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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dai, Yulan Zou, Beiji Kui, Xiaoyan Li, Qinsong Zhao, Wei Liu, Jun López, Miguel Bordallo Central South University School of Computer Science and Engineering Changsha China Central South University Big Data Institute Changsha China Second Xiangya Hospital Department of Radiology Changsha China University of Oulu Center for Machine Vision and Signal Analysis Oulu Finland

ISBN: (纸本)9798350386226

Vision-language foundational models have achieved commendable results on related tasks. However, their application to medical tasks is still limited due to issues arising from data biases. Currently, leveraging existing foundational models to improve medical tasks remains a challenge. To this end, this paper proposes a strong multimodal representation learning method based on cross-domain distillation handling structural Magnetic Resonance Imaging (sMRI), Positron Emission Computed Tomograph (PET) images, and mini-mental state examination (MMSE) score for Alzheimer's disease (AD) classification. Specifically, we establish a text-to-image cross-domain distillation learning framework, enabling a text encoder pre-trained on general visual recognition tasks to guide the training of sMRI and PET image feature extractors. Simultaneously, positional encoding is used to extract the magnitude features of MMSE scores. Based on the multimodal representations extracted from sMRI, PET images, and MMSE scores, we perform a self-attention operation equipped with a gating mechanism for multimodal feature fusion. This mechanism controls the contribution of each modality representation to the classification decision, dynamically strengthening or weakening specific modality representations and helping construct stronger fused features for AD classification. Our method undergoes 5-fold cross-validation on the widely used ADNI dataset, and comparative experimental results demonstrate that our method achieves advanced performance in two AD-related binary classification tasks. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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Safe and Reliable Training of Learning-Based Aerospace Controllers 43

Safe and Reliable Training of Learning-Based Aerospace Contr...

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43rd aiAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

作者： Mandal, Udayan Amir, Guy Wu, Haoze Daukantas, Ieva Newell, Fletcher Lee Ravaioli, Umberto Meng, Baoluo Durling, Michael Hobbs, Kerianne Ganai, Milan Shim, Tobey Katz, Guy Barrett, Clark Stanford University Center for Ai Safety Stanford United States School of Cs & Engineering The Hebrew University of Jerusalem Jerusalem Israel It University of Copenhagen Department of Computer Science Copenhagen Denmark Google Mountain View United States Ge Aerospace Research Niskayuna United States Us Air Force Air Force Research Laboratory Dayton United States Stanford University Department of Computer Science Stanford United States Stanford University Department of Data Science Stanford United States

ISBN: (纸本)9798350349610

In recent years, deep reinforcement learning (DRL) approaches have generated highly successful controllers for a myriad of complex domains. However, the opaque nature of these models limits their applicability in aerospace systems and sasfety-critical domains, in which a single mistake can have dire consequences. In this paper, we present novel advancements in both the training and verification of DRL controllers, which can help ensure their safe behavior. We showcase a design-for-verification approach utilizing k-induction and demonstrate its use in verifying liveness properties. In addition, we also give a brief overview of neural Lyapunov Barrier certificates and summarize their capabilities on a case study. Finally, we describe several other novel reachability-based approaches which, despite failing to provide guarantees of interest, could be effective for verification of other DRL systems, and could be of further interest to the community. © 2024 IEEE.

关键词： Deep reinforcement learning

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Person Re-Identification with Model-Contrastive Federated Learning in Edge-Cloud Environment

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Intelligent Automation & Soft Computing 2023年第10期38卷 35-55页

作者： Baixuan Tang Xiaolong Xu Fei Dai Song Wang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and TechnologyNanjing210044China School of Big Data and Intelligence Engineering Southwest Forestry UniversityKunming650233China Nanjing Meteorological Service Center Nanjing Meteorological BureauNanjing210019China

Person re-identification(ReID)aims to recognize the same person in multiple images from different camera *** person ReID models are time-consuming and resource-intensive;thus,cloud computing is an appropriate model training ***,the required massive personal data for training contain private information with a significant risk of data leakage in cloud environments,leading to significant communication *** paper proposes a federated person ReID method with model-contrastive learning(MOON)in an edge-cloud environment,named ***,based on federated partial averaging,MOON warmup is added to correct the local training of individual edge servers and improve the model’s effectiveness by calculating and back-propagating a model-contrastive loss,which represents the similarity between local and global *** addition,we propose a lightweight person ReID network,named multi-branch combined depth space network(MB-CDNet),to reduce the computing resource usage of the edge device when training and testing the person ReID ***-CDNet is a multi-branch version of combined depth space network(CDNet).We add a part branch and a global branch on the basis of CDNet and introduce an attention pyramid to improve the performance of the *** experimental results on open-access person ReID datasets demonstrate that FRM achieves better performance than existing baseline.

关键词： Person re-identification federated learning contrastive learning

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data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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DeepKet- Quantum Space-Efficient Word Embedding Layer for Steganalysis

DeepKet- Quantum Space-Efficient Word Embedding Layer for St...

