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Esteso: Interactive AI Music Duet Based on Player-Idiosyncratic Extended Double Bass Techniques

Esteso: Interactive AI Music Duet Based on Player-Idiosyncra...

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International Conference on New Interfaces for Musical Expression, NIME 2024

作者： Stefani, Domenico Tomasetti, Matteo Angeloni, Filippo Turchet, Luca Dept. of Information Engineering and Computer Science University of Trento Trento Italy Italy

Extended playing techniques are a crucial characteristic of contemporary double bass practice. Players find their voice by developing a personal vocabulary of techniques through practice and experimentation. These player-idiosyncratic techniques are used in composition, performance, and improvisation. Today’s AI methods offer the opportunity to recognize such techniques and repurpose them in real-time, leading to new forms of interactions between musicians and machines. This paper is the result of a collaboration between a composer/double-bass player and researchers, born from the musician’s desire for an interactive improvisational experience with AI centered around the practice of his extended techniques. With this aim, we developed Esteso: an interactive improvisational system based on extended technique recognition, live electronics, and a timbre-transfer double-bass model. We evaluated our system with the musician with three duet improvisational sessions, each using different mapping strategies between the techniques and the sound of the virtual double bass counterpart. We collected qualitative data from the musician to gather insights about the three configurations and the corresponding improvisational duets, as well as investigate the resulting interactions. We provide a discussion about the outcomes of our analysis and draw more general design considerations. © 2024, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： computer music

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Leveraging Convolutional Neural Network for Real-Time Facial Emotion Detection

Leveraging Convolutional Neural Network for Real-Time Facial...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Thakur, Sujata Negi Pandey, Muskan Tiwari, Vaibhavi Graphic Era Hill University Dept. of Computer Science and Engineering Haldwani India

ISBN: (纸本)9798350379716

Facial emotion detection is a very relevant research domain, having its applications in a wide diversified field of topics that include statistical analysis, the social sciences, and computer vision. Developing methods for machines that can recognize and interpret human emotions in real-time has been revolutionary for improving human-computer interactions, enhancing security systems, and helping with substantial insights from psychological studies. In this paper, it is proposed to develop a new model of emotion recognition where emotions through facial images will be classified based on seven different emotional features. Taking grayscale photos from a dataset called fer2013, we train a Convolutional Neural Network for the purpose of identifying these emotions. As far as the approach applied in this paper is concerned, the process of emotion identification is basically divided into three major phases: face detection, feature extraction, and emotion classification. We tried different depths and layers of CNNs, achieving as high an accuracy as 89.98%, which has huge potential for deep learning in advancing FER technologies. The contribution with this research is to developing efforts on more intuitive and responsive AI systems that can understand and respond to human emotions in an emotional sense. © 2024 IEEE.

关键词： Convolutional neural networks

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Active Control for Resonance Mitigation in Electric Drive Applications 15

Active Control for Resonance Mitigation in Electric Drive Ap...

引用

15th International Symposium on Industrial Electronics and Applications, INDEL 2024

作者： Berglund, Emelie Borg, Johan Johansson, Jonny Luleå University of Technology Dept. of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350352320

The use of inverter-based motor controllers that can generate fast voltage level transitions is increasing in the field of electric drive systems because of their ability to achieve higher efficiency. However, the fast voltage transitions, dv/dt's, are prone to generate bearing currents and cause overvoltages that may harm the electric motor. An LC filter applied between the inverter and the motor terminals can reduce the dv/dt's. However, the LC filter will cause overvoltage due to resonance but this can be overcome by introducing an extra pulse at each pulse-width modulation (PWM) transition. The timing of this extra pulse is crucial for suppressing the resonance, but due to nonlinearities in a real system, the time instants may vary over time and are thus in need of control. In this study, an analytical expression for the optimal pulse location and duration is derived and we propose a control algorithm that continuously calculates the optimal pulse switch time instants based on the measured output voltage. The control algorithm can handle sudden system disturbances and a Monte Carlo simulation is performed to evaluate the performance of the algorithm. This hybrid filter successfully reduces dv/dt's at the motor terminals while simultaneously eliminating overvoltages due to the filter resonance. © 2024 IEEE.

