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arXiv 2024年

作者： Villena, Fabián Miranda, Luis Aracena, Claudio Department of Computer Science University of Chile Chile Millennium Institute for Foundational Research on Data Chile Department of Computer Science Catholic University of Chile Chile Faculty of Physical and Mathematical Sciences University of Chile Chile

Large language models (LLMs) allow us to generate high-quality human-like text. One interesting task in natural language processing (NLP) is named entity recognition (NER), which seeks to detect mentions of relevant information in documents. This paper presents llmNER, a Python library for implementing zero-shot and few-shot NER with LLMs;by providing an easy-to-use interface, llmNER can compose prompts, query the model, and parse the completion returned by the LLM. Also, the library enables the user to perform prompt engineering efficiently by providing a simple interface to test multiple variables. We validated our software on two NER tasks to show the library’s flexibility. llmNER aims to push the boundaries of in-context learning research by removing the barrier of the prompting and parsing steps. © 2024, CC BY-NC-ND.

关键词： Zero-shot learning

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Evolutionary Approach to Security Games with Signaling 31

Evolutionary Approach to Security Games with Signaling

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31st International Joint Conference on Artificial Intelligence, IJCAI 2022

作者： Żychowski, Adam Mańdziuk, Jacek Bondi, Elizabeth Venugopal, Aravind Tambe, Milind Ravindran, Balaraman Faculty of Mathematics and Information Science Warsaw University of Technology Poland Center for Research on Computation and Society Harvard University United States Robert Bosch Centre for Data Science and AI IIT Madras India Department of Computer Science and Engineering IIT Madras India

ISBN: (纸本)9781956792003

Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information. Incorporating both human and sensor defender resources strategically is the subject of recent work on Security Games with Signaling (SGS). However, current methods to solve SGS do not scale well in terms of time or memory. We therefore propose a novel approach to SGS, which, for the first time in this domain, employs an Evolutionary Computation paradigm: EASGS. EASGS effectively searches the huge SGS solution space via suitable solution encoding in a chromosome and a specially-designed set of operators. The operators include three types of mutations, each focusing on a particular aspect of the SGS solution, optimized crossover and a local coverage improvement scheme (a memetic aspect of EASGS). We also introduce a new set of benchmark games, based on dense or locally-dense graphs that reflect real-world SGS settings. In the majority of 342 test game instances, EASGS outperforms state-of-the-art methods, including a reinforcement learning method, in terms of time scalability, nearly constant memory utilization, and quality of the returned defender's strategies (expected payoffs). © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Chromosomes

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A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Online Affinity of Instructing Methods for Using Personal-Efficacy and Reading Capacious During Covid-19 5th

Online Affinity of Instructing Methods for Using Personal-Ef...

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5th International Conference on Communication, Device and networking, ICCDN 2021

作者： PremaLatha, V. Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Bhoi, Akash Kumar Ghosh, Uttam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Guntur Vaddeswaram522502 India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur603203 India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Sikkim Gangtok737102 India Department of Computer Science and Data Science Meharry School of Computational Sciences Nashville NashvilleTN United States

ISBN: (纸本)9789811920035

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be "more expensive, difficult, and time-consuming". Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes "comprehension reading work" is treated as fundamental in the proposal of disclosures. In case of any, "the programme appeared to positively affect contributing students". Techniques of classification like "decision tree and Mixed Model database Miner (MMDBM)" are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection. © 2023, The Author(s), under exclusive lice

关键词： Students

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Neural network Driven by Density and Parallel Features for Field-Road Mode Mining

Neural Network Driven by Density and Parallel Features for F...

