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Platform Construction to Protect Portraits of Others

Platform Construction to Protect Portraits of Others

引用

2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023

作者： Cheng, Hui-Jun Zeng, Sheng-Fu Lin, Chun-Yuan Minth University of Science and Technology Dept. of Artificial Intelligence Hsinchu County Taiwan Chang Gung University Dept. of Computer Science and Information Engineering Taoyuan City Taiwan Asia University Dept. of Computer Science and Information Engineering Taoyuan City Taiwan

ISBN: (纸本)9798350302561

As the times change, online video activities and video sharing have become commonplace. However, publicly sharing the images of others can involve risks of violating privacy and portrait rights, especially in today's era of widespread high-resolution cameras that can capture detailed facial features. Therefore, protecting the portrait rights of others has become crucial. In order to balance the protection of portrait and the preservation of image quality, we propose a deep learning-based person tracking system. This system can record and track the time and location of people in videos or video streams. Through image synthesis techniques, we can either place the necessary people in virtual environments or hide people in real videos. In this way, we ensure the portrait and privacy of others while maintaining the authenticity and quality of the image. This approach is not only applicable for individuals protecting their own images but can also be applied to respect the privacy of others, making online video activities safer and more respectful of people rights. © 2023 IEEE.

关键词： Deep learning

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Implementing a Dynamic-Static Hybrid Fog Computing System for Content Sharing on Local Networks

Implementing a Dynamic-Static Hybrid Fog Computing System fo...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Sugawara, Shinji Chiba Institute of Technology Faculty of Engineering Dept. Information and Communication Systems Engineering Narashino Japan

ISBN: (纸本)9798350324136

We have so far proposed a method for configuring a virtual fog server using mobile user terminals that originally belong to the edge in an information network system, making a three-layer cloud-fog-edge structure logically to give a content sharing service. This method is called dynamic fog. In this method, in each area in a city where a large number of users gather, multiple user terminals work together and dynamically form a fog server, switching roles around each other over time. In this paper, as a step before implementing a complete dynamic fog for content sharing, we describe the design for implementing a dynamic-static hybrid fog. In that fog system, a static, simple terminal, which works as a core of a dynamic fog, is set in the place where users can easily gather and it bundles multiple user terminals to make a fog function. © 2024 IEEE.

关键词： cloud-fog-edge structure content sharing dynamic fog peer-To-peer network

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Revolutionizing Healthcare with IoT: Integrated Monitoring of BMI and ECG

Revolutionizing Healthcare with IoT: Integrated Monitoring o...

引用

2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Srinivas, M.G.C. Sangeetha, Y. Vinay, P Sai Kumar, S Tharun V R Siddhartha Engineering College Dept. of Information Technology Vijayawada India

ISBN: (纸本)9798350381887

People lacking awareness of the importance of BMI may not acknowledge the potential health hazards associated with being underweight, overweight, or obese. Insufficient awareness may result in neglecting potential concerns, such as cardiovascular diseases and diabetes, commonly associated with BMI. Without knowledge of ECG and its role in monitoring heart health, individuals may not be aware of early signs of cardiovascular problems. Delayed detection of irregular heart patterns or abnormalities may increase the risk of heart disease or other cardiac-related complications. This project combines hardware components with software development to create a holistic health monitoring system that promotes user awareness and engagement. © 2024 IEEE.

关键词： Heart

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Performance Investigation of Customized MFCC Feature Extraction in Recognizing Indonesian Conversational Emotion 16

Performance Investigation of Customized MFCC Feature Extract...

引用

16th International Conference on information technology and Electrical engineering, ICITEE 2024

作者： Halimah, Noor Siti Hidayat, Risanuri Mustika, I Wayan Gadjah Mada University Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350375817

Modifying the MFCC extraction process is an opportunity to improve Indonesian SER performance. This work confirms the performance of MFCC features obtained by customized MFCC extraction. This paper utilizes customized MFCC features with single-layer LSTM as a classifier model. MFCC was customized by providing values of pre-emphasis of 0.97, a frame length of 0.025 seconds with an overlap of 0.01 seconds, FFT points of 512, a mel-filter of 40, and 13 MFCCs. The result shows better performance in single-layer LSTM compared with SVM using PCA, which results in a training accuracy of 0.789 and a testing accuracy of 0.787. K-fold cross-validation was confirmed with an average accuracy of 0.816 using k value 5 and 0.879 with k value 10. © 2024 IEEE.

