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MFFLEN: Multi-Label Text Classification Based on Multi-Feature Fusion and Label Embedding

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MFFLEN: Multi-Label Text Classification Based on Multi-Featu...

IEEE International Conference on Systems, Man and Cybernetics

作者： Qiliang Gu Shuo Zhao Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Shandong Engineering Research Center of Big Data Applied Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

To address the challenges associated with insufficiently extracting and utilizing features at different levels, overlooking the connection between label meanings and text, and facing problems of over-compression or information loss when extracting global information using recurrent neural networks in the field of multi-label text categorization, this paper introduces an innovative model known as MFFLEN (MultiFeature Fusion and Label Embedding Neural Network). First, a back-translated enhanced label set is constructed by back-translated splicing enhancement of the original label set. This set, together with the text, is then input into the embedding layer, which consists of the pre-trained model of bert-baseChinese, thus establishing the initial connection between the text and the labels within the same vector space. Then, to comprehensively extract multi-level semantic features, the model uses a convolutional layer to extract local features and an embedding layer to extract sentence-level features. A bidirectional attention embedded GRU (BAE-GRU) layer is used to extract hybrid finegrained features, which are then fed into the attention layer to further extract hybrid labeled features based on labeling information. Finally, these three different types of features are fused and multi-label text classification results are obtained using a classifier. The experiments proved that the MFFLEN model achieved 73.82% and 88.44% macro-F1 and 88.00% and 88.86% micro-F1 on the two datasets CAIL 2018 Small and CAIL 2018 Split, respectively, which is better than other baseline models.

关键词： Measurement Recurrent neural networks Splicing Text categorization Semantics Multi label classification Logic gates Feature extraction Vectors data mining

FERI: Feature Enhancement and Relational Interaction for Image-text Matching

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FERI: Feature Enhancement and Relational Interaction for Ima...

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Shuo Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method.

关键词： computer vision Fuses Image matching Semantics Feature extraction Natural language processing

Lessons learned from the 1st ariel machine learning challenge: correcting transiting exoplanet light curves for stellar spots

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arXiv 2020年

作者： Nikolaou, Nikolaos Waldmann, Ingo P. Tsiaras, Angelos Morvan, Mario Edwards, Billy Yip, Kai Hou Tinetti, Giovanna Sarkar, Subhajit Dawson, James M. Borisov, Vadim Kasneci, Gjergji Petković, Matej Stepišnik, Tomaž Al-Ubaidi, Tarek Bailey, Rachel Louise Granitzer, Michael Julka, Sahib Kern, Roman Ofner, Patrick Wagner, Stefan Heppe, Lukas Bunse, Mirko Morik, Katharina Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom School of Physics and Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Department of Computer Science University of Tuebingen Tuebingen Germany Jožef Stefan Institute Ljubljana Slovenia DCCS Austria Space Research Institute Austrian Academy of Sciences Austria Chair of Data Science University of Passau Germany Know-Center GmbH Research Center for Data-Driven Business & Big Data Analytics Austria Commission for Astronomy Austrian Academy of Sciences Graz Austria Artificial Intelligence Group TU Dortmund University Germany

The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is to identify the effects of spots visually and correct for them manually or discard the affected data. This paper explores a first step towards fully automating the efficient and precise derivation of transit depths from transit light curves in the presence of stellar spots. The methods and results we present were obtained in the context of the 1st Machine Learning Challenge organized for the European Space Agency’s upcoming Ariel mission. We first present the problem, the simulated Ariel-like data and outline the Challenge while identifying best practices for organizing similar challenges in the future. Finally, we present the solutions obtained by the top-5 winning teams, provide their code and discuss their implications. Successful solutions either construct highly nonlinear (w.r.t. the raw data) models with minimal preprocessing –deep neural networks and ensemble methods– or amount to obtaining meaningful statistics from the light curves, constructing linear models on which yields comparably good predictive performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Extrasolar planets

Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem

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arXiv 2022年

作者： Tahir, Barzan Hussein Rashid, Tarik A. Rauf, Hafiz Tayyab Bacanin, Nebojsa Chhabra, Amit Vimal, S. Yaseen, Zaher Mundher Department of Computer Science and Engineering University of Kurdistan Helwer Erbil Iraq Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia USQ's Advanced Data Analytics Research Group School of Mathematics Physics and Computing University of Southern Queensland QLD4350 Australia New era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq

Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced Fitness Dependent Optimizer. Correspondingly, various standard estimations are used to prove the stability

关键词： Decision making

Miniature UAV Empowered Reconfigurable Energy Harvesting Holographic Surfaces in THz Cooperative Networks

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arXiv 2024年

作者： Song, Yifei Jalali, Jalal Lemic, Filip Devroye, Natasha Famaey, Jeroen Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States IDLab University of Antwerp - imec Sint-Pietersvliet 7 Antwerp2000 Belgium Wireless Communication Research Group JuliaSpace Inc. ChicagoIL United States AI-Driven Systems Lab i2Cat Foundation Spain University of Illinois Chicago ChicagoIL United States

This paper focuses on enhancing the energy efficiency (EE) of a cooperative network featuring a ‘miniature’ unmanned aerial vehicle (UAV) that operates at terahertz (THz) frequencies, utilizing holographic surfaces to improve the network’s performance. Unlike traditional reconfigurable intelligent surfaces (RIS) that are typically used as passive relays to adjust signal reflections, this work introduces a novel concept: Energy harvesting (EH) using reconfigurable holographic surfaces (RHS) mounted on the miniature UAV. In this system, a source node facilitates the simultaneous reception of information and energy signals by the UAV, with the harvested energy from the RHS being used by the UAV to transmit data to a specific destination. The EE optimization involves adjusting non-orthogonal multiple access (NOMA) power coefficients and the UAV’s flight path, considering the peculiarities of the THz channel. The optimization problem is solved in two steps. Initially, the trajectory is refined using a successive convex approximation (SCA) method, followed by the adjustment of NOMA power coefficients through a quadratic transform technique. The effectiveness of the proposed algorithm is demonstrated through simulations, showing superior results when compared to baseline methods. © 2024, CC BY.

