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Meta-Adaptation Goals: Leveraging Feedback Loop Requirements for Effective Self-Adaptation 5

Meta-Adaptation Goals: Leveraging Feedback Loop Requirements...

引用

5th IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2024

作者： Groner, Raffaela Caldas, Ricardo Diniz Wohlrab, Rebekka Chalmers | University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798350389760

Self-∗ systems focus on optimizing systems regarding adaptation goals, e.g. availability, that relate to properties of the managed system. While these adaptation goals are important, the feedback loop must also be considered. It is problematic if systems adapt too often, make too time-consuming changes that involve insecure transition states, or react too slowly when planning adaptations. Current approaches do not allow to enforce requirements on the adaptation loop - including sensitivity, sta-bility, and transition states. When planning adaptations, those meta-adaptation goals need to be considered and potentially traded off against traditional adaptation goals. Such a tradeoff might entail, for instance, that it may be worth it to adapt a system less frequently, although the overall availability of the system could be higher with more adaptations. We propose to explicitly consider feedback loop requirements for self-adaptive systems. Concretely, we propose using machine learning to predict quality measures based on the state of the feedback loop and the managed system. This information is used to plan for better adaptations that consider the adaptation goals and the meta-adaptation goals of the feedback loop. The explicit representation of meta-adaptation goals and adaptation goals enables the optimization of adaptation strategies, tradeoff analysis, and evaluation of adaptations. © 2024 IEEE.

关键词： Self-supervised learning

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TotCareBot: A Comprehensive Approach Towards Enhancing Early Childhood Education through Robotics 5

TotCareBot: A Comprehensive Approach Towards Enhancing Early...

引用

5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Chougala, Manjula R Sushma, B. Bhayyar, Akshata S Bhandarkar, Abhay Pavithra, P.N. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331528539

Robots have emerged as versatile tools with significant potential to enhance teaching and learning environments, including classrooms, laboratories, play homes, crèches, and even at home. Their engaging nature can captivate young children, boosting motivation and interest. This paper proposes a methodology for an Educational Robot (ER) designed for play homes. The ER is capable of moving around the classroom, monitoring children, analyzing their emotions, responding appropriately, and alerting the teacher when necessary. The proposed system aims to leverage the positive impact of expressive behaviors in robots, such as storytelling and pampering. Our findings indicate that expressive robot narration is as effective as that of a human displaying similar behaviors and even surpasses a static, inexpressive human narrator. Furthermore, the robot excels in conveying educational content, although its limited emotional expression slightly reduces its effectiveness in narrating emotionally rich stories. This research underscores the potential of ERs to create dynamic and interactive learning experiences for young children, fostering both their cognitive and emotional development. © 2024 IEEE.

关键词： Educational robots

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Performance Comparison of Overlapping Sound Event Detection Methods 14

Performance Comparison of Overlapping Sound Event Detection ...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Tyagi, Akshay Singh, Kartikeya S Jaswanth Kumar, P.V. Garg, Prof. Rakesh Yousef Abdullah, Mohammed Edres Amity University Dept. of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350344837

Sound event detection is pivotal in various applications, including environmental monitoring and surveillance systems, enhancing situational awareness and response strategies. This paper investigates the intricacies of overlapping sound events, where multiple events occur concurrently, creating complexities in accurate detection. Traditional methods falter when confronted with this challenge, necessitating advanced approaches. This research pursues two primary objectives: comprehensive review of existing overlapping sound event detection methodologies and exploration of innovative solutions to enhance accuracy and robustness. Through meticulous analysis of prior research, this paper offers insights into field progress and identifies unexplored areas. Subsequent sections dissect existing methodologies, evaluating their strengths and limitations, and addressing challenges in common evaluation metrics and datasets. This paper contributes to the advancement of sound event detection technology, particularly in complex audio environments, promoting better solutions for real-world applications. © 2024 IEEE.

