices, highlighting the intersection of bio-polymeric materials and circular economy principles, ultimately guiding the reader towards a more sustainable future. The book also presents an in-depth analysis of bio-polymerized sulfur\u27s role in promoting sustainable development. It discusses how polymerized sulfur can be used to develop engineered products that align with sustainability goals
The impact of past trauma on psychological distress among university students during COVID-19: a moderated mediation of cognitive distortion and dispositional mindfulness
No full textAims: The current study aimed to examine the potential effects of cognitive distortion and dispositional mindfulness on the association between PTSD symptoms and psychological distress among Chinese university students during COVID-19. Methods: Latent moderated structural equation modelling with a longitudinal design was adopted. 208 participants from Chinese universities completed the posttraumatic stress disorder (PTSD) Checklist for DSM-5, Cognitive Distortion Scale, Philadelphia Mindfulness Scales, and General Health Questionnaire twice in a 6-month interval. Results: Initial PTSD symptoms following past trauma were positively associated with psychological distress at Time 2 (T2). Distorted cognition at T2 mediated the association between them. Moreover, this mediation effect was moderated by dispositional mindfulness. Specifically, dispositional mindfulness moderated the first stage of the mediating process. That is, mindful awareness and acceptance could effectively mitigate the distorted cognition caused PTSD symptoms from past trauma, and the protective effects of this moderation on higher awareness and acceptance were more obvious. Conclusions: Following traumas, university students can develop PTSD symptoms affecting mental health via distorted perceptions of themselves, others, and the world. Dispositional mindfulness awareness and acceptance could be useful strategies to alleviate PTSD symptoms and trauma effects especially for those who have developed distorted cognitions
A pre–post study evaluating an online CBT-based intervention to improve academic performance in students with low mood
Get PDFOnline cognitive-behavioural therapy (CBT)-based interventions have shown the potential to improve the mental health of university students. However, their impact on West Asian cultures and educational achievement has yet to be fully investigated. This study explores the feasibility, acceptability, and potential effectiveness of a self-directed, internet-delivered, cognitive–behavioural skills training programme (MoodGYM) in reducing depression and improving academic performance among university students in the United Arab Emirates (UAE). This exploratory pre- and postintervention study with a historical control group recruited 50 students, having a GPA \u3c2 and self-reporting at least one of two key depressive symptoms, from one UAE university. The results demonstrated that the total Hospital Depression and Anxiety Scale (HADS) depression score (HADS-D) decreased after the intervention (P = 0.004), and the proportion of participants scoring above the cutoff for depression (HADS-D ≥8) decreased from 77.2 to 27.3% (p \u3c 0.001). There was also a significant reduction in HADS-anxiety scores (p \u3c 0.001), and the proportion of participants above the cut-off for anxiety (HADS-A ≥8) decreased from 50% to 11.4% (p = 0.001). GPA improved significantly over time (p \u3c 0.001, d = 1.3), and attendance warnings decreased (p = 0.008, d = 0.6). Most students (79.6%) evaluated MoodGYM as useful, and all students completed at least two MoodGYM modules. This study shows that MoodGYM, a web-based mental health promotion intervention, improves academic achievement in university students with depressive symptoms. Further research is needed to explore how MoodGYM can be best implemented within university settings
Quadruped Robots and Canine Likeness: The Uncanny Valley Effect
No full textThis paper presents canine likeness features in robotic quadrupeds that influence their social perception. We adopted Contrastive Language-Image Pre-Training (CLIP), a neural network that has demonstrated signatures of the Uncanny Valley effect, to explore how the perception of quadrupeds evolves as their level of canine likeness intensifies. Seven models were tested, ranging from a fully robotic quadruped to a living dog with 252 images. Our findings indicate that the Uncanny Valley effect also develops in quadruped robots. This finding is a reference to selecting an appropriate level of realism for canine likeness fourlegged robots in Human-Robot Interaction (HRI)
Building Trust in AI Voice Assistants: Exploring the Role of Security, Information Quality, and Social Influence in the Telecommunications Industry
No full textThe growing incorporation of AI-powered voice assistants in telecommunications has revolutionized customer service interactions. Nevertheless, trust continues to be a crucial factor influencing user adoption. This study investigates the factors influencing trust in AI voice assistants, focusing on perceived security and privacy, quality of information, and social influence. A quantitative method was used to gather data from 248 respondents and analyze the findings through structural equation modeling. The results reveal that perceived security and privacy strongly influence trust, followed by information quality and social influence. Additionally, trust plays a crucial mediating role in shaping users\u27 intentions to adopt AI voice assistants. These findings offer valuable insights for telecom service providers, emphasizing the importance of strengthening data security, enhancing information quality, and utilizing social influence strategies to build user trust
Closing the Loop on Speech to Music Translation: Automatically Generating Synthetic Percussive Sequences on the Mridangam from Konnakol
No full textThis paper presents a pipeline to convert spoken Konnakol sequences, a South Indian vocal percussion language, into synthetic rhythmic sequences performed on the mridangam. We fine-tune the Whisper speech-to-text model on Konnakol data, enabling accurate transcription of spoken sequences, despite the small size of our dataset (approximately 15 minutes). The transcriptions are rhythmically encoded in a format that is compatible with the Konnakol Typewriter, a web application that converts these sequences into mridangam audio. Additionally, these transcriptions serve as input for a Markov model, which generates new rhythmic sequences that can also be processed through the Konnakol Typewriter to produce mridangam audio. Whisper\u27s performance is impressive with very low error rates, making it an ideal tool for this task. This pipeline not only facilitates the transcription of Konnakol but also opens possibilities for creating educational tools, preserving cultural heritage, and generating data for rhythm-based applications. Future work will focus on refining the process to improve accuracy and versatility
AI doesn\u27t have a soul. Exploring Gen Z\u27s Perceptions of Social Chatbots
No full textRecent breakthroughs in AI have allowed social chatbots (SCs) to mimic engaging, compassionate conversations that dissolve the traditional barriers between humans and machines while also offering critical social and emotional assistance. As digital natives, Generation Z (Gen Z) students thrive on personalized, emotionally resonant digital interactions, positioning them as a pivotal audience for these cutting-edge AI-driven SCs. This qualitative study examines Gen Z students\u27 experiences with SCs using a survey featuring open-ended questions. 156 students shared their insights into the SCs they have used, evaluating the benefits these tools offer and identifying areas where improvements are needed. The results reveal that students highly value SCs who exhibit genuine empathy, personalized responses, and robust conversational abilities. These findings provide actionable recommendations for enhancing SCs to better meet the social and emotional needs of their users
Characterizing Vulnerabilities in Microservices: Source, Age and Severity
No full textMicroservices architecture has become a popular choice for developing scalable, cloud-native applications because of its modularity. However, this architecture introduces unique security risks due to its distributed nature and dependency management requirements. While existing literature has investigated some security challenges and proposed mitigation strategies, there is a lack of comprehensive research on the security vulnerabilities present within systems using this architecture. To bridge this gap, we used three vulnerability detection tools to analyze security vulnerabilities across 30 open-source microservices projects from GitHub, identifying three sources of vulnerabilities: \u27application code\u27, \u27dependencies\u27, and \u27container configurations\u27. Vulnerabilities related to request and data handling were most common, stemming from container misconfigurations and outdated dependencies. Dependency-related vulnerabilities are new but most fall into pre-established CWE and OWASP top 10 categories. Most of the detected vulnerabilities fall under a severity of medium to high. While the emergence of microservices has not introduced new vulnerabilities, the severity of existing vulnerabilities urges developers to implement secure data and request handling, address container misconfigurations, and update dependencies timely