关键词： Unmanned aerial vehicles (UAV)

Layerwise perturbation-based adversarial training for hard drive health degree prediction

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arXiv 2018年

作者： Zhang, Jianguo Wang, Ji He, Lifang Li, Zhao Yu, Philip S. Department of Computer Science University of Illinois at Chicago ChicagoIL United States College of Systems Engineering National University of Defense Technology Changsha Hunan China Weill Cornell Department of Healthcare Policy & Research Cornell University NY United States Alibaba Group Hangzhou Zhejiang China Shanghai Institute for Advanced Communication and Data Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China

With the development of cloud computing and big data, the reliability of data storage systems becomes increasingly important. Previous researchers have shown that machine learning algorithms based on SMART attributes are effective methods to predict hard drive failures. In this paper, we use SMART attributes to predict hard drive health degrees which are helpful for taking different fault tolerant actions in advance. Given the highly imbalanced SMART datasets, it is a nontrivial work to predict the health degree precisely. The proposed model would encounter overfitting and biased fitting problems if it is trained by the traditional methods. In order to resolve this problem, we propose two strategies to better utilize imbalanced data and improve performance. Firstly, we design a layerwise perturbation-based adversarial training method which can add perturbations to any layers of a neural network to improve the generalization of the network. Secondly, we extend the training method to the semi-supervised settings. Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model. Our extensive experiments on two real-world hard drive datasets demonstrate the superiority of the proposed schemes for both supervised and semi-supervised classification. The model trained by the proposed method can correctly predict the hard drive health status 5 and 15 days in advance. Finally, we verify the generality of the proposed training method in other similar anomaly detection tasks where the dataset is imbalanced. The results argue that the proposed methods are applicable to other domains. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

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Optimizing Ethanol Production in Escherichia Coli Using a Hy...

Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

Using GPT-4 to guide causal machine learning

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arXiv 2024年

作者： Constantinou, Anthony C. Kitson, Neville K. Zanga, Alessio research group School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Informatics Systems and Communication University of Milano - Bicocca Milan Italy Data Science and Advanced Analytics F. Hoffmann - La Roche Ltd Basel Switzerland

Since its introduction to the public, ChatGPT has had an unprecedented impact. While some experts praised AI advancements and highlighted their potential risks, others have been critical about the accuracy and usefulness of Large Language Models (LLMs). In this paper, we are interested in the ability of LLMs to identify causal relationships. We focus on the well-established GPT-4 (Turbo) and evaluate its performance under the most restrictive conditions, by isolating its ability to infer causal relationships based solely on the variable labels without being given any other context by humans, demonstrating the minimum level of effectiveness one can expect when it is provided with label-only information. We show that questionnaire participants judge the GPT-4 graphs as the most accurate in the evaluated categories, closely followed by knowledge graphs constructed by domain experts, with causal Machine Learning (ML) far behind. We use these results to highlight the important limitation of causal ML, which often produces causal graphs that violate common sense, affecting trust in them. However, we show that pairing GPT-4 with causal ML overcomes this limitation, resulting in graphical structures learnt from real data that align more closely with those identified by domain experts, compared to structures learnt by causal ML alone. Overall, our findings suggest that despite GPT-4 not being explicitly designed to reason causally, it can still be a valuable tool for causal representation, as it improves the causal discovery process of causal ML algorithms that are designed to do just that. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

A Mixing Time Lower Bound for a Simplified Version of BART

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arXiv 2022年

作者： Ronen, Omer Saarinen, Theo Tan, Yan Shuo Duncan, James Yu, Bin Department of Statistics UC Berkeley United States Department of Electrical Engineering and Computer Sciences UC Berkeley United States Center for Computational Biology UC Berkeley United States Department of Statistics and Data Science National University of Singapore Singapore Microsoft Research United States Group in Biostatistics UC Berkeley United States

Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression algorithm. The posterior is a distribution over sums of decision trees, and predictions are made by averaging approximate samples from the posterior. The combination of strong predictive performance and the ability to provide uncertainty measures has led BART to be commonly used in the social sciences, biostatistics, and causal inference. BART uses Markov Chain Monte Carlo (MCMC) to obtain approximate posterior samples over a parameterized space of sums of trees, but it has often been observed that the chains are slow to mix. In this paper, we provide the first lower bound on the mixing time for a simplified version of BART in which we reduce the sum to a single tree and use a subset of the possible moves for the MCMC proposal distribution. Our lower bound for the mixing time grows exponentially with the number of data points. Inspired by this new connection between the mixing time and the number of data points, we perform rigorous simulations on BART. We show qualitatively that BART’s mixing time increases with the number of data points. The slow mixing time of the simplified BART suggests a large variation between different runs of the simplified BART algorithm and a similar large variation is known for BART in the literature. This large variation could result in a lack of stability in the models, predictions and posterior intervals obtained from the BART MCMC samples. Our lower bound and simulations suggest increasing the number of chains with the number of data points. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision trees