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Artificial Intelligence For Internet of Things (aiIoT), International Conference on

作者： N Roshan Ahmed S. Shridevi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Advanced Data Science Vellore Institute of Technology Chennai India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Text-based Statistical steganography is one of the most non-human detectable methods of embedding hidden messages in plain text format which is useful in concealing information. Steganalysis is its counter, the process of detecting if a text has any encrypted data in it. This paper applies quantum computing to create DeepKet Embedding, which optimizes the space requirements for word embeddings similar to Word2Vec. DeepKet is benchmarked against existing embedding layers and a significant size reduction is achieved while maintaining accuracy for steganalysis.

关键词： Steganography Quantum computing Accuracy Benchmark testing Internet of Things Artificial intelligence

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Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on Retentive Network 7

Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on ...

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7th International Conference on computer Information science and Application Technology, CISAT 2024

作者： Su, Ruilin Zhang, Wanshan Yu, Dunhui Hubei University Wuhan China Wuhan430062 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China Hubei University Hubei Key Laboratory of Big Data Intelligent Analysis and Application Wuhan430062 China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan430062 China

ISBN: (纸本)9798350375107

Multi-hop Knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model's memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model's own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing. © 2024 IEEE.

关键词： Iterative decoding

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How to Detect ai-Generated Texts? 14

How to Detect AI-Generated Texts?

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Nguyen, Trung T. Hatua, Amartya Sung, Andrew H. Winona State University Department of Computer Science WinonaMN55987 United States AI Center of Excellence Fidelity Investments BostonMA02210 United States The University of Southern Mississippi School of Computing Sciences and Computer Engineering HattiesburgMS39401 United States

ISBN: (纸本)9798350304138

Recent advances in large language models (LLMs) have significantly improved the quality of synthetic text data. LLMs imitate human writing patterns to produce highly natural text, raising serious ethical, moral, legal, social, and economic concerns in various industries. To address these issues, we present two methods to distinguish Synthetically Generated Text (SGT) from Human-Written Text (HWT): machine learning and text similarity. Our methods include procedures for dataset creation, feature engineering, model training, and result analysis with feature importance analysis and explanation models. As part of our research, we created two datasets - a Wikipedia-based and a US Election 2024 news article-based dataset using ChatGPT, from which we obtained promising results of up to ***% accuracy. These datasets can be used as open-source datasets in future studies. We also identified a set of handcrafted features that can serve as the baseline feature set for future research. © 2023 IEEE.

关键词： ChatGPT document similarity explanation models feature engineering feature importance LLM

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An Automatic Method for Fast Synaptic Signal Detection and Count 22

An Automatic Method for Fast Synaptic Signal Detection and C...

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5th International Conference on Big data Technologies, ICBDT 2022

作者： Xu, Xiaofeng Lan, Yihua Li, He Qi, Qinglei Liu, Quan Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang473061 China

ISBN: (纸本)9781450396875

Synapses are the special connection structure between brain neurons, and they are the core element of brain plasticity and information transmission. In order to better understand the process of brain information transmission, it is necessary to quantitatively calculate the synapse, but the automatic identification of a large range of synaptic images is still a difficult problem. According to the characteristics of synaptic signal in microscopy optical mouse brain images, this paper proposes the automatic fast synapse recognition method based on density peak clustering, which solves the identification problem of dense small synaptic signal, and realizes the rapid synapse positioning and counting in a large brain area. The results show that the proposed methods in accuracy and efficiency bring great convenience to the researchers. CCS CONCEPTSĝ€¢Applied computing © 2022 ACM.

关键词： Signal detection

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Expression recognition algorithm based on CM-PFLD key point detection

Expression recognition algorithm based on CM-PFLD key point ...

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2021 International Conference on computer Graphics, Artificial Intelligence, and data Processing, ICCaiD 2021

作者： Zhang, Chao Hu, Siquan Shi, Zhiguo School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan China Beijing Big Data Center Beijing China

ISBN: (数字)9781510652170

ISBN: (纸本)9781510652163

Facial expression recognition is a hot research topic in artificial intelligence industry and has a good research prospect in various fields. At present, facial expression recognition processes the whole face directly, but the pixel value of non-feature region may bring some interference for feature descriptor extraction. Considering that the cartoon effect of the face can directly reflect the facial expression features. In order to make the network pay more attention to the information of the facial features and their surrounding pixels, this paper proposes an expression recognition algorithm based on key point detection of Covering multi-scale Practical Landmark Detector (CM-PFLD). Under this algorithm, this paper constructs a cartoon expression data set, which only retains the key points of facial expression information, and then classifies facial expression by directly locating the key points of facial expression information. In order to verify the feasibility of the expression recognition method in this paper. The experiment uses Fer2013 and CK data sets to produce cartoon expression data sets, and trains and compares cartoon data sets and original data sets respectively under the same network. The experimental results show that the method proposed in this paper has high detection accuracy and fast speed on standardized and neat data sets. On the data set with more unfavorable factors, the training accuracy of the two methods is similar, but the processing speed of the proposed method is faster. Experimental results show that the proposed method is feasible and effective. © 2022 SPIE.

关键词： Pixels

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