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Electronic Technology, Communication and Information (ICETCI), IEEE International Conference on

作者： Hongsheng Wang College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

In view of the existing security products limited use scenarios, deployment and use is not flexible, limited performance and other problems, design and implementation of intelligent video surveillance system based on face recognition technology. The key technology of the system, facial recognition, is deeply studied. The system includes: real-time alarm system, intelligent access control system, intelligent inspection system and so on. Real-time early warning of illegal behaviors can accurately identify the facial features of violations and push them to the relevant departments for early warning. Through intelligent access control, the security protection of the face is realized. The system can collect and mark the information in real time through the monitoring device set in the patrol area, thus greatly improving the efficiency of patrol. Experiments show that this method has high performance in face detection and recognition. It can well solve the safety protection requirements of large venues, theaters, warehouses and other large venues.

关键词： Access control Accuracy Face recognition Streaming media Video surveillance Real-time systems Servers

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Analyzing technical debt management in an industrial project: diffuseness and actual remediation time of SonarQube issues

Analyzing technical debt management in an industrial project...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Giovanni Garifo Luca Maina Antonio Vetrò Marco Torchiano Department of Control and Computer Engineering Politecnico di Torino Torino Italy

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

Technical Debt (TD) affects nearly all software projects, with tools like SonarQube (SQ) used to detect and estimate its severity and remediation effort. This paper examines TD management in an Italian software company that developed a software product in the railway domain. We assess SQ accuracy in estimating remediation efforts by comparing it to actual developer remediation times. Results indicate that SQ generally overestimates remediation time for this closed-source code base.

关键词： Codes Accuracy Companies Software Information and communication technology

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CNFPRNet: Innovative Fusion of GPT-2 Language Model and Natural Language Generation Model for Personalized Educational Recommendations 6th

CNFPRNet: Innovative Fusion of GPT-2 Language Model and Natu...

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6th International Conference on computer Science and Educational Informatization, CSEI 2024

作者： Li, Chao Xu, Zixuan Jing, Weipeng College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

ISBN: (纸本)9789819637379

In this paper, we propose a novel AI-based model that combines a GPT-2 language model with a natural language generation component to generate Customized Narrative Fragments for Personalized Recommendations(CNFPR) in education. Our approach builds on the existing literature on AI-based learning content generation and learning pathway augmentation, but introduces a new component that leverages the power of GPT-2 and an innovative Conceptual Definition Extractor to generate high-quality natural language text. By integrating this component into our model, we aim to create a more personalized and engaging learning experience for students, one that is tailored to their individual needs and preferences. Experimental results show that compared with the Zero-shot evaluation with GPT-2, our CNFPRNet model demonstrates a 23.5% increase in ROUGE scores. This improves the accuracy of recommended content and enables teaching resources to better meet the individual needs of users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Report generators

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The Quest for Believability: Exploring FACS Adaptations for Emotion Facial Expressions in Virtual Humans

The Quest for Believability: Exploring FACS Adaptations for ...

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IEEE Games Media Entertainment (GEM)

作者： Stefano Calzolari Francesco Strada Andrea Bottino Control and Computer Engineering Department Politecnico di Torino Torino Italy

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

In interactive computer graphics, Facial Action Coding System (FACS) has been adapted to enhance the emotional expressiveness of Virtual Humans (VHs) by associating certain Action Units (AUs) with corresponding facial blendshapes. In this way, animators can (theoretically) recreate any human emotion on a VH's face with precision and flexibility. However, conveying realistic and believable emotional expressions with this approach comes with some challenges. In particular, given a set of AUs representing a particular emotion, it is not straightforward to define the correct set of blendshape weights that can render the same realistic and believable emotion on all VHs, as even small differences in weight values can drastically change the perceived emotion. This complexity raises several critical questions such as: is there for each emotion a universal set of blendshape weights that can effectively convey that emotion across all VHs? How can this set be found? If such a universal set proves elusive, can optimal combinations be identified for specific subgroups based on specific facial features such as men and women? Answering these questions is critical to understanding the general applicability of FACS-based facial emotion coding, which allows designers and animators to easily develop VHs that are able to interact with users in a way that is both emotionally rich and authentic. This paper explores these issues through a preliminary investigation aimed at defining realistic representations of happiness.

