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The Effects of Selected Preferred Music on Perceived Emotions through Audiovisual Stimuli

The Effects of Selected Preferred Music on Perceived Emotion...

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Big Data, Cloud Computing, Data science & engineering (BCD)

作者： Thunrada Thaiwong Makoto Fukumoto Graduate School of Computer Science and Engineering Fukuoka Institute of Technology Fukuoka Japan Dept. of Computer Science and Engineering Fukuoka Institute of Technology Fukuoka Japan

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

Music surrounds us, and there is no denying that music in visual media can shape and evoke emotions. Yet, understanding how musical preference influences emotions through audio and visual stimuli remains an important task. To address this task, we investigated the role of musical preference effect on perceived emotions induced through music and visual stimuli (i.e., animation), using a 7-point scale for valence-arousal ratings and physiological responses in electroencephalogram (EEG) band power. The perceived emotions are categorized into four states: happiness, calmness, fear, and sadness. One emotional state contains 4 sessions: (1) Preferred Music, (2) Unfamiliar Music, (3) Preferred Music+Animation, and (4) Unfamiliar Music+ Animation. Behavior-wise, the rating results showed that the presence of preferred music resulted in higher perceived valence ratings, particularly in happiness. However, no stimulus had a significant effect on perceived arousal ratings and satisfaction ratings. Physiologically, the EEG indexes showed that the presence of preferred music appears to affect an increase in alpha power across various emotions except sadness, whereas unfamiliar music seems to affect beta power, particularly in happiness and calmness. Overall, these findings supported that musical preference is an affective factor reflecting the levels of valence and alpha power in EEG. Nevertheless, our findings did not confirm a significant difference between only preferred music and combining it with animation. Interestingly, we also found that participants were more likely to be attracted to and perceive positive (i.e., happiness and calmness) emotions easily through preferred music and/or visual stimuli than negative (i.e., fear) emotions.

关键词： Visualization Cloud computing Shape Music Media Data science Animation Physiology Electroencephalography Indexes

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Data Access Control in Cloud Computing

Data Access Control in Cloud Computing

引用

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Gayathri, G.N. Channakrishnaraju Sri Siddhartha Academy of Higher Education Dept. of Computer Science and Engineering Tumkur India

ISBN: (纸本)9781665483162

Currently, news has revealed powerful attackers who can breach the confidentiality of data by means of secretly obtaining cryptographic keys. The keys are obtained through threats within software cryptography. When the encryption key gets exposed, the lone reasonable act is used for maintaining the confidentiality of data to confine the attacker access to the ciphertext. The Confining attacker's access is accomplished using widening blocks for ciphertext over servers within several administrative domains. Thus, presuming the adversary will not pose danger. However, once encryption of data is done using prevalent designs then the process of encryption key adversary will be compromised with a single server as it will decrypt to perform encryption over the blocks of ciphertext. An adversary perceives the encryption key and will possess it for accessing a huge portion of the ciphertext blocks. The present research paper will review the existing encryption-decryption techniques for achieving Secure, Scalable, and Fine-grained Data Access Control in Cloud Computing. © 2022 IEEE.

关键词： Cryptography

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QPUF: Quantum Physical Unclonable Functions for Security-by-Design of Industrial Internet-of-Things 9

QPUF: Quantum Physical Unclonable Functions for Security-by-...

引用

9th IEEE International Symposium on Smart Electronic Systems, iSES 2023

作者： Bathalapalli, Venkata K. V. V. Mohanty, Saraju P. Pan, Chenyun Kougianos, Elias University of North Texas Dept. of Computer Science and Engineering United States University of Texas Dept. of Electrical Engineering Arlington United States University of North Texas Dept. of Electrical Engineering United States

ISBN: (纸本)9798350383249

This work presents a Quantum Computing based Physical Unclonable Functions (PUF) architecture with a novel approach for PUF key generation and noise reduction in noisy Quantum systems. The motivation is to explore the potential of Quantum Computing assisted cybersecurity for Security-by-Design (SbD) of Industrial-Internet-of-Things (IIoT). Internet-of-Things (IoT) in Industry 4.0 can improve efficiency, increase automation, and facilitate intelligent decision making through the integration of edge cloud computing paradigms. However, the lack of standardization of security protocols across IIoT devices can be challenging due to the heterogeneous nature of these devices. In this context, a novel Quantum circuits based PUF design that is built on real Quantum hardware is presented. The proposed QPUF can facilitate security in IIoT by using a Quantum hardware generated PUF key as the device identity. The proposed PUF Design is built using Quantum Hadamard Gate, and Ry gates and has been experimentally demonstrated on IBM Quantum hardware systems. The proposed architecture also supports PUF key generation from Quantum hardware for security applications. Figures-of-merits (FoM) of PUF have been evaluated and presented to demonstrate the robustness of the proposed Quantum PUF for SbD of IIoT. © 2023 IEEE.

