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arXiv 2022年

作者： Love, Rachel Law, Edith Cohen, Philip R. Kulić, Dana The Department of Electrical and Computer Systems Engineering Monash University Australia The David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada Openstream Inc. The Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Australia The Department of Electrical and Computer Systems Engineering Department of Mechanical and Aerospace Engineering Monash University Australia

Conversational teachable agents offer a promising platform to support learning, both in the classroom and in remote settings. In this context, the agent takes the role of the novice, while the student takes on the role of teacher. This framing is significant for its ability to elicit the Protégé effect in the student-teacher, a pedagogical phenomenon known to increase engagement in the teaching task, and also improve cognitive outcomes. In prior work, teachable agents often take a passive role in the learning interaction, and there are few studies in which the agent and student engage in natural language dialogue during the teaching task. This work investigates the effect of teaching modality when interacting with a virtual agent, via the web-based teaching platform, the Curiosity Notebook. A method of teaching the agent by selecting sentences from source material is compared to a method paraphrasing the source material and typing text input to teach. A user study has been conducted to measure the effect teaching modality on the learning outcomes and engagement of the participants. The results indicate that teaching via paraphrasing and text input has a positive effect on learning outcomes for the material covered, and also on aspects of affective engagement. Furthermore, increased paraphrasing effort, as measured by the similarity between the source material and the material the teacher conveyed to the robot, improves learning outcomes for participants. © 2022, CC BY.

关键词： Students

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Assessment of Mixing Efficiency in a Planar Passive Micromixer With T-shaped Configuration

Assessment of Mixing Efficiency in a Planar Passive Micromix...

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Electrical, Energy, and artificial intelligence (EICEEAI), International engineering Conference on

作者： Jafar Ababneh Mohanad A. Deif Ahmad Solyman Mansour Y. Ibrahim Mohamed Hafez Yosef M. Ashmawy The Faculty of Information Technology Cyber security Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Artificial intelligence College of Information Technology Misr University for Science & Technology (MUST) 6th of October City Egypt School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK Department of Bioelectronics Modern University of Technology and Information (MTI) University Cairo Egypt Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Microfluidic devices have garnered considerable interest owing to their prospective utilization in diverse domains, encompassing chemical synthesis, biological analysis, and medicinal research. Micromixers are critical in adequate fluid mixing at a microscale within the array of devices under consideration. This study aims to offer a comprehensive analysis of the efficacy of the T-shaped micromixer configuration in scenarios that necessitate accurate and expeditious mixing. This study examines the performance of a T micromixer through simulation and analysis. The findings demonstrate that T micromixers exhibit some drawbacks that result in suboptimal mixing efficiency. The attainment of a desirable level of mixing efficiency can be accomplished by utilizing splitting-recombination and chaotic advection mechanisms. The study's outcomes indicate that the T micromixer demonstrates its maximum mixing effectiveness, roughly 60% when the Reynolds number (Re) is at or below 0.5. Nevertheless, it has been observed that the T micromixer encounters a decrease in mixing effectiveness as the Reynolds number escalates within the range of 0.5 to 15.

关键词： Performance evaluation Fluids Ethanol Software Biology Computational efficiency Microfluidics

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Blockchain and Machine Learning Integration for Safe data Sharing in Finance and HR

Blockchain and Machine Learning Integration for Safe Data Sh...