关键词： Entertainment industry computer graphics Media Encoding Complexity theory Facial features Faces

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Comparative Analysis of Wearable and computer Vision-Based Methods for Human Torso Posture Estimation

Comparative Analysis of Wearable and Computer Vision-Based M...

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International Conference on Mathematics and computers in Sciences and in Industry (MCSI)

作者： Emanuel Muntean Monica Leba Department of System Control and Computer Engineering University of Petrosani Petrosani Romania

ISBN: (数字)9798350377224

ISBN: (纸本)9798350377231

This paper conducts a comparative analysis of human torso posture estimation methodologies, focusing on an inertial measurement unit (IMU) sensor coupled with an Arduino UNO as a wearable approach, and Kinect V2, utilizing OpenCV for posture analysis. The core objective of this study is to ascertain which method yields greater precision in the estimation of human torso posture. The IMU sensor, characterized by its wearability, provides the distinct advantage of unobtrusiveness and the capability to record motion across diverse environments. Conversely, Kinect V2 leverages computer vision techniques to derive posture estimations from visual data in real-time. Through comprehensive experimentation, this research evaluates the accuracy of both methodologies by juxtaposing their posture estimation outcomes. The findings of this investigation aim to significantly contribute to the enhancement of human posture estimation systems, with wide-ranging implications for health, sports, and ergonomics.

关键词： Torso Visualization Accuracy Measurement units Estimation Inertial navigation Sensor fusion Reliability Wearable devices Sports

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Research on lightweight algorithm for smoking detection based on improved YOLOv8

Research on lightweight algorithm for smoking detection base...

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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Guorui Zhou Xingjian Wang Northeast Forestry University College of Computer And Control Engineering Harbin China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

In response to the issues of high computation and large model parameters in current smoking detection algorithms, making them difficult to deploy on edge devices, this paper proposes an improved lightweight YOLOv8 algorithm for real-time detection and accurate recognition of smoking targets. Firstly, the C2f module in the backbone of YOLOv8 is replaced with a lightweight ContextGuided module, which enhances the accuracy of small object detection through the analysis of feature context, and also reduces model complexity. Secondly, a slim-neck structure paradigm is adopted to further optimize the complexity of the neck part without affecting accuracy. Experimental results show that the improved algorithm reduces the number of parameters, computation, and model size by 17.94%, 20.990%, and 15.78 % , respectively, with mAP@0.5 and FPS reaching 94.9% and 98. This ensures real-time and reliable detection.

关键词： Accuracy Target recognition Computational modeling Feature extraction Real-time systems Complexity theory Neck

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Level Up Lab Projects' Showcase

Level Up Lab Projects' Showcase

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IEEE Games Media Entertainment (GEM)

作者： Leonardo Pieraccioli Daniel Bologna Gabriele Massimiani Ilaria Pasini Martina Plumari Pietro Uras Francesco Strada Control and Computer Engineering Department Politecnico di Torino Torino Italy

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

The demo aims to showcase the progress made by a student team from Politecnico di Torino called Level Up Lab. The team is developing 5 video games, each with unique characteristics. The games are presented in their alpha version during the demo to gather feedback for improvements in future development iterations. The demos also aim to illustrate the teams' activities and mission to academics and industry professionals to expand its network.

关键词： Video games Entertainment industry Games Media

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Opinion Score Distribution Prediction via AI-Based Observers in Media Quality Assessment

Opinion Score Distribution Prediction via AI-Based Observers...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Lohic Fotio Tiotsop Antonio Servetti Enrico Masala Control and Computer Engineering Department Politecnico di Torino Turin Italy

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

Training a Deep Neural Network (DNN) to predict an individual’s opinion score regarding the quality of multimedia content is a recent research direction. This type of DNN is called Artificial Intelligence-based Observer (AIO). By generating individual opinion scores, AIOs enable the prediction of the Opinion Score Distribution (OSD) for a given multimedia content. Multimedia image quality assessment literature lacks contributions that thoroughly assess the ability of AIOs to predict the OSD. In this paper a new set of AIOs is trained and shown to predict the OSD more accurately than state-of-the-art methods.

