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Deep learning methods for Fake News detection

Deep learning methods for Fake News detection

引用

international Symposium on Computational Intelligence and Informatics

作者： Viera Maslej Krešňáková Martin Sarnovský Peter Butka Technical university of Košice Košice Slovakia

ISBN: (数字)9781728156255

ISBN: (纸本)9781728156262

Spreading of misinformation on the web nowadays represents a serious issue, as their influence on peoples opinions may be significant. Fake news represents a specific type of misinformation. While its detection was mostly being performed manually in the past, automated methods using machine learning and related fields became more critical. On the other hand, deep learning methods became very popular and frequently used methods in the field of data analysis in recent years. the study presented in this paper deals with the detection of fake news from the textual data using deep learning techniques. Our main idea was to train different types of neural network models using both entire texts from the articles and to use just the title text. the models were trained and evaluated on the Fake News dataset obtained from the Kaggle competition.

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Gene Prediction by the Scale-limited Gabor Wavelet Transform for Identifying the Protein Coding Regions

Gene Prediction by the Scale-limited Gabor Wavelet Transform...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Qian Zheng Wenxiang Zhou Tao Chen Lei Xie Hong Ye Su State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Inertial Technology and Integrated Navigation Laboratory Beihang University Beijing China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

the identification of protein coding regions is one of the important applications of genome sequence analysis. Many digital signal processing (DSP) based methods, which rely on 3-base periodicity of DNA sequences, have been proposed. However, for most Fourier Transform based methods, a prior time-domain window length limits their performances. Even though several wavelet-based methods get rid of the dependence of window length, an overly wide scale range results in the loss of identification accuracy of these methods. In this paper, we propose a novel method based on Scale-limited Gabor Wavelet Transform (SLGWT) for identifying protein coding regions. this method inherits the advantage of wavelet-based methods in the independence of time-domain window length, while maintaining the consistent performance under different wavelet window lengths. More importantly, compared with other wavelet-based method, SLGWT identifies coding regions under narrower and more suitable scale range, thereby improving the identification accuracy and reducing computational load. the experimentations in the sequence and dataset levels verify the superiority of our proposed method.

关键词： Proteins Hidden Markov models Bioinformatics Genomics Encoding Wavelet transforms Time-domain analysis

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Observability of Two-Time-Scale Multi-Agent Systems with Antagonistic Interactions

Observability of Two-Time-Scale Multi-Agent Systems with Ant...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Mengqi Gu Qingbo Li Kaijie Zhou Jing Chen Jin Yang Huaian Key Laboratory of Millimeter-Wave Communication Technology Huaiyin Normal University Huaian China College of Automation Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Provincial Key Laboratory of Intelligent Electromagnetic Sensing and Communication Huaiyin Normal University Huaian China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

this paper investigates the observability of two-time-scale multi-agent systems (MASs), with particular attention to antagonistic interactions among agents. the study begins by modeling the systems, followed by defining observability and establishing its conditions using singular perturbation theory. Numerical simulations are conducted to demonstrate the effectiveness of the proposed conditions.

关键词： Perturbation methods Reliability theory Numerical simulation Numerical models Observability Artificial intelligence Robots Multi-agent systems Intelligent control

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Energy Efficiency Evaluation and Optimization methods in Electric Transportation Systems

Energy Efficiency Evaluation and Optimization Methods in Ele...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Han Wang Faculty of Electrical and Automation Engineering Nanjing Normal University Nanjing China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

this study presents a reinforcement learning-based framework for optimizing energy efficiency in electric vehicle (EV) and grid interactions. Managing grid load, especially during peak charging times, becomes critical as EV adoption increases. the framework leverages short-term load forecasting with data from the Electricity Load Forecasting dataset to dynamically balance energy distribution. Experimental results show a 131.5% reduction in Peak-to-Average Load Ratio (PALR) with a GRU model, a 97.53% improvement in Charging Efficiency Index (CEI) with a CNN-LSTM model, and a lower RMSE, demonstrating the framework’s effectiveness in enhancing grid integration and EV charging efficiency.

