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Artificial Intelligence for Fake News 1

International Conference on Advanced intelligent systems for Sustainable Development, AI2SD 2022

作者： Ennejjai, Imane Ariss, Anass Kharmoum, Nassim Rhalem, Wajih Ziti, Soumia Ezziyyani, Mostafa Department of Computer Science Intelligent Processing Systems and Security Team Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Rabat Morocco E2SN Research Team ENSAM Rabat Mohammed V University in Rabat Rabat Morocco Mathematics and Applications Laboratory Faculty of Sciences and Techniques of Tangier Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9783031263842

ISBN: (纸本)9783031263835

Fake news is a severe problem on social media networks, with confirmed detrimental consequences for individuals and organizations. As a result, detecting false news is a significant difficulty. In this way, the topic of new fakes and their proper detection are crucial. General knowledge states that the receiver of information must verify the sources. However, the creation of new information can be a difficult problem that requires more than a single viewpoint based on a news source. The objective of this paper is to evaluate the performance of six deep learning models for fake news detection including CNN, LSTM, Bi-LSTM, HAN, Conv-Han and Bert. We find that BERT and similar pre-trained models perform the best for fake news detection, the models are described below with their experimental *** models are examined against ISOT [22, 30] datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Tracking Methods: Comprehensive Vision and Multiple Approaches 1

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International Conference on Advanced intelligent systems for Sustainable Development, AI2SD 2022

作者： Ariss, Anass Ennejjai, Imane Kharmoum, Nassim Rhalem, Wajih Ziti, Soumia Ezziyyani, Mostafa Department of Computer Science Intelligent Processing Systems and Security Team Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Rabat Morocco E2SN Research Team ENSAM Rabat Mohammed V University in Rabat Rabat Morocco Mathematics and Applications Laboratory Faculty of Sciences and Techniques of Tangier Abdelmalek Essaadi University Tétouan Morocco

ISBN: (数字)9783031352515

ISBN: (纸本)9783031352508

Tracking of human beings, as well as objects, becomes a great challenge currently presented, it aims to understand the fundamental principles of objects and human beings detected to associate them with robust systems for treaties and draw the desired conclusions. Tracking generally uses several methods such as graph theory, technological tools, Internet of Things (IoT), Big Data, and artificial intelligence, where it maintains several hypotheses to help tracking objects or human beings. In this article, we propose the tracking methods proposed in this direction, and finally, we analyze and discuss the various results obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph theory

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Data-Driven Discovery of Stochastic Differential Equations

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Engineering 2022年第10期17卷 244-252页

作者： Yasen Wang Huazhen Fang Junyang Jin Guijun Ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye Yuan Jorge Gonçalves Jürgen Kurths Han Ding School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrenceKS 66045USA HUST-Wuxi Research Institute Wuxi 214174China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Department of Plant Sciences University of CambridgeCambridge CB23EAUK Luxembourg Centre for Systems Biomedicine University of LuxembourgBelvaux 4367Luxembourg Department of Physics Humboldt University of BerlinBerlin 12489Germany Department of Complexity Science Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different *** identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s *** practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data *** study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis *** importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series *** effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin *** framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.

关键词： Data-driven method System identification Sparse Bayesian learning Stochastic differential equations Random phenomena

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Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition

arXiv

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arXiv 2024年

作者： Li, Jiang Wang, Xiaoping Zeng, Zhigang The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China The Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Multimodal emotion recognition in conversation (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they fail to fully harness direct inter-modal cues, possibly leading to less-than-thorough cross-modal modeling;(2) they concurrently extract information from the same and different modalities at each network layer, potentially triggering conflicts from the fusion of multi-source data;(3) they lack the agility required to detect dynamic sentimental changes, perhaps resulting in inaccurate classification of utterances with abrupt sentiment shifts. To address these issues, a novel approach named GraphSmile is proposed for tracking intricate emotional cues in multimodal dialogues. GraphSmile comprises two key components, i.e., GSF and SDP modules. GSF ingeniously leverages graph structures to alternately assimilate inter-modal and intra-modal emotional dependencies layer by layer, adequately capturing cross-modal cues while effectively circumventing fusion conflicts. SDP is an auxiliary task to explicitly delineate the sentiment dynamics between utterances, promoting the model’s ability to distinguish sentimental discrepancies. Furthermore, GraphSmile is effortlessly applied to multimodal sentiment analysis in conversation (MSAC), forging a unified multimodal affective model capable of executing MERC and MSAC tasks. Empirical results on multiple benchmarks demonstrate that GraphSmile can handle complex emotional and sentimental patterns, significantly outperforming baseline models. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Effective and Efficient Intracortical Brain Signal Decoding with Spiking Neural Networks

arXiv

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arXiv 2024年

作者： Fu, Haotian Zhang, Peng Yang, Song Zhang, Herui Wang, Ziwei Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Department of Biomedical Engineering College of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China

A brain-computer interface (BCI) facilitates direct interaction between the brain and external devices. To concurrently achieve high decoding accuracy and low energy consumption in invasive BCIs, we propose a novel spiking neural network (SNN) framework incorporating local synaptic stabilization (LSS) and channel-wise attention (CA), termed LSS-CA-SNN. LSS optimizes neuronal membrane potential dynamics, boosting classification performance, while CA refines neuronal activation, effectively reducing energy consumption. Furthermore, we introduce SpikeDrop, a data augmentation strategy designed to expand the training dataset thus enhancing model generalizability. Experiments on invasive spiking datasets recorded from two rhesus macaques demonstrated that LSS-CA-SNN surpassed state-of-the-art artificial neural networks (ANNs) in both decoding accuracy and energy efficiency, achieving 0.80-3.87% performance gains and 14.78-43.86 times energy saving. This study highlights the potential of LSS-CA-SNN and SpikeDrop in advancing invasive BCI applications. © 2024, CC BY.