关键词： Training Image quality Accuracy Artificial neural networks Observers Media Quality assessment Information and communication technology Multimedia communication

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METER: a mobile vision transformer architecture for monocular depth estimation

arXiv

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arXiv 2024年

作者： Papa, Lorenzo Russo, Paolo Amerini, Irene the Department of Computer Control and Management Engineering Sapienza University of Rome Italy

Depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment. Deep learning algorithms for depth estimation have gained significant interest in recent years, owing to the potential benefits of this methodology in overcoming the limitations of active depth sensing systems. Moreover, due to the low cost and size of monocular cameras, researchers have focused their attention on monocular depth estimation (MDE), which consists in estimating a dense depth map from a single RGB video frame. State of the art MDE models typically rely on vision transformers (ViT) architectures that are highly deep and complex, making them unsuitable for fast inference on devices with hardware constraints. Purposely, in this paper, we address the problem of exploiting ViT in MDE on embedded devices. Those systems are usually characterized by limited memory capabilities and low-power CPU/GPU. We propose METER, a novel lightweight vision transformer architecture capable of achieving state of the art estimations and low latency inference performances on the considered embedded hardwares: NVIDIA Jetson TX1 and NVIDIA Jetson Nano. We provide a solution consisting of three alternative configurations of METER, a novel loss function to balance pixel estimation and reconstruction of image details, and a new data augmentation strategy to improve the overall final predictions. The proposed method outperforms previous lightweight works over the two benchmark datasets: the indoor NYU Depth v2 and the outdoor KITTI. © 2024, CC BY.

关键词： Learning algorithms

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Enhancing Electric Buggy Dynamics: A Comprehensive Analysis Using Single-Track Modeling

Enhancing Electric Buggy Dynamics: A Comprehensive Analysis ...

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Computing in Natural Sciences, Biomedicine and engineering (COMCONF), International Conference on

作者： Cosmin Rus Monica Leba Departament of System Control and Computer Engineering University of Petrosani Petrosani Romania

ISBN: (数字)9798350353358

ISBN: (纸本)9798350353365

In an era marked by technological advancements and a shift toward sustainable transportation solutions, dynamic modeling of autonomous electric vehicles is gaining significance. This paper explores the application of the singletrack model in the dynamic analysis of a customized electric Buggy, with the aim of enhancing maneuverability and the predictability of the vehicle's behavior under various operational conditions. Focusing on lateral dynamics, crucial for the precise control of vehicle motion, we have investigated the Buggy's response to different steering commands, analyzing how the interplay between steering, propulsion, and braking contributes to maintaining an accurate trajectory and executing effective evasive maneuvers. A framework based on principles of classical mechanics and control theory has been developed and implemented, which has enabled detailed simulation of the Buggy's interactions with its environment. The vehicle's stability, characterized by parameters such as the cornering stiffness of the wheels and the vehicle's inertia around the z-axis, was validated through eigenvalue analysis using Matlab simulations. These were instrumental in allowing us to visualize the dynamics of the Buggy without resorting to costly physical prototypes and time-consuming field testing. The results of our analysis demonstrate that the design and single-track model parameters selected for our Buggy ensure stability and maneuverability for both urban and off-road conditions, confirming that the system is stable at various speeds. The eigenvalue analysis indicates a system that retains equilibrium post-disturbances, with predictable and controllable vehicle behavior. Furthermore, the real-world applicability of the single-track model has been confirmed, ensuring a high level of safety and comfort for users.

关键词： Analytical models Adaptation models Computational modeling Dynamics Predictive models Electric vehicles Mathematical models Stability analysis Eigenvalues and eigenfunctions Vehicle dynamics

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