关键词： Internet of things

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A Multi-Model Rouge Nodes Detection System for Fog Computing

A Multi-Model Rouge Nodes Detection System for Fog Computing

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computer science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Thanh Duc Bui Brajendra Panda Dept. of Computer Science and Computer Engineering University of Arkansas Fayetteville USA

With the increasing speed of advance of both Internet of Things (IoT) and the demands of utilizing IoT devices in different locations with high compatibility, fog computing has been foreseen as a sustainable solution. However, new challenges related to fog computing's cyber security also emerge. Among various cyber-attacks on the fog computing system, one standard method is to implement corrupted internal nodes that allow malicious/compromised access from attackers, threatening the confidentiality of users' information and the system's performance. In this paper, we propose a method to quantify the distribution of each attribute value from historical access logs from each node within a fog computing system. We also utilize an unsupervised machine learning method to separate nodes into smaller clusters with respect to different models to improve the forecasting ability of the system. Finally, our models are evaluated over simulated data using real access logs. Our experimental results illustrate that our proposed method achieves some advanced performance over applying an overall classification, even using the same classification machine learning methods and dataset.

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A Factor Based Multiple Imputation Approach to Handle Class Imbalance

A Factor Based Multiple Imputation Approach to Handle Class ...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Baro, Pranita Borah, Malaya Dutta Dept. of Computer Science and Engineering National Institute of Technology Silchar Silchar788010 India

Class imbalance and incompleteness are the two most serious problems faced in data science and machine learning when working on real-life datasets. Both of these cases have severe implications on the ability of classification algorithms to make accurate predictions. When a dataset used for training classifiers is both imbalanced as well as incomplete, the traditional approach is to address the missing data first and then handle class imbalance but it could lead to some issues such as overfitting as well as amplification of some errors due to random duplication. In this paper, an alternate factor-based multiple imputation oversampling method (FB-MIO) is proposed to handle class imbalance as well as missing values in the training dataset at the same time. First, a new factor is presented to evaluate the density of missing values belonging to the majority class with respect to the minority class in a particular region. With the help of this factor, an oscillator is developed to guide how imputation based oversampling should be carried out. Then the training set is divided into multiple smaller subsets and used the oscillator to determine whether missing values for the majority class belonging to that subsets should be imputed or not. This would help in preventing exaggerated duplication when not needed. Experiments were carried out on 27 imbalanced datasets after random addition of missing value and the F1 and AUROC scores of FB-MIO were compared to other dataset level resampling methods such as SMOTE, ADASYN, B-SMOTE etc. The effectiveness of the proposed method has been validated after experiments on benchmark datasets and the comparative results are presented in the form of average rank and number of wins. © 2022 Elsevier B.V.. All rights reserved.

关键词： Random errors

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Ethnicity Prediction From Audio Samples for Different Languages Using Convolutional Neural

Ethnicity Prediction From Audio Samples for Different Langua...

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Emerging Technology (INCET), International Conference for

作者： Sharvaani Ravikumar Thoguluva Suraj Gopinath Udith S Rimjhim Padam Singh Sneha Kanchan Dept of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Dept of Internet Engineering and Computer Science University Tunku Abdul Rahman Malaysia

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

“Ethnicity Prediction from Voice Samples” deals with prediction of a person's ethnicity from his/her voice samples. Ethnicity Prediction has gained more and more importance and attention in fields like security, recommendations systems, learning apps etc. The proposed work uses a customized Convolutional Neural Network (CNN) based model to predict the ethnicity of the speaker based on combination of varied audio features like Mel-Frequency Cepstral Coefficients (MFCC), pitch, energy, and zero crossing rate. The different voice samples are obtained from the popular Speech Accent Archive Dataset. Augmentation of data by performing time and pitch shifting proved to give a significant boost in the accuracy as it increased the number of voice recordings. The work also compares the performance of the proposed model with other state-of-art models like LSTM. The F-score obtained was 98% for the class Russian, Polish and 90% for the class French, Italian, Spanish i.e. Romance Languages and 80% for mixed language category that comprises of Indian, Italian, German and Polish.

关键词： Accuracy Scalability Predictive models Data models Recording Convolutional neural networks Security

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A Review of Disease Detection in Sugarcane Using Machine Learning and Deep Learning Techniques

