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Dual-Branch Framework for Panoramic Activity Recognition: Integrating Appearance and Trajectory Information for Enhanced Social Relationship modeling

Dual-Branch Framework for Panoramic Activity Recognition: In...

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Advanced Algorithms and Control Engineering (ICAACE), international conference on

作者： Wenqing Gan Yan Sun School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Panoramic activity recognition is a novel task aimed at simultaneously identifying behaviors at multiple granularities within the same framework, including individual actions, social group activities, and global activities, without restricting the number of individuals or subgroups. However, existing methods primarily rely on appearance features and spatial positions within a single frame, neglecting the dynamic interactions captured by trajectory information, which limits their ability to accurately model complex social relationships. To address these challenges, we propose a dual-branch framework that integrates appearance and trajectory information to comprehensively model social relationships. In the appearance branch, we capture the spatiotemporal context of appearance features through spatiotemporal self-attention mechanism. In the trajectory branch, we use the OSPA (2) as a trajectory similarity metric and construct a graph transformer with LSTM-extracted trajectory features as nodes to effectively model the dynamic inter-trajectory interactions between individuals. We design a cross-granularity aggregation module based on the transformer encoder to capture unified motion patterns through a shared aggregation module, which then generates features for social groups and global activities. Compared to previous methods that only rely on appearance features and spatial positions within a single frame, the proposed method is able to more comprehensively capture social relationships and behavioral patterns in complex scenarios.

关键词： measurement Social groups Computational modeling Heuristic algorithms Dynamics Activity recognition Transformers Real-time systems Trajectory Spatiotemporal phenomena

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A Novel Lightweight Convolutional Neural Network for EEG-Based Miniature-Event-Related Potentials

A Novel Lightweight Convolutional Neural Network for EEG-Bas...

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IEEE international conference on Computational Intelligence and Virtual Environments for measurement systems and Applications

作者： Chang Liu Wenxiao Zhong Yang Di Liang Zhao Xingwei An Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin P. R. China Tianjin Key Laboratory of Brain Science and Neuroengineering academy of medical engineering and translation of medicine Tianjin University Tianjin Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration State Key Laboratory of Advanced Medical Materials and Devices Tianjin University Tianjin Tianjin P. R. China

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

Miniature asymmetric visual evoked potential (aVEP) induced by very small lateral visual stimuli is a comfortable stimulation paradigm. It could only induce a miniature potential about 0.5 μV in amplitude, which could be challenging to detect. Most decoding methods based on miniature aVEP are machine learning approaches, and deep learning methods have not yet been introduced. Therefore, this paper introduces deep learning techniques for the first time and designs a lightweight convolutional neural network. Specifically, depthwise convolution is employed to reduce the number of network parameters and extract temporal and spatial features from EEG signals. The model achieves an average classification accuracy of 78.48% on the miniature aVEP dataset, which is 1.14% higher than the previous state-of-the-art model, DCPM. Through comparison with other deep learning models in terms of parameter count, computational complexity, and classification accuracy, it is evident that the proposed model surpasses them in all these aspects The results suggest that the CNN model proposed in this study achieves high classification accuracy while demanding fewer computational resources. Although the network model we proposed is very simple, it can achieve good results in processing miniature aVEP. In future research, we will continue to improve this lightweight network to enhance its decoding performance for miniature aVEP.

关键词： Deep learning Visualization Accuracy Computational modeling Neural networks Virtual environments Brain modeling

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Reducing Hand Tremors Using Active Controller

Reducing Hand Tremors Using Active Controller

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RSI/ISM international conference on Robotics and Mechatronics (ICROM)

作者： Mohammad Mahdi Abedi Aghil Yousefi Koma Mohammad Amin Azari Fatemeh Azizi Center of Advanced systems and Technologies (CAST) School of Mechanical Engineering University of Tehran Tehran Iran Center of Advanced systems and Technologies (CAST) School of Computer Engineering Qazvin Islamic Azad University Qazvin Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

The study focuses on addressing involuntary hand tremors, a prevalent neurological disorder that hinders various tasks. Current research emphasizes methods with minimal side effects to alleviate these tremors. This investigation introduces an electromechanical system specifically designed to actively mitigate involuntary hand tremors, with one potential application being a tremor–reducing spoon. The system employs two actuators for controlling two degrees of freedom and incorporates a gyroscope to detect hand tremors. A comprehensive analysis of the physical modeling, encompassing the kinematics and dynamics of the system, is conducted to establish the final system model. Subsequently, a controller is developed in two modes—position control and angular acceleration control—to improve the model's performance. Practical experimentation is then undertaken to assess the system's performance, particularly its response to incoming vibrations.

关键词： Hands Vibrations Analytical models System performance Gyroscopes Usability System implementation Rotation measurement Robots Testing

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Do One Thing and Do It Well: Delegate Responsibilities in Classical Planning

Do One Thing and Do It Well: Delegate Responsibilities in Cl...

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IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Tin Lai Philippe Morere School of Computer Science The University of Sydney Australia Fait Corporation Australia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We propose a novel framework and algorithm for solving classical planning problems with an implicit hierarchical solver based on the principle of delegation. This framework, the Markov Intent Process, features a collection of skills that are each specialised to perform a single task well. Skills are aware of their intended effects and are able to analyse planning goals to delegate planning to the best-suited skill. This principle dynamically creates a hierarchy of plans, in which each skill plans for sub-goals for which it is specialised. Our method performs robustly in noisy environments with non-deterministic action effects and features on-demand execution—skill policies are only evaluated when needed. Plans are only generated at the highest level, then expanded and optimised when the latest state information is available. The high-level plan retains the initial planning intent and previously computed skills, effectively reducing the computation needed to adapt to environmental changes. We show this planning approach is experimentally very competitive to classic planning and reinforcement learning techniques on a variety of domains, both in terms of solution length and planning time.

关键词： Decision making Reinforcement learning Cognition Planning Noise measurement Intelligent robots Context modeling

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Performance Analysis of Deep Learning's CNN Architectures for Internet of Vehicles Classification

Performance Analysis of Deep Learning's CNN Architectures fo...

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Smart Energy systems and Artificial Intelligence (SESAI), international conference on

作者： Amaren Iyavoo Vinaye Armoogum Sameer Sunhaloo School of Innovative Technologies and Engineering University of Technology Port-Louis Mauritius

ISBN: (数字)9798350349689

ISBN: (纸本)9798350349696

The World Health Organization estimates that road accident fatalities reached about 1.19 million in 2021 and keep increasing yearly, with about 90% due to human errors. Artificial intelligence can elevate the environmental awareness of drivers and reduce human errors. With the ever-evolving field of deep learning, this study aims to compare the accuracy and effectiveness along with the computational capabilities of the seventeen most popular convolutional neural network architectures from AlexNet, published in 2012, to MaxVit in 2022, for an image binary classification problem. This study aims to be a foundation for future research on computer vision applications in the Internet of Vehicles domain in various ways, such as the effectiveness of transfer learning and the behaviour of CNN models on low-quality images. A total of sixty-seven models were trained with and without pre-trained weights, and the performance and computational metrics of each hundred and thirty-four models were measured. Among the chosen models, it was observed that the DenseNet model emerged as the most effective and efficient model, with a training time of 11.8 minutes, predictive accuracy of 100%, memory footprint of 26.53 MB and GPU utilisation of 4.1 GB.

关键词： Deep learning Weight measurement Training Analytical models Accuracy Computational modeling Transfer learning

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Trace Anomaly Detection for Microservice systems via Graph-based Semi-supervised Learning

Trace Anomaly Detection for Microservice Systems via Graph-b...

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international conference on computer Supported Cooperative Work in Design

作者： Shuai Ding Yuepeng E Jiye Zhang Liangxiong Li Lei Zhang Jingguo Ge Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Beijing Institute of Computer Technology and Applications Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Microservice architectures have become mainstream for cloud-native applications, and distributed tracing is widely used to ensure system observability. Trace is essentially an aggregated data with multimodal attributes (e.g., performance metric, invocation log, and topology relationships). Existing methods typically do not adequately consider the complex correlations and interactions between the different modalities of trace, whereas fusing multimodal data into a unified analysis provide the possibility to improve performance. On the other hand, most of the existing methods are trained in an unsupervised manner and cannot utilize historical data. Therefore, this article proposes a trace anomaly detection method using graph-based semi-supervised learning, TraceGSAD. It extracts features of trace from multiple modalities and train an improved message-passing neural network to fuse features for unified modeling and generate a graph-level representation. The end-to-end deep anomaly detection model trained in an semi-supervised manner, using only a small amount of labeled data to achieve superior performance. The evaluation on the microservice benchmarks show that TraceGSAD achieves a high precision, outperforming state-of-the-art trace anomaly detection approaches. It also demonstrates the effectiveness of multimodal fusion analysis as well as the use of empirical data in semi-supervised learning that can significantly improve anomaly detection performance.

关键词： measurement Correlation Fuses Computational modeling Neural networks Microservice architectures Semisupervised learning

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Application of Social and Private Interests Coordination Engine Model in State Development Programs 3

Application of Social and Private Interests Coordination Eng...

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3rd international conference on Control systems, Mathematical modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Gorbaneva, Olga I. Murzin, A.D. Ougolnitsky, Gennady A. Institute of Mathematics Mechanics and Computer Sciences Southern Federal University Rostov-on-Don Russia Southern Federal University Don State Technical University Management Department Rostov-on-Don Russia

ISBN: (纸本)9781665439817

An implementation of federal development programs affects the interests of many economic agents. At the regional level, such programs can be implemented in the form of joint activities' regulation of municipal districts for the purpose of their intensive development. This work solves the problem of applying well-known models of the coordination of social and private interests (SPICE-models) to the management of specific government programs implementation. The study simulates the interaction of several municipal districts of the Rostov region in the framework of the program 'Development of agriculture and regulation of markets for agricultural products, raw materials and food'. The authors considered the mechanisms of administrative, economic and proportional management. As a result of the above calculations, the expediency of the administrative management mechanism is substantiated, which makes it possible to solve all managerial tasks in the given conditions of all the districts under consideration. © 2021 IEEE.