关键词： Control nonlinearities

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Electronic Monitoring and Control of Environmental Parameters Using IoT

Electronic Monitoring and Control of Environmental Parameter...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Mohana Kumar, S. Kavitha, H. Murthy, Krishna Nisha, S. Dayananda, P. Manjunath, R. Ramaiah Institute of Technology Dept. Name of Dept.of Computer Science Bangalore India Siddaganga Institute of Technology Dept.of Information Science Tumakuru India Rvitm Dept.of Information Science Bangalore India Bangalore India Manipal Institute of Technology Department of Information Technology Bengaluru India Manipal Academy of Higher Education Manipal India R R Institute of Technology Department of Computer Science Bangalore India

ISBN: (纸本)9798350300826

A phenomenon is known as 'The Internet of Things' is unstoppable due to the rise of sensors, programmable chips, embedded wireless technologies, and a growing amount of internet bandwidth. Our time may be characterized by the Internet of Things (IoT), which is perhaps the most important technological niche. The interconnected devices that can send and receive data over the Internet are called the Internet of things. A prototype electronic and computer system for controlling and monitoring environmental parameters in a communications equipment room is described in this article. The weather stations such a climate monitoring, food chains, meat thermometers health monitoring, and smart home systems such as lighting, appliances, etc., and fruit dehydrators (temperature/humidity monitoring), this device can be implemented in a variety of applications. Additionally, the types of sensors used must be modified according to the target application, among other things. The objective of this work is to explore the Internet of Things, which can be applicable in different productive sectors in metro cities. In coordination with an opportunity was identified to improve the monitoring of its communications equipment rooms. Its work motivated on an IoT device. As part of the Shield Ethernet electronic board stacked onto the Arduino MEGA, the electronic device is capable of Internet connectivity via TCP/IP protocol. © 2023 IEEE.

关键词： Internet of things

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Enhancing Image Translation with Conditional Generative Adversarial Networks 3

Enhancing Image Translation with Conditional Generative Adve...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Verma, Shobhit Arora, Srishti Kayat, Tanvi Goel, Anurag Delhi Technological University Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350391565

This article examines the advantages of engaging in conflictual discourse as a means to address and transform image-related issues in a revolutionary manner. Unlike earlier approaches that necessitated human customization for particular applications, Conditional Generative Adversarial Networks (cGANs) automatically produce image outputs and negative impacts. Picture painting, object reconstruction from edge maps, and picture combining from a label map are just a few activities that may be executed using a unified framework. The notion has been tested and proven to work in various ways it doesn't need to be compared to other standards in any way to be implemented or embraced, many people on the internet, including artists, have played around with Pix2pix and its related apps since it was first released, making it more flexible and easier to use. As a major step forward in traditional image interpretation, this study eliminates the need for human map generation. So, there's no requirement for overlapping sessions devoted to particular objectives the community can get satisfactory answers for various image interpretation issues in this way. © 2024 IEEE.

关键词： Generative adversarial networks

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LiCT-Net: Lightweight Convolutional Transformer Network for Multiclass Breast Cancer Classification

LiCT-Net: Lightweight Convolutional Transformer Network for ...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Babita Nayak, Deepak Ranjan Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350350821

Classification of multiclass breast cancer through histopathological images is indispensable and poses daunting challenges due to color inconsistencies, high appearance variations, and large inter-class similarities. Regardless of the success of traditional convolutional neural networks (CNNs) and vision transformers (ViTs), they often fail to capture intricate patterns and local contextual information, respectively, while demanding high computational resources and data requirements. To mitigate these issues, this paper proposes a lightweight convo-lutional transformer network named LiCT-Net for multiclass breast cancer classification. The LiCT-Net introduces a local-global spatially-aware transformer layer and integrates it with a pre-trained FastViT model to effectively capture global and local contextual features, thereby facilitating learning fine-grained and intricate lesion patterns from histopathological images. The LiCT-Net is validated on a benchmark dataset and the experimental results and comparative analysis demonstrate its effectiveness. In specific, it achieves a higher accuracy of 96.16 %, 95.62 %, 95.25 %, and 94.21 % on 40×, 100×, 200×, and 400× magniflcations respectively, © 2024 IEEE.

关键词： Convolution

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Big Data Application in Cancer Classification by Analysis of RNA-seq Gene Expression 3

Big Data Application in Cancer Classification by Analysis of...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Amruth, A. Ramanan, R. Paul, Rhea Vishal, S. Saravanan, S. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita School of Computing Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350338607

computer science domain has got massive developments in the past decade, especially in the field of Big Data and Artificial Intelligence. Many industries are adopting these technologies to improve their products and services. Healthcare is one such industry which comes across voluminous data. Big Data platform is used to process the data and subsequently AI models can be used for prediction or classification. This paper focuses on proposing a Spark based pipeline to reduce the time required to process the voluminous data. With the aid of this new pipeline, we classify the type of cancer through analysis of RNA-seq gene expression levels. The Spark-based pipeline is run on default standalone mode and on a cluster. The study recorded that the Spark-based pipeline run on cluster mode improved the performance approximately by 11 times (regarding processing costs) when compared to the performance of Python-based pipeline. © 2023 IEEE.