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International Conference on computer Supported Cooperative Work in Design

作者： Fengqi Hao Xiyuan Zhao Cunxiang Bian Jinqiang Bai Qingyan Ding Faculty of Data Science City University of Macau Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Field-road mode mining (FRMM) has gained increasing attention because of its crucial role in machinery management. As the Global Navigation Satellite System (GNSS) is widely applied in agricultural machinery, many methods based on motion and spatial-temporal features of GNSS trajectory data have been proposed for FRMM. However, these methods ignore the density and parallel features of GNSS points. The two features are useful for FRMM because field points usually have a higher density and more parallel points than road points. Therefore, a neural network driven by density and parallel features is proposed for accurate FRMM. Firstly, a statistical method is designed to extract the density feature (i.e., the number of its neighbor points) and parallel feature (i.e., the number of approximately parallel points in its neighbors) of each point. Then, the two features and eight motion features (e.g., speed, direction, etc.) are fed into a neural network to extract valuable latent features. Finally, a linear classifier is used to identify the field and road categories of GNSS points based on the latent features. Experimental results show that our method outperforms state-of-the-art methods and achieves the accuracy of 91.69% and 86.44% on public Wheat and Paddy datasets, respectively.

关键词： Global navigation satellite system Accuracy Statistical analysis Federated learning Roads Neural networks Time series analysis Feature extraction Trajectory data mining

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Assessing the Improvements Brought by Artificial Intelligence on the Prediction of Aerodynamic Coefficients

Assessing the Improvements Brought by Artificial Intelligenc...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Zerouaoui, Jad Alaoui, Altaf Ettaki, Badia Chakir, ElMahjoub Laboratory of Material and Subatomic Physics Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

The implementation of Artificial Intelligence in the aerospace field is fairly new and various domains of the aerospace discipline are to be explored. This paper provides a general review on the upbringing and improvements of Artificial Intelligence as a pilar of the next generation of scientific research. Applications and developments brought by Artificial Intelligence and Deep Learning to the aerospace industry generally, mainly the prediction of aerodynamic coefficients and design optimization using intricate neural networks replicating the process of the human brain. Scientific developments participated in improving major areas in the aerospace field. Due to the expensive and time-consuming process related to the complexity of the geometry and the precision of the results required, researchers integrated surrogate models based on Deep Learning in order to improve the calculation process of the aerodynamic coefficients. On one hand, this paper introduces and discusses the general architecture of several networks used to enhance the calculation ability of the machine, while presenting the process of each work along with recounting the different methods employed. On the other hand, a discussion regarding the challenges presented with utilizing Computational Fluid Dynamics while assessing and comparing the results brought by surrogate models based on efficiency and time consumption is also tackled. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Evaluating the Impact of dataset Size on Univariate Prediction Techniques for Moroccan Agriculture

Evaluating the Impact of Dataset Size on Univariate Predicti...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Ed-daoudi, Rachid Alaoui, Altaf Zerouaoui, Jad Ettaki, Badia Zerouaoui, Jamal Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

Learning models used for prediction are mostly developed without taking into account the size of datasets that can produce models of high accuracy and better performance. Although, the general believe is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size depends on the circumstances and context of prediction. This means that what makes a dataset to be considered as being big or small is controversial. In this paper, the ability of the predictive model to adapt to a particular size of data in training is examined. The study experiments on three different sizes of Moroccan agricultural data using a variety of statistical and Machine Learning techniques, to create predictive models with a view to establishing if the size of data has any effect on the accuracy of a model. The output of each model is measured using the Mean Absolute Error (MAE) and r-squared, and comparisons are made. The results of training the models through the three partitioned dataset show that, the models trained with the smallest and largest size of training data appear to be less accurate, while the models trained with a medium sized dataset delivers a much better results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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EML-SAM: Incorporating Multi-Scale Fusion in SAM for Cine-CMR Segmentation

EML-SAM: Incorporating Multi-Scale Fusion in SAM for Cine-CM...