关键词： Neurons

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An Assist-as-Needed Variable Admittance Controller for Adaptive Support of Mobile Walking Assistive Robots

An Assist-as-Needed Variable Admittance Controller for Adapt...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Xinxing Chen Yue Zheng Zongming Su Yuquan Leng Jian Huang Chenglong Fu Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Technology Planning Department Huawei Technologies Shenzhen China Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Mobile walking assistive robots have been widely used for monitoring patients’ gait patterns and providing support to prevent falls. Traditional approaches often separate the operational modes of these robots into human-following and supporting modes, which can lead to discontinuities in assistance and potentially compromise safety. This paper proposes a novel assist-as-needed control strategy that incorporates a Balance Index (BI) to dynamically adjust the robot’s support based on the user’s balance capability. The BI is calculated using the interaction force between the user and the robot along the z-axis and the gait symmetry index (GSI). The proposed variable admittance controller modifies its parameters based on the BI, ensuring seamless transitions between compliance and support modes. Experimental validation was conducted with a healthy subject walking both with and without a knee fixation brace. The results demonstrate that the robot provides minimal interference during natural gait and increased support during impaired gait, validating the effectiveness of the proposed control strategy in enhancing user safety and promoting natural walking patterns.

关键词： Legged locomotion Force Stability criteria Interference Assistive robots User experience Safety Admittance Indexes Robots

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Overview of EMG Signal Preprocessing and Classification for Bionic Hand Control

Overview of EMG Signal Preprocessing and Classification for ...

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Information Technologies & Smart Industrial systems (ITSIS), IEEE

作者： Monaam Ayachi Hassene Seddik Electrical department RIFTSI research laboratory: intelligent robotics reliability and image signal processing Higher National Engineering School of Tunis (ENSIT) University of Tunis Tunis

This paper deals with the field of Biological Signal especially one of the most used signals in the field of prosthetic devices, which is the EMG signal. After introducing this kind of signal, we will talk about different techniques used for preprocessing it in order to prepare it for classification tasks. We Will then overview different noises that affect those sensible type of signals, different techniques applied for filtering them and the useful envelope detection methods that can be applied on EMG. The next part of this paper will look at some famous feature extraction and classification techniques that proved themselves useful when dealing with classification tasks of biological signals. Finally, we will present the mechanical design of a multiarticulate multifunctional prosthetic hand, the promising classification results that can gives 98.3% and our perspectives.

关键词： Support vector machines Filtering Shape Feature extraction Electromyography Biology Noise measurement

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Zero-Shot Audio Captioning Using Soft and Hard Prompts

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 2045-2058页

作者： Yiming Zhang Xuenan Xu Ruoyi Du Haohe Liu Yuan Dong Zheng-Hua Tan Wenwu Wang Zhanyu Ma Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Centre for Vision Speech and Signal Processing University of Surrey Guildford U.K. Department of Electronic Systems Aalborg University Aalborg Denmark

In traditional audio captioning methods, a model is usually trained in a fully supervised manner using a human-annotated dataset containing audio-text pairs and then evaluated on the test set from the same dataset. Such methods have two limitations. First, these methods are often data-hungry and require time-consuming and expensive human annotations to obtain audio-text pairs. Second, these models often suffer from performance degradation in cross-domain scenarios, i.e., when the input audio comes from a different domain than the training set, and this issue has received little attention. To address these issues, we propose a new zero-shot method for audio captioning. Our method is built on the contrastive language-audio pre-training (CLAP) model. During training, the model reconstructs the ground-truth caption using the CLAP text encoder. In the inference stage, the model generates text descriptions from the CLAP audio embeddings of given audio inputs. To enhance the ability of the model in transitioning from text-to-text generation to audio-to-text generation, we propose to use the mixed-augmentations-based soft prompt to learn more robust latent representations, leveraging instance replacement and embedding augmentation. Additionally, we introduce the retrieval-based acoustic-aware hard prompt to improve the cross-domain performance of the model by employing the domain-agnostic label information of sound events. Extensive experiments on AudioCaps and Clotho benchmarks show the effectiveness of our proposed method, which outperforms other zero-shot audio captioning approaches for in-domain scenarios and outperforms the compared methods for cross-domain scenarios, underscoring the generalization ability of our method.

关键词： Training Decoding Semantics Data models Acoustics Electronic mail Benchmark testing Transformers Robustness Perturbation methods

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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

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IEEE Transactions on Circuits and systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 IEEE.

关键词： Memristors

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Implications of neuroimaging findings in addiction

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Psychoradiology 2023年第1期3卷 18-23页

作者： Xinwen Wen Lirong Yue Zhe Du Linling Li Yuanqiang Zhu Dahua Yu Kai Yuan School of Life Science and Technology Xidian UniversityXi’an 710126China School of Biomedical Engineering Medical SchoolShenzhen UniversityShenzhen 518060China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen UniversityShenzhen 518060China Department of Radiology Xijing HospitalFourth Military Medical UniversityXi’an 710032China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information EngineeringInner Mongolia University of Science and TechnologyBaotou 014010China Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Xidian UniversityXi’an 710126China Xi’an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information School of Life Science and TechnologyXidian UniversityXi’an 710126China

Addiction is a chronic and often relapsing brain disorder characterized by drug abuse and withdrawal symptoms and compulsive drug seeking(Koob and Volkow,2010)when access to the drug is *** leads to structural and fun... 详细信息

关键词： motivation finding seeking

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