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Intelligent Algorithms for Computational intelligence Systems (IACIS), International Conference on

作者： T. B Sivakumar E. Prathiba Shweta Sharma Rashmi Saket Prasad M. S. Ramaratnam V. Krishnamoorthy Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Mailam Engineering College Mailam India Department of Business Administration Shambhunath Institute of Engineering & Technology Prayagraj India D Y Patil PGDM Institute Pune Akurdi India Department of Management Studies Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Kanchipuram India Department of Management Studies Kongu Engineering College Erode Perundurai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Modern finance and HR businesses have challenges in exchanging data due to centralized systems that are subject to hacking and lack transparency. The study proposes employing blockchain technology and machine intelligence (ML) to address these issues holistically. The proposed system enhances integrity and resilience to assaults by utilizing a decentralized ledger for secure data exchange. ML algorithms enable predictive analytics, risk management, and personalized insights. The proposed system outperforms the existing system in terms of accuracy, security, and efficiency. The accuracy of fraud detection increases from 0.75 to 0.90, as does the estimate of staff turnover from 0.80 to 0.95. The proposed system achieves an overall accuracy of 95%, surpassing the existing system's 85%. Blockchain and ML integration can alter data management methods in finance and HR industries, with improved security measures resulting in a risk score of 2 compared to 7 in the existing system.

关键词： Industries Training Adaptation models Accuracy Heuristic algorithms data security Computational modeling Finance Blockchains Predictive analytics

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RankCSE: Unsupervised Sentence Representation Learning via Learning to Rank

arXiv

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arXiv 2023年

作者： Liu, Jiduan Liu, Jiahao Wang, Qifan Wang, Jingang Wu, Wei Xian, Yunsen Zhao, Dongyan Chen, Kai Yan, Rui Wangxuan Institute of Computer Technology Peking University China Center for Data Science AAIS Peking University China Meituan China Meta AI United States National Key Laboratory of General Artificial Intelligence China BIGAI Beijing China School of Economics Peking University China Gaoling School of Artificial Intelligence Renmin University of China China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China

Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning has been widely adopted which derives high-quality sentence representations by pulling similar semantics closer and pushing dissimilar ones away. However, these methods fail to capture the fine-grained ranking information among the sentences, where each sentence is only treated as either positive or negative. In many real-world scenarios, one needs to distinguish and rank the sentences based on their similarities to a query sentence, e.g., very relevant, moderate relevant, less relevant, irrelevant, etc. In this paper, we propose a novel approach, RankCSE, for unsupervised sentence representation learning, which incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework. In particular, we learn semantically discriminative sentence representations by simultaneously ensuring ranking consistency between two representations with different dropout masks, and distilling listwise ranking knowledge from the teacher. An extensive set of experiments are conducted on both semantic textual similarity (STS) and transfer (TR) tasks. Experimental results demonstrate the superior performance of our approach over several state-of-the-art baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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data Driven Network Load Balancing: Leveraging Ai for Efficient Resource Allocation

SSRN

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SSRN 2025年

作者： Agal, Sanjay Odedra, Niyati Dhirubhai Department of Artificial Intelligence and Data Science Parul University Vadodara India Dr V R Godhania College of Engineering and Technology Porbandar India

This paper investigates the development of a data-driven framework for network load balancing that leverages artificial intelligence to enhance resource allocation efficiency. The research addresses the significant problem of inefficiencies in existing load balancing mechanisms, which struggle to adapt to dynamic network traffic patterns. Through the analysis of extensive datasets encompassing network traffic, application performance metrics, and resource utilization rates, the study identifies key insights demonstrating that AI-driven algorithms can significantly optimize load distribution, resulting in improved application performance and reduced latency. The findings reveal that implementing these AI methodologies leads to a marked enhancement in overall system responsiveness, particularly relevant in the context of healthcare, where timely access to data and resources can directly influence patient outcomes. By facilitating more efficient resource allocation, the proposed framework not only addresses current limitations in network management but also paves the way for the integration of advanced technologies in healthcare settings, ultimately fostering better patient care, reduced operational costs, and enhanced adaptability to future technological advancements. The implications of this research extend beyond healthcare, suggesting a transformative potential for various sectors requiring robust network infrastructures, thereby contributing to the broader discourse on AI applications in optimizing complex systems. © 2025, The Authors. All rights reserved.

关键词： Resource allocation

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Multi-objective Aerial IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Wang, Jiacheng Du, