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Multi-feature and Multi-branch Action Segmentation Framework for modeling Long-Short-Term Dependencies

Multi-feature and Multi-branch Action Segmentation Framework...

引用

IEEE international conference on Multimedia and Expo (ICME)

作者： Junkun Hong Yitian Long Yueyi Luo Qianqian Qi Jun Long School of Computer Science and Engineering Central South University Changsha China Data Science Institute Vanderbilt University Nashville Tennessee United States School of Mathematics and Statistics Central South University Changsha China Big Data Institution Central South University Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Pioneer efforts have been dedicated to action segmentation that predicts what step is occurring in a video frame. Existing studies focus on improving the accuracy of video segmentation, but neglect the temporal continuity of intersegments and semantic consistency of intra-segments, which are necessary for developing computer-assisted systems. Meanwhile, Temporal Convolutional Networks have shown good performance in action segmentation tasks, but their high layers tend to lose fine-grained information and impact the results. Toward this end, we devise a multi-feature and multi-branch action segmentation framework for modeling long-term and short-term dependencies. Specifically, we present a multi-feature fusion to enhance temporal video representation and design a multi-branch predictor for extracting both segment-level and frame-level information. We justify our framework over three datasets and experimental results demonstrate its superiority, especially in Edit and F1 metrics, which means our framework is more applicable to computer-assisted systems.

关键词： measurement Accuracy Computational modeling Aggregates Semantics Feature extraction Data mining

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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?

Self-supervised Benchmark Lottery on ImageNet: Do Marginal I...

引用

international joint conference on Neural Networks (IJCNN)

作者： Utku Ozbulak Esla Timothy Anzaku Solha Kang Wesley De Neve Joris Vankerschaver Center for Biosystems and Biotech Data Science Ghent University Global Campus Republic of Korea Department of Electronics and Information Systems Ghent University Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Machine learning (ML) research strongly relies on benchmarks in order to determine the relative effectiveness of newly proposed models. Recently, a number of prominent research effort argued that a number of models that improve the state-of-the-art by a small margin tend to do so by winning what they call a "benchmark lottery". An important benchmark in the field of machine learning and computer vision is the ImageNet where newly proposed models are often showcased based on their performance on this dataset. Given the large number of self-supervised learning (SSL) frameworks that has been proposed in the past couple of years each coming with marginal improvements on the ImageNet dataset, in this work, we evaluate whether those marginal improvements on ImageNet translate to improvements on similar datasets or not. To do so, we investigate twelve popular SSL frameworks on five ImageNet variants and discover that models that seem to perform well on ImageNet may experience significant performance declines on similar datasets. Specifically, state-of-the-art frameworks such as DINO and Swav, which are praised for their performance, exhibit substantial drops in performance while MoCo and Barlow Twins displays comparatively good results. As a result, we argue that otherwise good and desirable properties of models remain hidden when benchmarking is only performed on the ImageNet validation set, making us call for more adequate benchmarking. To avoid the "benchmark lottery" on ImageNet and to ensure a fair benchmarking process, we investigate the usage of a unified metric that takes into account the performance of models on other ImageNet variant datasets.

关键词： measurement computer vision Computational modeling Neural networks Machine learning Self-supervised learning Benchmark testing

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Pyramidal Sun Sensor Calibration via Dichotomy Method

Pyramidal Sun Sensor Calibration via Dichotomy Method

引用

Electrical, computer, Telecommunication and Energy Technologies (ECTE-Tech), international conference on

作者： Elhassen Benfriha Akram Adnane Mohammed El-Amine Cheriet Rima Roubache Department of research in space mechanics Satellite development Center (CDS) Algerian Space Agency (ASAL) Oran Algeria

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

This work presents the calibration of a low-cost analog sun sensor essential for satellite attitude determination, focusing on measuring azimuth and elevation angles. A dedicated test bench including an optical table, dark room, solar emulator, and rotating table was established to evaluate sensor performance under realistic conditions. The calibration employed the dichotomy method to address the complexities of a multi-input multi-output (MIMO) system, ensuring accurate modeling of sensor outputs against solar angles. Results demonstrated the algorithm's robustness, achieving precise angle identification even with measurement uncertainties. Additionally, an intuitive graphical interface was developed to facilitate the calibration process and enhance the efficiency. This work significantly advances the reliability and affordability of sun sensors in satellite attitude control systems.

关键词： Satellites Azimuth measurement uncertainty Position measurement Robustness MIMO Calibration Complexity theory Optical sensors Sun

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Interference-Protected Radio Communication System Using a Repeater

Interference-Protected Radio Communication System Using a Re...