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International Conference on computer Supported Cooperative Work in Design

作者： Huijuan Hao Wenpeng Wang Qingyan Ding Haibo Cheng Fengqi Hao Hu Zhang Jinqiang Bai Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Accurate and reproducible assessment of myocardial conditions is essential for the diagnosis of prior infarctions, cardiomyopathies, and inflammatory diseases. Although cardiac magnetic resonance imaging (CMR) is regarded as the gold standard for evaluating myocardial anatomy and function, manual segmentation remains labor-intensive and susceptible to variability. The demand for high-resolution images, dense predictions, and comprehensive segmentation across all phases of the cardiac cycle introduces significant challenges for Cine-CMR analysis. To address these issues, we present EML-SAM, an innovative interactive segmentation model based on the Segment Anything Model (SAM). EML-SAM incorporates an early-mid-late (EML) multi-scale fusion strategy, effectively mitigating the premature dilution of interaction information, thereby reducing segmentation errors and enhancing feature utilization. Furthermore, the model utilizes a multi-scale linear attention (MLA) transformer to establish an efficient global receptive field. Comprising 12 Transformer layers, the model integrates multi-scale attention with multi-scale perceptrons, striking a balance between computational efficiency and capacity. The proposed methodology offers a robust and efficient solution for high-resolution Cine-CMR segmentation, delivering accurate and real-time segmentation performance throughout the various stages of temporal changes within the cardiac cycle.

关键词： Image segmentation Accuracy Computational modeling Refining Myocardium Transformers Real-time systems Reproducibility of results Reliability Diseases

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Wearable Biosensors: Transformative Innovations and Future Pathways for Continuous Health Monitoring

Wearable Biosensors: Transformative Innovations and Future P...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Trupti Thute Amit Gudadhe Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Wearable biosensors are revolutionizing healthcare by enabling continuous monitoring of health status, transforming it from an event-based process to an ongoing one. When integrated with AI, nanotechnology, and IoMT (Internet of Medical Things), these devices enhance precision care, telediagnosis, and chronic disease management. Advancements in electrochemical, optical, and biochemical sensors, along with innovations in energy harvesting and miniaturized flexible materials, improve both the performance and comfort of these devices. Wearable biosensors are now crucial for everyday fitness, disease prediction, and the seamless exchange of data between m-health apps and EHRs. Despite these advancements, challenges remain, such as data security, system integration, and user incentives. Addressing these concerns through enhanced security features, standardized protocols, and user-focused designs is critical. The future development of wearable biosensors is aimed at leveraging cost-effective energy sources, utilizing AI for data analysis, and improving user convenience, making significant strides in preventive and personalized healthcare.

关键词： Integrated optics Technological innovation System integration Biosensors Optical sensors Energy harvesting Biomedical monitoring Monitoring Nanotechnology Diseases

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A Trace-Selection Preprocessing Approach of Discovering Structurally Complete ICN-Process Models from Process Logs 3rd

A Trace-Selection Preprocessing Approach of Discovering Str...

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3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Pham, Dinh-Lam Batzaya, Tsagaantsooj Kim, Kyoung-Sook Chang, Jeong-Hyun Kim, Kwanghoon Pio CCSRI Kyonggi University Gyeonggi Suwon16227 Korea Republic of Department of Public Safety Bigdata Graduate School of Kyonggi University Gyeonggi Suwon16227 Korea Republic of Data Process Engineering Research Laboratory Faculty of AI Computer Science and Engineering Kyonggi University Gyeonggi Suwon16227 Korea Republic of

ISBN: (纸本)9783031809422

In business process life-cycle management and reengineering through process mining, it is crucial for the process mining system to discover structurally safe and complete business process models from process logs. However, most process mining systems typically suffer from discovering inaccurate and unstructured business process models due to various types of anomalous traces hidden in process logs. In this paper, we therefore propose a process log preprocessing approach that purifies a corresponding process log dataset by selecting a group of reasonable process log traces and applying to discover an essential business process model satisfying the structural completeness requirement of matched-pairing and proper-nesting properties. The theoretical basis of the proposed approach is the mathematical graph model of information control net model that is the typical representation of business process models. Finally, we practically demonstrate the conceptual excellence and functional correctness of the proposed preprocessing approach through experimental verifications using real business process enactment log datasets available in the 4TU Center for research data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reengineering

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