关键词： Emotion Recognition

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LEARNING MEAN FIELD GAMES ON SPARSE GRAPHS: A HYBRID GRAPHEX APPROACH 12

LEARNING MEAN FIELD GAMES ON SPARSE GRAPHS: A HYBRID GRAPHEX...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Fabian, Christian Cui, Kai Koeppl, Heinz Dept. of Electrical Engineering and Information Technology Technische Universität Darmstadt Germany

Learning the behavior of large agent populations is an important task for numerous research areas. Although the field of multi-agent reinforcement learning (MARL) has made significant progress towards solving these systems, solutions for many agents often remain computationally infeasible and lack theoretical guarantees. Mean Field Games (MFGs) address both of these issues and can be extended to Graphon MFGs (GMFGs) to include network structures between agents. Despite their merits, the real world applicability of GMFGs is limited by the fact that graphons only capture dense graphs. Since most empirically observed networks show some degree of sparsity, such as power law graphs, the GMFG framework is insufficient for capturing these network topologies. Thus, we introduce the novel concept of Graphex MFGs (GXMFGs) which builds on the graph theoretical concept of graphexes. Graphexes are the limiting objects to sparse graph sequences that also have other desirable features such as the small world property. Learning equilibria in these games is challenging due to the rich and sparse structure of the underlying graphs. To tackle these challenges, we design a new learning algorithm tailored to the GXMFG setup. This hybrid graphex learning approach leverages that the system mainly consists of a highly connected core and a sparse periphery. After defining the system and providing a theoretical analysis, we state our learning approach and demonstrate its learning capabilities on both synthetic graphs and real-world networks. This comparison shows that our GXMFG learning algorithm successfully extends MFGs to a highly relevant class of hard, realistic learning problems that are not accurately addressed by current MARL and MFG methods. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Learning algorithms

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Predict of the Hidden Drug Interactions Based on Similarity Utilizing the Knowledge Graph

Predict of the Hidden Drug Interactions Based on Similarity ...

引用

2024 International Conference on computer and Applications, ICCA 2024

作者： Elfiky, Mariam Gamal Ghoniemy, Samy Eldin, Ahmed Sharaf University of Hertfordshire hosted by Gaf Dept. of Information Technology Management for Business Cairo Egypt Galala University Faculty of Computer Science and Engineering Cairo Egypt Sinai University Faculty of Information Technology and Computer Science Cairo Egypt

ISBN: (纸本)9798350367560

Drug-drug interactions (DDIs) can significantly impact treatment outcomes by causing adverse effects or reducing therapeutic efficacy. Whereas many DDIs are already known, there are numerous hidden interactions. Therefore, this study tries to predict unknown interactions based on drug structure and target information from the Drug Bank dataset. This paper trains the random forest model integrated with a knowledge graph on a dataset containing 922 unique drugs and 731,196 drug interactions. To illustrate the complex interactions between these drugs, we created a knowledge graph with Neo4j. This graph integrates information gathered from the dataset and extracts relevant data using Cypher queries. The inference engine was created according to the model's predictions on unknown DDIs. The random forest model, with an F1-score of 0.82, achieved a recall of 0.99 and a precision of 0.70. Also, 34,580 potential DDIs were extracted from the sample dataset of drugs. By embedding the knowledge network, our inference engine significantly enhanced predicting the hidden interactions and was found strong in the balance of precision and recall. Our research is applicable to the enhancement of drug safety and effectiveness via mitigating potential DDIs by identifying them. © 2024 IEEE.

关键词： Knowledge graph

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AI-Powered Diabetes Management: Reliable Insulin Dosing through Machine Learning 5

AI-Powered Diabetes Management: Reliable Insulin Dosing thro...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Lakshmi, G. Jamini Kalyani, G. Vamsikrishna, J. Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Vijayawada India

ISBN: (纸本)9798331504403

This project focuses on creating a smart system utilizing machine learning for diabetes prediction and precise insulin dosage recommendations. By looking at various factors such as blood sugar levels, body mass, and insulin intake etc. the system can calculate the right amount of insulin needed for a patient. This helps in managing diabetes more effectively, reducing the chances of dangerous blood sugar highs or drops. The system is trained on a large dataset of patient information to learn patterns and make reliable predictions. The objective is to promote better living for individuals with diabetes by offering personalized and reliable insulin dosage suggestions, thereby reducing the risk of complications related to incorrect insulin administration. Ultimately, this technology aims to improve the quality of life for people with diabetes by providing personalized and accurate insulin dosage recommendations. © 2024 IEEE.