引用

international conference on Modern Problems of Radio Engineering, Telecommunications and computer Science

作者： Yuriy Bobalo Ivan Horbatyi Department of Theoretical Radio Engineering and Radio Measurement Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

Methods of ensuring interference immunity in radio relay systems, radio communication systems between ground stations and unmanned ground vehicles or unmanned aerial vehicles are considered. On the basis of the conducted analysis, it is proposed to use special signals in the system characterized by increased interference immunity, as well as to include a repeater in the system. Mathematical modeling of the radio channel between two ground stations or vehicle and a ground station has been carried out for various types of vehicles and ground stations in the absence or presence of a repeater in the system. The technical characteristics of the variants of the proposed system are calculated. Based on the results of the research, a structural diagram of an interference-protected radio communication system using a repeater is proposed.

关键词： Error analysis Symbols Interference Repeaters Market research Mathematical models Land vehicles Telecommunications Relays Radio communication

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Automated Verification of Silq Quantum Programs using SMT Solvers

Automated Verification of Silq Quantum Programs using SMT So...

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Quantum Software (QSW), IEEE international conference on

作者： Marco Lewis Paolo Zuliani Sadegh Soudjani Newcastle University Newcastle upon Tyne UK Università degli Studi di Roma La Sapienza Rome Italy Max Planck Institute for Software Systems Kaiserslautern Germany

ISBN: (数字)9798350368475

ISBN: (纸本)9798350368482

We present SilVer (Silq Verification), an automated tool for verifying behaviors of quantum programs written in Silq, which is a high-level programming language for quantum computing. The goal of the verification is to ensure correctness of the Silq quantum program against user-defined specifications using SMT solvers. We introduce a programming model that is based on a quantum RAM-style computer as an interface between Silq programs and SMT proof obligations, allowing for control of quantum operations using both classical and quantum conditions. Additionally, users can employ measurement flags within the specification to easily specify conditions that measurement results require to satisfy for being a valid behavior. We provide case studies on the verification of generating entangled states and multiple oracle-based algorithms.

关键词： Silver computer languages Adaptation models Quantum computing Quantum entanglement Computational modeling Programming

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Survey Optimizing Reinforcement Learning, Federated Learning, and Computational Network Model Performance

Survey Optimizing Reinforcement Learning, Federated Learning...

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Antennas Design and measurement international conference (ADMInC)

作者： Sarah H. Mnkash Faiz A. Al Alawv Israa T. Ali University of Technology Baghdad Iraq

ISBN: (数字)9798350390568

ISBN: (纸本)9798350390575

Federated learning enables distributed collaborative machine learning without compromising data privacy. Federated learning is a novel way to improve computer network efficiency and flexibility, when performance is a top goal. This study examines how federated learning might improve computerised network performance by focusing on bandwidth, latency, and fault tolerance. The research proposes to create a federated learning model where nodes with their own datasets may enhance computer network performance. The federated learning approach allows nodes to artefact or learn from experience without losing data locality via data aggregation. This study addresses federated learning difficulties such communication overhead, data heterogeneity, and model convergence. We also provide plausible amelioration methods based on tests and simulations to determine federated learning's effectiveness in improving computer network performance. Simulations show that this novel method may increase network efficiency, flexibility, and resilience in dynamic and heterogeneous computer networks. After addressing potential applications in wireless sensor networks, edge computing, and Internet of Things systems, the article will recommend further research in this area. Federated learning may convert efficient, resilient, and secure computer networks, unlike the RL, FL Model, and Computational model.

关键词： Training Fault tolerance Federated learning Computational modeling Design methodology Fault tolerant systems computer networks Optimization Resilience Edge computing

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Generating Book Suggestions Using a Hybrid Model with Sentiment Analysis

Generating Book Suggestions Using a Hybrid Model with Sentim...

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Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), international conference on

作者： Manvitha Pagadala Sreshta Cheekatla Karthik N Vani V Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Recommendation systems are ubiquitous, whether it is in social media posts, advertisements, or digital book and movie libraries. Many recommender systems suggest items based on the product or its use and do not consider the user's sentiments towards the item or user preferences. This paper employs a hybrid model comprising a mix of popularity and content-based approaches to generate book suggestions along with sentiment analysis. Sentiment analysis of the book descriptions and user feedback enhances the recommendations. This recommendation system also incorporates classification and filtering of explicit books based on the user's age to generate the top 10 recommendations of books. The measurement metrics implemented in testing this proposed model are Precision, Recall, Mean Absolute Error, and Mean Squared Error to test the recommendation system's performance. The proposed system with high precision and low error results in notable additions to existing recommendation systems.

关键词： Analytical models Sentiment analysis Filtering Computational modeling System performance measurement uncertainty Hybrid power systems

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Optimizing User Recommendations with Variational Autoencoders: Insights from MovieLens-1M and BookCrossing Datasets

Optimizing User Recommendations with Variational Autoencoder...

