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arXiv 2022年

作者： Xu, Yang Li, Li Xu, Haiyang Huang, Songfang Huang, Fei Cai, Jianfei School of Computer Science and Engineering of Southeast University China Bell Honors School Nanjing University of Posts and Telecommunications Nanjing China Alibaba Group China Department of Data Science and AI Monash University Australia

Image Captioning (IC) has achieved astonishing developments by incorporating various techniques into the CNN-RNN encoder-decoder architecture. However, since CNN and RNN do not share the basic network component, such a heterogeneous pipeline is hard to be trained end-to-end where the visual encoder will not learn anything from the caption supervision. This drawback inspires the researchers to develop a homogeneous architecture that facilitates end-to-end training, for which Transformer is the perfect one that has proven its huge potential in both vision and language domains and thus can be used as the basic component of the visual encoder and language decoder in an IC pipeline. Meantime, self-supervised learning releases the power of the Transformer architecture that a pre-trained large-scale one can be generalized to various tasks including IC. The success of these large-scale models seems to weaken the importance of the single IC task. However, we demonstrate that IC still has its specific significance in this age by analyzing the connections between IC with some popular self-supervised learning paradigms. Due to the page limitation, we only refer to highly important papers in this short survey and more related works can be found at https://***/SjokerLily/awesome-image-captioning. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal encoding

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data reinforcement control technique-based monitoring and controlling of environmental factors for IoT applications

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Arabian Journal of Geosciences 2022年第7期15卷 1-8页

作者： Mehbodniya, Abolfazl Haq, Mohd Anul Kumar, Anil Ismail, Mohd Erfy Dahiya, Priyanka Karupusamy, Sathishkumar Department of Electronics and Communications Engineering Kuwait College of Science and Technology (KCST) Doha Area Kuwait Department of Computer Science College of Computer and Information Sciences Majmaah University AlMajmaah Saudi Arabia Data Science Research Group School of Computing DIT University Dehradun India Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Gobi Arts & Science College Gobichettipalayam India

In recent years, environmental monitoring is essential to minimize environmental problems. However, the need for more and more aquatic ecosystems from pollution, increasing climate change, and direct environment loss, so that they can continue to be thoroughly monitored for risks. In this system, the implementation of a data reinforcement control technique (DRCT) using the Internet of Things (IoT) systems is presented to aquatic inquiry environments. In particular, it describes an automated system using data from the design and implementation of recording environments that monitor sensors and installation parameters in lakes and rivers. Design and low-cost real-time monitoring, alert worker development, polluted notification data are confirmed around. Various parameters such as air quality, temperature, humidity, and PIC controller sound intensity sensor, collection, and upload data to the cloud using the Wi-Fi module. It then forwards to a digital dashboard for confirmation on the web via a smartphone or cloud platform exception notification to alert the user. The industry will be the power of closed industrial electricity if they do not take any steps to reduce pollution within a specified period until they are paid under government regulations and pollution control and the amount of pollution in the city. The proposed system is simulated in the PROTEUS 8.1 simulator to analyze the system output.

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Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification

Equilibrium Optimizer with Deep Learning Model for Autism Sp...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： A. Praveena N. Senthamilarasi T. S. Karthik Abirami S.K Vijayakrishna Rapaka E Shyamali Das Department of Computer Science and Engineering Jansons Institute of Technology Karumathampatti Coimbatore Tamilnadu India Department of Information Technology Panimalar Engineering College Chennai Tamilnadu India Department of Electronics and Communication Engineering Aditya College of Engineering and Technology Surampalem Andhrapradesh India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamilnadu India Data Science Indian Institute of Technology Madras Chennai Tamilnadu India Department of Computer Science CMR Group of Institutions (SOSS) Bangalore Karnataka India

ISBN: (纸本)9781665460859

Autism Spectrum Disorder (ASD) is a developing disorder if the symptoms develop obvious in the initial years of age but it could be present in some age groups. ASD is mental health problem that affects communicational, social, and non-verbal performances. It could not be cured entirely but is decreased when identified initially. The primary analysis was hampered by the difference and severity of ASD symptoms and containing symptoms usually realized in other mental health problems as well. With the application of machine learning (ML) for the predictive and recognition of several diseases with optimum accuracy, a ray of hope to initial recognition of ASD dependent upon many physiological and physical parameters is projected. This article designs an Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification (EODL-ASDC) technique. The presented EODL-ASDC technique mainly focuses on the identification and classification of ASD. To attain this, the presented EODL-ASDC technique exploits the deep belief network (DBN) system to perform the classification procedure. In addition, the EO algorithm is employed for the optimal hyperparameter tuning of the DBN approach. To demonstrate the enhanced ASD classification result of the EODL-ASDC approach, an extensive range of experimental evaluates was executed. The experimental results demonstrate the improvements of the EODL-ASDC technique over other approaches.