关键词： Agricultural products

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Application of computer Simulation Technology in the Design of Virtual Simulation Experimental Platform for Food Science and Engineering

Application of Computer Simulation Technology in the Design ...

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Integrated Circuits and Communication systems (ICICACS), IEEE international conference on

作者： Xue Li Bohai University Jinzhou Liaoning China

In recent years, with the rapid development of computer simulation CS technology, CS technology has played an increasingly important role in food science(FC), engineering Virtual simulation experimental(VSP) platform design, etc. The current FC and engineering VSP platform lacks an effective platform framework to manage the experimental environment as a whole, and the configuration and evaluation of the experiment are relatively inconvenient. Based on the relevant application research of CS technology, this paper constructs the design of the current FC and engineering VSP platform to meet the needs of the current experimental platform design and application. In the current period of increasing food safety concerns, in this paper, we have conducted extensive research on FC and engineering in designing a simulation experimental platform and created a logical and effective impact assessment metric in a simulated experimental environment. system. The development case for virtualized experiences offers the possibility. The final result of the study shows that when the number of trials of experimental platform four is 76, its corresponding accuracy rate is 98.8%. When the number of trials of experimental platform 5 is 91, its corresponding accuracy rate is 98.2%. The results show that when the number of tests on the experimental platform increases, the corresponding experimental accuracy rate is always maintained at about 98.5%, indicating that the platform has a high and stable accuracy rate, and has feasibility and certain promotion value.

关键词： measurement Integrated circuits Analytical models Correlation Communication systems computer simulation Computational modeling

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Transformer vs. LSTM: Evaluating Machine Translation models for Sanskrit to English and Sanskrit to Hindi Datasets

Transformer vs. LSTM: Evaluating Machine Translation models ...

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Intelligent Algorithms for Computational Intelligence systems (IACIS), international conference on

作者： N Biraja Isac Himansu Das School of Computer Engineering KIIT University Bhubaneswar India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Neural Machine Translation (NMT) has emerged as a dominant technique offering fluent and natural translations across various language pairs, but it demands substantial computational resources and large datasets, posing challenges with rare words or phrases. This research leverages the Bhagavad Gita dataset, consisting of 700 parallel Sanskrit-English and Sanskrit-Hindi sentences and a larger dataset of 13,000 English-Sanskrit parallel sentences from the Srimad Bhagavatam. The evaluation metrics for translation quality include BLEU and METEOR scores. The Transformer model consistently outperformed the LSTM model in translating Sanskrit to English and Sanskrit to Hindi, demonstrating higher BLEU and METEOR scores. Notably, Sanskrit-Hindi translations achieved better scores than Sanskrit-English translations, indicating that the Transformer model is more effective for languages with similar syntactic structures. The LSTM model exhibited over-fitting with smaller datasets but performed adequately with larger datasets. Training curves indicated optimal performance of Transformer models within 20 epochs, whereas LSTM models required more extensive training.

关键词： Training measurement Computational modeling Transfer learning Neural machine translation Syntactics Transformers Robustness Meteors Long short term memory

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Seed Kernel Counting using Deep Learning for High-Throughput Phenotyping

Seed Kernel Counting using Deep Learning for High-Throughput...

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IEEE international conference on Internet of Things and Intelligence System (IOTAIS)

作者： Venkat Margapuri Prapti Thapaliya Mitchell Neilsen Department of Computer Science Villanova University Villanova PA Department of Computer Science Kansas State University Manhattan KS

ISBN: (数字)9798331521219

ISBN: (纸本)9798331521226

High-throughput phenotyping (HTP) of seeds, also known as seed phenotyping, is the comprehensive assessment of complex seed traits such as growth, development, tolerance, resistance, ecology, yield, and the measurement of parameters that form more complex traits [1]. One of the key aspects of seed phenotyping is cereal yield estimation that the seed packaging industry relies upon to conduct their business. While mechanized seed kernel counters are available in the market, they are often priced high and outside the affordability realm of small-scale seed packaging firms. The development of object tracking neural network models such as You Only Look Once (YOLO) enables computer scientists to design algorithms that can estimate cereal yield inexpensively. The key bottleneck of neural network models is that they require a plethora of labeled training data before they can be put to task. We demonstrate that the use of synthetic imagery serves as a feasible substitute to train neural networks for object tracking that includes the tasks of object classification and detection. Furthermore, we propose a seed kernel counter that uses a low-cost mechanical hopper, trained YOLOv8 neural network model, and object tracking algorithms of StrongSORT and ByteTrack to estimate cereal yield from videos captured using a mobile phone. The experiment yields a seed kernel count with an accuracy of 95.2% and 93.2% for Soy and Wheat respectively using the StrongSORT algorithm, and an accuracy of 96.8% and 92.4% for Soy and Wheat respectively using the ByteTrack algorithm.

关键词： YOLO Deep learning Accuracy Biological system modeling Computational modeling Neural networks Packaging Seeds (agriculture) Object tracking Kernel

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