A Review of Disease Detection in Sugarcane Using Machine Lea...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Undavalli Chandra Pavan R.K Pateriya Deepak Singh Tomar Surendra Singh Dept. of computer science & engineering Maulana Azad National Institute of Technology Bhopal India Dept. of computer science & engineering National Institute of Technology Uttarakhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Agriculture has been an active source of food and economic growth, but it faces significant challenges from diseases and climate change. In Indonesia, sugarcane production is severely impacted by viral infections such as Sugarcane mosaic virus (SCMV) and Sugarcane streak mosaic virus (SCSMV). These infectious diseases pose a threat to the world’s sugarcane industry, resulting in significant crop losses in nations like Brazil and India, along with other viruses like the Fiji Disease Virus (FDV), and Sugarcane Yellow Leaf Virus (SCYLV). For such diseases to be managed and performance to be maintained, early detection is essential. Disease detection is accomplished by both conventional and cutting-edge approaches, such as molecular, serological, and imaging methods. However, the advancements in Machine learning, Deep learning and computational image processing algorithms have demonstrated significant potential in the early and precise diagnosis of disease. In this study, different methods used for machine learning (ML) and deep learning (DL) for plant disease diagnosis were reviewed and it highlights the need of high-quality datasets, various pre-processing techniques, robust extraction of features, and efficient classification techniques. It also discussed the issues with data collection, symptom variability, over-fitting, and imbalances in classes, along with potential avenues for further advancement in diagnosing diseases systems.

关键词： Deep learning Economics Plant diseases computer viruses Computational modeling Crops Data collection Feature extraction Viruses (medical) Diseases

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Attentiveness Recognition System for Online Classes using OpenCV 2

Attentiveness Recognition System for Online Classes using Op...

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2nd IEEE International Conference on Intelligent Technologies, CONIT 2022

作者： Vignesh, K. Deepak Nithin Gupta, V. Kumar, Hemanth Kalasalingam Academy of Research and Education Krishnankoil Dept of Computer Science and Engineering TamilNadu Virudhunagar India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

During the pandemic, education was challenging for many institutions. While some of them even to closed, and were subjected to criticisms from multiple stakeholders, others persevered and embraced 'Online-Education'. This seemed to work well for some institutes though others were struggling to cope with the new culture that was not well understood by both sides;the instructors as well as the students. Our sole objective of this undertaking is to ease the pressure on teachers and students, and help create a healthier ecosystem that further promotes progress in education. We have designed a tool that essentially picks up each student's emotions throughout the whole online class and calculate the degree of attentiveness of the student basing on multiple parameters. At the end of the class, the teacher gets an auto-generated report about their students. With the help of this report, teachers shall be able to come up with a counter measure to adjust accordingly by either fixing their teaching patterns or spending extra time with particular students. We used OpenCV and Convolutional Neural Networks for this system. © 2022 IEEE.

关键词： Students

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Improving Healthcare Question Answering System by Identifying Suitable Answers 2

Improving Healthcare Question Answering System by Identifyin...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Das, Baivab Nirmala, S. Jaya National Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruchirappalli India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Healthcare question answering is an important task in the field of Natural Language Processing. As the era of the internet has already begun, textual data related to the healthcare field has also increased rapidly. There is a huge opportunity to utilize the full potential of deep learning-based methods to achieve better accuracy in healthcare question answering. Unfortunately, the open-domain pre-trained models do not perform well in healthcare question answering. As the terminologies used in the healthcare field are unique, the concept of understanding them is hard. To resolve this issue, in this project, the BioBERT model is used to answer questions related to the healthcare field. BioBERT is based on BERT (Bidirectional Encoder Representations from Transformers) and it is pre-trained on large biomedical corpora which gives it the ability to understand healthcare-related terminologies. As the BERT model has already proved its efficiency as a bidirectional language model, the BioBERT model here is trained with the SQUAD question-answering dataset. The model is able to answer questions related to the healthcare field with an F1 score of 88. © 2022 IEEE.

关键词： Terminology

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Analysis of Energy Potential of Micro Wind Turbines at Three Locations in the Central-Eastern Part of the Bulgarian Danube River Region 5

Analysis of Energy Potential of Micro Wind Turbines at Three...

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5th IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Valov, Nikolay Gabrovska-Evstatieva, Katerina Stoyanov, Ivaylo Kaneva, Tsvetelina Kadirova, Seher Evstatiev, Boris Mihailov, Nicolay University of Ruse "Angel Kanchev" Dept. of Automation and Electronics Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Computer Science Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Electrical Power Engineering Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Computer Systems and Technologies Ruse Bulgaria

ISBN: (纸本)9798350352863

The share of renewable energy sources in the energy mix worldwide is gradually increasing. And even though they all have a highly variable nature, they can be forecasted statistically. The wind energy potential was investigated in this article in the Bulgarian Danube region, located between Ruse and Silistra. Two years of meteorological data, including the wind speed at 10 m height and the ambient temperature was used to estimate the average wind energy for each month of the year at three geographic locations - Ruse, Staro Selo (near Tutrakan), and Calarasi, Romania (near Silistra). The results showed that Staro Selo has the highest energy potential, reaching up to 0.75 kWh/day/m2 in February, followed by Calarasi with 0.38 kWh/day/m2. The city of Ruse showed the lowest wind potential with the highest average monthly value reaching only 0.0027 kWh/day/m2. The obtained results showed that the regions around Tutrakan and Silistra are appropriate for the application of micro wind turbines. © 2024 IEEE.

关键词： Windmill

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