关键词： Diseases

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A Deep Learning Approach to Enhance Air Quality Prediction: Comparative Analysis of LSTM, LSTM with Attention Mechanism and BiLSTM

A Deep Learning Approach to Enhance Air Quality Prediction: ...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Bhardwaj, Deepanshu Ragiri, Prakash Rao Netaji Subhas University of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350364866

In this study, we address the challenge of predicting air quality indices (AQIs) by leveraging Long Short-Term Memory (LSTM) networks. Recognizing the dynamic nature of air quality data, we explore the efficacy of three LSTM variants - standard LSTM, LSTM with an attention mechanism, and Bidirectional LSTM (BiLSTM) - in capturing temporal dependencies and improving forecast accuracy. Our methodology involved preprocessing a comprehensive dataset of air quality measures, followed by an experimental design that tested each model variant under different sequence lengths (30 and 365 days). The models were evaluated based on their mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) across three response variables: Nitrogen Oxides (NO), Particulate Matter up to 10 micrometers in size (PM10), and Particulate Matter up to 2.5 micrometers in size (PM2.5). Notably, when comparing the RMSE values across the three models, the LSTM with the attention mechanism consistently outperformed the standard LSTM and BiLSTM models, particularly with a sequence length of 30 days, indicating a superior ability to capture the intricacies of temporal dependencies in environmental data. Moreover, the shorter sequence length generally yielded better predictive performance, suggesting that longer sequences might introduce unnecessary complexity or overfitting. These indications of our study suggest a promising direction for the deployment of attention-based models in real-time AQI prediction systems, which could enhance the accuracy of environmental forecasts and inform public health decisions more effectively. Future work will explore the integration of additional environmental factors, the refinement of model frameworks, and the expansion of the dataset to include multi-city comparisons. © 2024 IEEE.

关键词： Mean square error

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Using ChatGPT in Undergraduate computer science and Software engineering Courses: A Students' Perspective 54

Using ChatGPT in Undergraduate Computer Science and Software...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Andersen-Kiel, Noah Linos, Panagiotis Butler University Computer Science and Software Engineering Dept. IndianapolisIN United States

ISBN: (纸本)9798350351507

This innovative practice full paper presents an empirical study aimed at evaluating the potential of ChatGPT, an advanced AI-driven chatbot, as a supplementary educational tool in undergraduate computer science and Software engineering (CSSE) courses. The study, initiated in the summer of 2023, focused on assessing ChatGPT's capabilities in generating accurate and complete computer code, identifying and rectifying code defects (bugs), and its scalability in handling larger programs. To achieve this, we conducted a series of experiments with ChatGPT. In one experiment, we introduced bugs into small programs from introductory CSSE courses. ChatGPT was tasked with detecting these defects and providing recommendations for fixing them. We evaluated ChatGPT's effectiveness in bug detection, the quality of its recommendations, and the completeness of the proposed solutions. We sought answers to questions such as whether ChatGPT found all injected defects, provided appropriate recommendations, and delivered high-quality solutions based on criteria like code completeness, size, complexity, and readability. In another experiment, ChatGPT was asked to generate code for assignments from previous CSSE courses, including Intro to computer science and Programming in C++, Intro to Python Programming, and Object-Oriented Programming and Data Structures using Java. We assessed the generated code's correctness and quality in comparison to student-written code. Similarly, in a third experiment, we evaluated ChatGPT's ability to generate larger programs using requirement specifications from an upper-division CSSE course on Agile Software engineering. Analyzing both qualitative and quantitative data from these experiments during the summer, we determined that ChatGPT showed promise as an educational tool. Consequently, we developed a plan to integrate ChatGPT into select CSSE courses for the fall semester of 2023. Specifically, ChatGPT was integrated into two of our introductory CSSE cou

关键词： Object oriented programming

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Improving Time Series Forecasting Accuracy with Transformers: A Comprehensive Analysis with Explainability 3

Improving Time Series Forecasting Accuracy with Transformers...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Joseph, Susmera Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science Engineering Kottayam India

ISBN: (纸本)9798350369083

Time series forecasting is crucial in numerous sectors, including healthcare, energy, and finance. Transformer models, initially designed for natural language processing applications, demonstrate potential in capturing temporal dependencies within sequential data. In this paper, the application of transformer models in time-series forecasting is explored, and their performance and interpretability are investigated. Transparent models build trust and facilitate decision-making, especially in industries with ethical guidelines. While inherently interpretable time-series Transformers exist, there is a gap in research regarding post hoc methods, which is an area ripe for exploration. Understanding and comparing complex Transformer models for time series forecasting can benefit from advancements in explainability, ensuring accurate and comprehensible predictions. In comparison to five other transformer-based models and classical models, the Temporal Fusion Transformer (TFT) model outperformed them in short-term forecasting. Further enhancement of the model performance was achieved through a meticulous analysis of the Encoder-Decoder's importance, which contributed significantly to refining the forecasting accuracy. The vanilla transformer was explained using permutation feature importance. The model stability was analyzed using perturbation methods. © 2024 IEEE.

关键词： Spatio-temporal data

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