关键词： Adaptation models Load forecasting Electricity Stability criteria Transportation Reinforcement learning Energy efficiency Electric vehicle charging Vehicle dynamics Load modeling

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Solving the Inverse Kinematics Problem Symbolically by Means of Knowledge-Based and Linear Algebra-Based methods

Solving the Inverse Kinematics Problem Symbolically by Means...

引用

12th IEEE international conference on Emerging Technologies and Factory automation (ETFA 2007), vol.3

作者： Michael Wenz Heinz Worn Institute for Process Control and Robotics Universität Karlsruhe Karlsruhe Germany

the construction of kinematics models by hand is often a tedious and error-prone process. this paper presents an overview of the Organic Robot Control project. One main goal of the project is to automate the computation of closed-formed solutions for both the forward and inverse kinematics of general industrial manipulators. For simple manipulator geometries, we use rule-based programming techniques to obtain a closed-form solution. For complicated geometries, we use elimination techniques based on the computation of Groebner basis. In so doing we convert a complicated coupled transcendental set of kinematics equations into a univariate polynomial. this polynomial can much easier be solved. the generated analytic models fulfill real time requirements and can be parallelized very well.

关键词： Kinematics Manipulators Computational geometry Polynomials Robot control Computer industry Construction industry Robot programming Closed-form solution Equations

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Equipment Health Monitoring for Industrial Robotic Arms

Equipment Health Monitoring for Industrial Robotic Arms

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IEEE international conference on automation Science and Engineering (CASE)

作者： James Moore Daniela Sawyer Advanced Manufacturing Research Centre’s Integrated Manufacturing Group University of Sheffield Sheffield UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

the topic of equipment health monitoring (EHM) for robotics, including condition monitoring (CM), process monitoring (PM) and predictive maintenance (PdM) is of great interest within the literature, however, there is a significant lack of historical datasets for techniques to be tested upon. Commercial offerings in this area are often manufacturer specific, meaning that fleets that include robots from multiple suppliers cannot easily have performance/condition compared across the fleet. To address this, the work presented within this paper includes an accelerated wear test (AWT) conducted on an industrial robotic arm whilst being monitored using a suite of retrofitted sensors. the resulting data from the AWT is then analysed through a variety of techniques, including regression models, classification models, and a long short-term memory (LSTM) autoencoder, to demonstrate the potential for such methods to be utilised for robot ***, the associated dataset captured during the AWT is to be made openly available through the University of Sheffield’s online research data repository, ORDA, to allow further research to be conducted.

关键词： Adaptation models Accuracy Service robots Predictive models Robot sensing systems Manipulators Data models Maintenance Robots Long short term memory

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A comparison of adaptive PID methodologies controlling a DC motor with a varying load

A comparison of adaptive PID methodologies controlling a DC ...

引用

international conference on Emerging Technologies and Factory automation (ETFA)

作者： Luís Osório Jérôme Mendes Rui Araújo Tiago Matias Institute for Systems and Robotics (ISR-UC) and Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Coimbra Portugal

this work addresses the problem of controlling unknown and time varying plants for industrial applications. To deal with such problem several Self-Tuning Controllers with a Proportional Integral and Derivative (PID) structure have been chosen. the selected controllers are based on different methodologies, and some use implicit identification techniques (Single Neuron and Support Vector Machine) while the others use explicit identification (Dahlin, Pole placement, Deadbeat and Ziegler-Nichols) based in the Least Squares Method. the controllers were tested on a real DC motor with a varying load. the results have shown that all the tested methods were able to properly control an unknown plant with varying dynamics.

关键词： Vectors DC motors Support vector machines Heuristic algorithms Neurons Process control Adaptation models

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MLS: A Meta-Learning Based Stackelberg Model for Robot Trajectory Guidance

MLS: A Meta-Learning Based Stackelberg Model for Robot Traje...