关键词： Diabetes management Diabetes prediction Healthcare technology Insulin dosage recommendation Quality of life Smart system

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Facial Recognition Authentication in Electoral Processes using Raspberry Pi 5

Facial Recognition Authentication in Electoral Processes usi...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kuppa, Datta Sai Abdul, Raasheed Mande, Ramesh Kumbha, Jagadeesh Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Vijayawada India

ISBN: (纸本)9798350379945

Elections serve as the basis of democratic governance by offering people the power to elect their Delegates. Upholding the integrity and trust worthiness of these election procedures is vital in preserving democratic values. This work is dedicated to reinforcing the trust through the implementation of progressive technology to safeguard the duplicity of the voting process. The initiative focuses on bolstering the security measures within electoral systems by harnessing advanced facial recognition technology. Leveraging Raspberry Pi a compact computing solution paired with cameras, the aim is to authenticate voters' identities accurately. To achieve this, we utilized the FaceNet model from DeepFace library, which in comparison with other models, was found to have a greater accuracy of 99.63%. Facial recognition integration is a proactive measure to deter fraudulent practices, particularly voter impersonation, during election processes. The primary objective is to establish an electoral system characterized by security and trustworthiness. Facial recognition technology in this context seeks to validate each vote's legitimacy and ensure it corresponds to the rightful voter. This approach aims to instill confidence in the electing process, providing a fair-minded and dependable system aligned with democratic principles of honesty and transparency. © 2024 IEEE.

关键词： Face recognition

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Carrer Compass: A Career Path Recommender using Machine Learning 5

Carrer Compass: A Career Path Recommender using Machine Lear...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Guntupalli, Umesh Guru Sai Pandala, Madhavi Latha Veeranki, Dharma Teja Kumbha, Pramodhini Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Kanuru India

ISBN: (纸本)9798350379945

In the ever-expanding corporate realm, making an appropriate career choice is crucial for building a successful career. Students often face confusion due to increased choices or lack of guidance, leading to missteps in career selection. Such decisions might result in individuals working in fields unsuited to their strengths and interests, reducing overall efficiency. The proposed project highlights the need for a personalized career prediction system. Traditional methods often fall short in capturing the interests and strengths of individuals while solely depending on standard tests. This research study focuses on building a personalized career recommendation system based on the skills and interests of an individual. The project promotes the idea of dynamic and tailor-made tests for identifying suitable careers and an interactive, user-friendly mobile application using the Flutter framework. Machine Learning models like Random Forest and Support Vector Machine are employed for predicting and recommending careers. By evaluating personality traits, interests, and skills, the application will recommend a career. Continuous improvement is fostered through user feedback, ensuring the model's adaptability to changing job markets. This approach provides a comprehensive solution to challenges faced by individuals while choosing a career. © 2024 IEEE.

关键词： Adversarial machine learning

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LEARNING DECENTRALIZED PARTIALLY OBSERVABLE MEAN FIELD CONTROL FOR ARTIFICIAL COLLECTIVE BEHAVIOR 12

LEARNING DECENTRALIZED PARTIALLY OBSERVABLE MEAN FIELD CONTR...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Cui, Kai Hauck, Sascha Fabian, Christian Koeppl, Heinz Dept. of Electrical Engineering and Information Technology Technische Universität Darmstadt Germany

Recent reinforcement learning (RL) methods have achieved success in various domains. However, multi-agent RL (MARL) remains a challenge in terms of decentralization, partial observability and scalability to many agents. Meanwhile, collective behavior requires resolution of the aforementioned challenges, and remains of importance to many state-of-the-art applications such as active matter physics, self-organizing systems, opinion dynamics, and biological or robotic swarms. Here, MARL via mean field control (MFC) offers a potential solution to scalability, but fails to consider decentralized and partially observable systems. In this paper, we enable decentralized behavior of agents under partial information by proposing novel models for decentralized partially observable MFC (Dec-POMFC), a broad class of problems with permutation-invariant agents allowing for reduction to tractable single-agent Markov decision processes (MDP) with single-agent RL solution. We provide rigorous theoretical results, including a dynamic programming principle, together with optimality guarantees for Dec-POMFC solutions applied to finite swarms of interest. Algorithmically, we propose Dec-POMFC-based policy gradient methods for MARL via centralized training and decentralized execution, together with policy gradient approximation guarantees. In addition, we improve upon state-of-the-art histogram-based MFC by kernel methods, which is of separate interest also for fully observable MFC. We evaluate numerically on representative collective behavior tasks such as adapted Kuramoto and Vicsek swarming models, being on par with state-of-the-art MARL. Overall, our framework takes a step towards RL-based engineering of artificial collective behavior via MFC. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Markov processes

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