Modern educational environments require effective and efficient systems to track attendance and participation to ensure better learning outcomes and increased productivity. Traditional systems often mark attendance au...
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ISBN:
(数字)9798331519582
ISBN:
(纸本)9798331519599
Modern educational environments require effective and efficient systems to track attendance and participation to ensure better learning outcomes and increased productivity. Traditional systems often mark attendance automatically, regardless of the level of student engagement. This paper introduces a novel system, “Revolutionizing Classroom Engagement with Face Recognition and Attention-Based Attendance,” designed to detect multiple faces in real time and automate the process of attendance marking based on students' attention levels. In contrast to traditional methods, attendance is only recorded when students surpass a predefined attention threshold (e.g. 75%) based on their focus during the lecture. This approach fosters a more dynamic, interactive, and focused learning environment. The proposed system leverages advanced face detection and recognition techniques, integrating Haar Cascade Classifiers, Deep Learning-based Face Detection, and K-Nearest Neighbors (KNN) to offer robust and accurate identification even in large, diverse classrooms. Real-time video processing is handled by OpenCV, which captures and analyzes classroom footage, while NumPy processes complex numerical computations for image data. Pandas is utilized for efficient attendance logging, storing data in easily accessible CSV files. The system's attention-tracking feature is another key innovation, as it analyzes students' gaze and behavioral cues to assess their level of engagement. This ensures that attendance is only recorded when students are genuinely focused and attentive. Designed to be scalable and non-intrusive the system can be adapted to classrooms of varying sizes and is easily incorporated into existing educational frameworks. By providing accurate attendance tracking and engagement analysis, the system not only simplifies administrative tasks but also contributes to fostering a smarter, more engaging, and more productive classroom environment.
Software-defined vehicles (SDVs) are an emerging paradigm in the automotive industry where vehicles’ functionality, performance, and safety can be enhanced and updated through software, even after production. Unlike ...
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ISBN:
(数字)9798331531195
ISBN:
(纸本)9798331531201
Software-defined vehicles (SDVs) are an emerging paradigm in the automotive industry where vehicles’ functionality, performance, and safety can be enhanced and updated through software, even after production. Unlike traditional vehicles, which rely primarily on physical components, SDVs leverage advanced connectivity, real-time data analytics, and cloud integration to adapt to changing regulations, driver preferences, and environmental conditions. This shift enables vehicles to evolve continuously, responding to new technological advances and customer needs. In this paper, we propose an end-to-end framework to demonstrate and realize the full benefits of SDVs. We incorporate the concept of digital twin (of the vehicle) driven software update authorization to download or update an application on the vehicle. In our framework, attestation refers to the verification of each software update’s compatibility and functionality with the vehicle’s current ECUs before deployment. Any impending/requested update is first verified for its compatibility with the vehicle’s architecture (as per the twin) on the cloud. The idea is via the SDVs’ digital twin—since each and every ECU is virtualized —the simulation and testing of the actual hardware setup with the new application software can be done without direct impact on the physical vehicle. Once the application is installed across all relevant virtualized ECUs, the framework confirms compatibility, ensuring smooth deployment and functionality in the real-world vehicle. Post this successful attestation, the installation will be done on the real vehicle corresponding to that digital twin. Through this proposed framework, we intend to ensure the safety of the updation, while the new updates contribute to the functionality and performance improvements.
Fast shipping and efficient routing are key problems of modern logistics. Building on previous studies that address package delivery from a source node to a destination within a graph using multiple agents (such as ve...
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Disaggregating the prefill and decoding phases represents an effective new paradigm for generative inference of large language models (LLM), which eliminates prefill-decoding interference and optimizes resource alloca...
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In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by ...
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In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.
The technique of video watermarking performs a critical part in ensuring protection of digital video content by embedding an imperceptible yet detectable mark into the video stream. This paper explores both the qualit...
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ISBN:
(数字)9798331542375
ISBN:
(纸本)9798331542382
The technique of video watermarking performs a critical part in ensuring protection of digital video content by embedding an imperceptible yet detectable mark into the video stream. This paper explores both the qualitative and quantitative aspects of various video watermarking techniques. The study categorizes watermarking methods into spatial domain and frequency domain techniques, providing a comprehensive comparison of their effectiveness regarding visual being imperceptible and resilient to attacks, computational complexity, along with embedding capacity. Important quantitative evaluation measures are provided, such as PSNR(Peak Signal to Noise Ratio), Bit Error Rate(BER), and Structural Similarity Index(SSIM).
A world model is essential for an agent to predict the future and plan in domains such as autonomous driving and robotics. To achieve this, recent advancements have focused on video generation, which has gained signif...
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Eye fundus conditions are dangerous and can cause significant visual impairment if not detected early. Diabetic retinopathy, cataracts, and glaucoma are among the conditions for which manual assessment is directly imp...
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In corporate settings, conferences, or classrooms, an orator relies on manual slide transitions, which can disrupt their presentation flow. Presentation devices can be inaccessible due to the physical limitations of i...
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ISBN:
(数字)9798331531195
ISBN:
(纸本)9798331531201
In corporate settings, conferences, or classrooms, an orator relies on manual slide transitions, which can disrupt their presentation flow. Presentation devices can be inaccessible due to the physical limitations of individuals, reducing engagement with the audience. Traditionally used remote-controlled wireless presenters are hand-held devices that usually require a compatible connector slot for the presentation device and partially engage one hand of the orator. To ease the hand-free presentation of slides, we introduce SlidEar, a voice-assisted smart slide supervision successfully tested on eSense in-ear wearables. SlidEar leverages voice recognition fusion with wearable sensors to facilitate seamless slide operations. It allows real-time feedback on natural language commands during the presentation of slides. Compared with conventional methods, SlidEar enhances engagement and accessibility through hand-free in-ear placement and helps the orator focus on delivering their content without any disruption or distraction. The evaluation shows promising results that improve the presentation experience profusion. SlidEar significantly reduces errors and can be widely adopted in diverse presentation scenarios.
As short text data in native languages like Hindi increasingly appear in modern media, robust methods for topic modeling on such data have gained importance. This study investigates the performance of BERTopic in mode...
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