引用

computer Vision and Machine Intelligence (CVMI), international conference on

作者： Kumari Samridhi Bam Bahadur Sinha Trinanjan Das Electronics and Communication Engineering Indian Institute of Information Technology Ranchi India Computer Science and Engineering National Institute of Technology Sikkim India Computer Science and Engineering Indian Institute of Information Technology Ranchi India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Recommender systems utilize algorithms and data to predict user preferences based on their past choices. While these systems can be highly accurate, this increased accuracy can sometimes lead to predictability and monotony. In the realm of big data, recommender systems are both prevalent and valuable. Research on these systems has intensified in recent years, accompanied by a growing number of datasets. This paper examines the application of Variational Autoencoder (VAE)-based recommender systems using the MovieLens 1M and BookCrossing datasets. The study primarily evaluates the performance of the VAE model using Root Mean Square Error (RMSE) as the key metric. The VAE model was trained on these datasets to predict user preferences and generate personalized recommendations. Experimental results revealed an RMSE of less than 1 for both datasets, indicating the model’s proficiency in accurately estimating user ratings for items. These findings suggest that VAE-based recommender systems are promising for enhancing recommendation accuracy and personalization. This research contributes to the understanding of VAE models within the context of recommender systems and provides insights for future research and potential improvements in this field.

关键词： measurement computer vision Accuracy Mean square error methods Predictive models Prediction algorithms Root mean square Recommender systems Machine intelligence Context modeling

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MyanSpeech: joint CTC-Attention and RNN Language Model for End-To-End Read Speech Recognition

MyanSpeech: Joint CTC-Attention and RNN Language Model for E...

引用

computer Applications (ICCA), IEEE conference on

作者： Sann Su Su Yee Win Lai Lai Phyu Win Pa Pa Moe Myint Yain Sai Win Maw Nyein Chan Faculty of Computer Systems and Technologies Polytechnic University (Myeik) Myeik Myanmar Faculty of Information Science Polytechnic University (Myeik) Myeik Myanmar Faculty of Computing Naypyitaw State Polytechnic University Naypyitaw Myanmar Myanmar Language and Literature Improvement Project (MLLIP) Naypyitaw Myanmar

ISBN: (数字)9798331534592

ISBN: (纸本)9798331534608

This paper proposed MyanSpeech 1 , an end-to-end Automatic Speech Recognition (ASR) system designed for the Myanmar language, exploiting a joint CTC-Attention architecture for the acoustic model in combination with a Recurrent Neural Network Language Model (RNN-LM). The integration of CTC-Attention addresses the challenge of aligning audio with text in Myanmar, effectively combining the strengths of Connectionist Temporal Classification (CTC) for stable framewise alignment with Attention mechanisms for handling longer dependencies and variable input lengths. This dual approach enhances recognition accuracy, particularly for low-resource languages. The RNN-LM further supports the model by capturing language-specific grammar and contextual relationships. The dataset includes 36 hours, 33 minutes, and 51 seconds of read speech from 20 males and 18 females speakers, comprising 35,468 utterances of daily conversation from the Linguistic Committee (LC). Among various configurations, the best models achieved a word error rate (WER) of 7.093% and character error rate (CER) of 4.474% on a speaker-dependent (closed test), and a WER of 26.829% and CER of 17.597% on a speaker-independent (open test), respectively. These results highlight the effectiveness of integrating CTC-Attention and RNN-LM for Myanmar ASR, offering a reliable solution for speech recognition in low-resource language.

关键词： Training Analytical models Recurrent neural networks Accuracy Error analysis Computational modeling Linguistics Encoding Time measurement Context modeling

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Evaluating RF Data Transmission for Autonomous Underwater Vehicles for a Biomimicry Fish

Evaluating RF Data Transmission for Autonomous Underwater Ve...

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modeling, Simulation & Intelligent Computing (MoSICom), international conference on

作者： Prathamesh Bheemanathi Mitrang Gupta Majid Hassan Khan Nilesh Goel Shashank Khurana Department of Computer Science BITS Pilani Dubai Campus Dubai U.A.E

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Underwater Wireless Communications (UWC) are extremely challenging. To understand and overcome this the paper examines the effectiveness of Radio Frequency (RF) data transmission for Autonomous Underwater Vehicles (AUVs). A NodeMCU v3 ESP8266 board was used for this experiment to understand the bandwidth of 2.4 GHz RF communications in underwater environments. Key performance metrics like signal strength, latency, data loss, and speed were measured to understand the data transmissions. These findings highlight the challenges of RF signal underwater including attenuation and multipath effects. The results will help us understand the potential for real time sensor transmission that can be used in biomimicry of underwater robots. This will help us understand future advancements in RF, AUVs, and UWC technology.

关键词： Radio frequency Wireless communication Autonomous underwater vehicles Biomimetics RF signals Fish Robot sensing systems Propagation losses Real-time systems Loss measurement

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