关键词： Pattern recognition

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Vehicle Detection and Speed Estimation Using Deep Learning 6

Vehicle Detection and Speed Estimation Using Deep Learning

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Mishra, Vishnu Kumar Mishra, Megha Tiwari, Rajesh Kumar, T. Ram Diwan, Tarun Dhar Patra, Raj Kumar Malla Reddy Engineering college for Women Dept. of Computer Science and Engineering Hyderabad India Csvtu Shri Shankaracharya Technical Campus Department of Computer Science and Engineering Bhilai India CMR Engineering College Department of Computer Science and Engineering Hyderabad India Atal Bihari Vajpayee University Dept. of Computer Science and CoE Chhattisgarh Bilaspur India

ISBN: (纸本)9798331505790

The use of deep learning algorithms for vehicle detection and speed estimate in traffic surveillance systems is investigated in this research study. Convolutional Neural Networks (CNNs) are the main tool used in this study to solve the problems related to precise and timely monitoring of vehicle movement. By improving the effectiveness and dependability of current systems, the suggested approach hopes to promote traffic management for smart transportation. © 2024 IEEE.

关键词： Convolutional neural networks

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Integration of Swarm Intelligence with KNN for Optimal Nearest Neighbors Value Prediction 9

Integration of Swarm Intelligence with KNN for Optimal Neare...

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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Anchalia, Aniketh Kakati, Anirudh Patil, Ambika Bhavana, B.N. Paudel, Ankit Chaudhari, Shilpa Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334395

K-Nearest Neighbors (KNN), a simple and widely used algorithm, is extremely valuable in the field of machine learning models. Finding an optimal value of the nearest neighbor parameter in the KNN algorithm has been a problem that has existed for quite some time. In recent years, nature inspired algorithms have been widely emerging, with scope of being utilized in various engineering problems. This work proposes integrating a swarm intelligence optimization technique, the Sparrow Search Algorithm (SSA), for optimizing the process of finding K. The proposed hybrid model has been tested against four datasets of varying sizes and the results depict improvement in efficiency. The improvement is much larger as the size of the dataset increases, with around a 99% improvement in efficiency for the largest dataset tested on. KNN is also used to preprocess two of the datasets which have missing values, which result in improved accuracies of prediction. © 2023 IEEE.

关键词： Swarm intelligence

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NextGen Dynamic Video Generator using AI 9

NextGen Dynamic Video Generator using AI

引用

9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Anitha, R. Kishore, N. Vijay Anand, M. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384208

In an era marked by remarkable technological advancements, the way we create and share information has undergone a profound transformation. This paradigm shift is epitomized by the NextGen Dynamic Video Generator using AI, a cutting-edge tool that seamlessly integrates artificial intelligence with content creation. What sets this tool apart is the customizability, which allows the users to fine-tune every aspect of the tutorials. Users could tune creative elements like humor, the dept. of explanation, character appearance, and voice to tailor tutorials to precise specifications. The core functionality of the tool revolves around script generation, the Cohere's language, laying the groundwork for the tutorial's content. Furthermore, seamless integration with Edge TTS ensures that the generated scripts are delivered with utmost clarity and engagement, enhancing the overall learning experience. Character animation is powered by SadTalker, adding a dynamic and captivating dimension to these tutorials. This animated character serves as a relatable guide, facilitating a deeper connection between the content and the audience. The tool also seamlessly integrates relevant and eye-catching images from Google, which are incorporated into the presentation slides. The workflow is a well-orchestrated process involving script generation, audio dialogue creation, image retrieval, video generation, and the seamless fusion of character animations and slides. The resulting video tutorials are not only comprehensive but also engaging and ready to be shared as valuable educational resources. © 2023 IEEE.