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robotics, Control and automation (ICRCA), international conference on

作者： Jin Guo Zhiyong Jiang Yifan Liu Lili Gong Robotics Engineering Center The 21st Research Institute of China Electronics Technology Group Corporation Shanghai China

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

the critical role of trajectory guidance in facilitating follower robots to reach target destinations under the supervision of a leader robot is well-established. However, the complexity of coordinating efficient collaboration increases when a leader must guide a diverse set of followers with unknown specific characteristics. To address this challenge, we propose a novel meta-learning-based Stackelberg approach. We model the guided trajectory planning problem as a dynamic Stackelberg game, capturing the intricate interaction dynamics between the leader and followers. Utilizing meta-learning techniques, the leader learns a meta-best-response model from a selected set of followers. When a follower initiates a guidance request, the leader swiftly adapts to the follower's unique characteristics using a few learning data. this follower-specific model is then used for effective trajectory guidance. Comprehensive simulations demonstrate the superior generalization and adaptability of our proposed method over alternative learning methods, and emphasize the practical value and effectiveness of the guidance mechanism. Our findings underscore the potential of our meta-learning-based Stackelberg approach in addressing collaborative trajectory planning challenges in heterogeneous multi-robot systems.

关键词： Metalearning Adaptation models Trajectory planning Robot kinematics Collaboration Games Trajectory

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Enhancing Simultaneous Arrival at a Dynamic Target: A Hybrid Approach with Proximal Policy Optimization and Expert Rules

Enhancing Simultaneous Arrival at a Dynamic Target: A Hybrid...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Yifei Lei Jinwen Hu Zhao Xu Chenqi Gao Jiatong Li School of Automation Northwestern Polytechnical University Xi'an China School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

this paper addresses the complex challenge of ensuring simultaneous arrival of multiple agents at a dynamic target, a critical requirement for operations such as roundups and saturation attacks. Traditional guidance systems have faced significant hurdles due to inaccurate flight time estimates and difficulties adapting to high-speed maneuvers. To overcome these limitations, we introduce a novel framework that utilizes Proximal Policy Optimization (PPO) and expert rules. this approach leverages distributed computing to enable autonomous decision-making among agents, thereby simplifying the deep reinforcement learning model and reducing computational overhead, which enhances scalability and adaptability. Additionally, we incorporate an angular velocity reward into the reward function, improving the predictability and effectiveness of maneuvers, particularly for targets with high-speed and irregular trajectories. the proposed methods have been rigorously tested through numerous simulations and high-fidelity scenarios, confirming their robustness and superior performance over traditional and enhanced proportional guidance systems.

关键词： Adaptation models Scalability Computational modeling Decision making Angular velocity Robustness Trajectory Distributed computing Optimization Testing

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three-Dimensional Trajectory Recognition Technology for the Junction Trace of a Material Assembly Used in footwear manufacturing automation

Three-Dimensional Trajectory Recognition Technology for the ...

引用

international conference on Advanced robotics (ICAR)

作者： C. C. Mai C. K. Huang H. HoCheng National Tsing Hua University Hsinchu City Taiwan Industrial Technology Research Institute Hsinchu City Taiwan

ISBN: (纸本)9781665436854

the footwear manufacturing industry is a so-called “3D industry” (i.e., an industry that is dirty, dangerous, and difficult) because its processes involve the use of toxic substances. Moreover, this industry is labor-intensive. Leading footwear brands that have attempted to replace labor with automated machinery have encountered problems such as a high defect rate and complicated manufacturing process, which reduce their production yield. the fundamental reasons for these problems are that the footwear industry manufactures products with different styles, materials, and sizes and must cope with challenges such as poor precision in processing tools, substantial variations in stitched shoe uppers, and soft and easily deformed sole materials. therefore, working trajectories must be generated for each pair of shoes before the automatic manufacturing process can commence. Consequently, trajectory recognition is essential for introducing automation into the footwear industry. Considering the aforementioned problems, this paper proposes a three-dimensional trajectory recognition technology, which involves three-dimensional point cloud model matching, for the junction trace of shoe parts. When the proposed technology is used, models are constructed for the upper and sole so that after they are joined, three-dimensional point cloud model matching can be conducted to determine the possible location of the junction trace according to the location of the sole in the assembly. Subsequently, a material identification device is used to examine the materials in the identified junction area and determine whether these materials are the same. the trajectory recognition operation for automated shoe production is completed by scanning the entire junction area where the upper and sole meet. the aforementioned method effectively resolves the drawbacks of existing automation methods in the footwear industry, including the effects of the workpiece material and color on the accuracy of traj

关键词： Point cloud compression Solid modeling Footwear industry Manufacturing processes Footwear Production Trajectory

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