关键词： Deep learning Autism Renewable energy sources Automation Mental health Physiology Classification algorithms

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

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

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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

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No Fear of Representation Bias Graph Contrastive Learning with Calibration and Fusion

SSRN

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SSRN 2024年

作者： Li, Jindong Liu, Yixin Xing, Qianli Wang, Qi Pan, Shirui The School of Artificial Intelligence Jilin University Changchun130012 China The Department of Data Science and AI Monash University MelbourneVIC3800 Australia The College of Computer Science and Technology Jilin University Changchun130012 China The School of Information and Communication Technology Griffith University Gold CoastQLD4222 Australia Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China

Graph contrastive learning (GCL) is receiving growing attention due to its promising self-supervised learning capability on graph-structured data. While GCL approaches have demonstrated significant potential, their inherited stochastic perturbation-based augmentation causes the representation bias on representation hyperspace. Such a serious issue, unfortunately, causes the representation drift between the distributions of original and augmented embeddings, misleading the contrastive training for the graph neural network encoders. Moreover, the information loss caused by bias can further result in incomplete representation problem, hindering the self-supervised framework from capturing comprehensive structural and attributive patterns. To address these challenges, this paper introduces the CAlibratioN aNd FusiON model (CANNON for short). CANNON introduces a cross-view calibrating contrast mechanism to enhance the agreement between the original and augmented representations, which effectively alleviates the representation drift. Concurrently, CANNON overcomes the incomplete representation problem with a fused-view calibrating contrast mechanism for contrastive learning application on the fused embeddings, where a transformer-based cross-view fusion block is used to integrate two augmented views into a comprehensive one. The extensive experimental results, executed on 9 real-world datasets, demonstrate that CANNON significantly outperforms existing state-of-the-art methods. The code is available at https://***/jindongli-Ai/CANNON. © 2024, The Authors. All rights reserved.

关键词： Calibration

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An empirical examination of balancing strategy for counterfactual estimation on time series 24

An empirical examination of balancing strategy for counterfa...

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Proceedings of the 41st International Conference on Machine Learning

作者： Qiang Huang Chuizheng Meng Defu Cao Biwei Huang Yi Chang Yan Liu School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China Department of Computer Science University of Southern California California Los Angeles Halicioğlu Data Science Institute University of California San Diego San Diego California School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Changchun Jilin China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.

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Global brain network modularity dynamics after local optic nerve damage: an EEG-tracking study

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Brain Stimulation 2021年第6期14卷 1609-1610页

作者： Zheng Wu Bernhard Sabel Institute of Medical Psychology Medical Faculty Otto-von-Guericke University of Magdeburg Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke University of Magdeburg Germany

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Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective

arXiv

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arXiv 2022年

作者： Wang, Jihong Luo, Minnan Li, Jundong Liu, Ziqi Zhou, Jun Zheng, Qinghua The Ministry of Education Key Lab for Intelligent Networks and Network Security School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China The Department of Electrical and Computer Engineering Department of Computer Science School of Data Science University of Virginia United States The Ant Financial Services Group Zhejiang Hangzhou310000 China

Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rendering them infeasible when label information is not available. A straightforward direction is to employ the widely used Infomax technique from typical Unsupervised Graph Representation Learning (UGRL) to learn robust unsupervised representations. Nonetheless, directly transplanting the Infomax technique from typical UGRL to robust UGRL may involve a biased assumption. In light of the limitation of Infomax, we propose a novel unbiased robust UGRL method called Robust Graph Information Bottleneck (RGIB), which is grounded in the Information Bottleneck (IB) principle. Our RGIB attempts to learn robust node representations against adversarial perturbations by preserving the original information in the benign graph while eliminating the adversarial information in the adversarial graph. There are mainly two challenges to optimize RGIB: 1) high complexity of adversarial attack to perturb node features and graph structure jointly in the training procedure;2) mutual information estimation upon adversarially attacked graphs. To tackle these problems, we further propose an efficient adversarial training strategy with only feature perturbations and an effective mutual information estimator with subgraph-level summary. Moreover, we theoretically establish a connection between our proposed RGIB and the robustness of downstream classifiers, revealing that RGIB can provide a lower bound on the adversarial risk of downstream classifiers. Extensive experiments over several benchmarks and downstream tasks demonstrate the effectiveness and superiority of our proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning systems

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

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

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

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. © 2022 Association for Computing Machinery.

关键词： Ethanol

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SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking 25

SoAy: A Solution-based LLM API-using Methodology for Academi...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Yuanchun Wang Jifan Yu Zijun Yao Jing Zhang Yuyang Xie Shangqing Tu Yiyang Fu Youhe Feng Jinkai Zhang Jingyao Zhang Bowen Huang Yuanyao Li Huihui Yuan Lei Hou Juanzi Li Jie Tang School of Information Renmin University of China Beijing China & Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Institute of Education Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Information Renmin University of China Beijing China & Engineering Research Center of Database and Business Intelligence MOE Beijing China School of Information Renmin University of China Beijing China Zhipu AI Beijing China

ISBN: (纸本)9798400712456

Applying large language models (LLMs) to academic API usage shows promise in reducing researchers' efforts to seek academic information. However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution. This solution simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency. LLMs are aligned with this solution-oriented, code-based reasoning method by automatically enumerating valid API coupling sequences and transforming them into queries and executable *** evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://***/RUCKBReasoning/SoAy.

关键词： academic information seeking

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