关键词： Image retrieval

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Comparing Developmental Approaches for Game-Based Learning in Cyber-Security Campaigns 1

Comparing Developmental Approaches for Game-Based Learning i...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Sreehari, S. Gokulapriya, R. Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Digital game-based learning (DGBL) has been viewed as an effective teaching strategy that encourages students to pick up and learn a subject. This paper explores its viability to help increase the reach and efficiency of the existing cybersecurity awareness spreading campaigns that find adolescent students as their demographic. This work intends to reinforce the benefits of multimedia learning in schools and universities with the use of video games and further find the ideal type and genre of game that can be developed to spread awareness about cybersecurity to students in grades 8th to 12th (tailored towards the Indian context). Game genres were compared on the basis of having a simple gameplay loop, being easy for instructors to train themselves in, being inclusive to special needs children, being able to be published as an independent title, and having very low hardware specification requirements. Ideally, the paper proposes that this game would be a single-player experience that would follow a game-based learning approach to maximize the game's reach. Once identified, the model of the game was assessed using already existing implementations. Finally, the ideal model, a single-player visual novel is proposed. A future iteration of the paper will implement the proposed model of game design and perform an analysis of the effects the video game had on the learning experience of the students surveyed. © 2023 IEEE.

关键词： Students

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration 1

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Saklani, Sumit Manchanda, Mahesh Sharma, Raman Singh, Devendra Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Application Dehradun India

ISBN: (纸本)9798331518578

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments. © 2025 IEEE.

关键词： Decision making

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An Efficient Natural Merge Sort Incorporating Array Decomposition 26

An Efficient Natural Merge Sort Incorporating Array Decompos...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Gharami, Kanchon Rashid, Kazi Mehrab Rabbi, Golam Amin, Farhana Shatabda, Swakkhar Shovan, S.M. Mehedi Hasan, Md. Al Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh United International University Dept. of Computer Science & Engineering Dhaka Bangladesh Missouri University of Science & Technology Dept. of Computer Science & Engineering Missouri United States

ISBN: (纸本)9798350359015

Modifying an algorithm that has been established over many years and making it even faster has always been a fascinating and challenging area in the field of algorithms, which motivated us to take the challenge of improving the performance of Knuth's NaturalMergeSort by reducing the runtime with considering both ascending and descending runs. The way we optimize it is by taking advantage of both ascending and descending runs, i.e., increasing the potential of the decomposition method compared to the existing algorithm. The proposed algorithm was implemented in C++, and the experiment was conducted with some random and manually prepared datasets that resulted in improving the worst case of NaturalMergeSort by an exceedingly large margin of 97.5%, demonstrating the efficiency and flexibility of our algorithm. Even for the average case, our proposed algorithm beats Knuth's NaturalMergeSort by a slight margin, and it also outperforms traditional merge sort with 17.5% improvements. The performance and efficiency of our algorithm have been recorded and presented in graphical form by comparing time and space complexity with other competitor sorting algorithms. © 2023 IEEE.

关键词： MergeSort NaturalMergeSort Sorting Sorting Algorithm

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Identification of Affective States from Malayalam Audios Based on Valence Arousal Prediction

Identification of Affective States from Malayalam Audios Bas...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： George, Talit Sara George, Jini Pournami, P.N. National Institute of Technology Dept. of Computer Science and Engg. Calicut India Providence College of Engineering Dept. of Computer Science and Engg. Chengannur India

ISBN: (纸本)9798331540067

Affective state plays a key role in emotion recognition, influencing various applications from mental health evaluation to human-machine interaction. These states can be described using two primary aspects: valence, which indicates the pleasantness of the speech, and arousal, which relates to the intensity of the emotion. This study aims to automate the prediction of valence and arousal values from speech data, addressing the limitations of manual annotation and human error. The IIITSAINT-EMOMDB dataset, created from Malayalam films, uses the Circumplex Model of Affect to map emotions such as happy, sad, angry, and calm. The approach leverages machine learning models, including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), for prediction. Experimental results demonstrate that the traditional models like SVR and ANN are outperformed by the CNN model, which achieved a mean squared error (MSE) of 0.3 for valence and 0.1 for arousal. These findings highlight the power of automated emotion recognition in large-scale audio datasets, offering promising applications in affective computing and psychological analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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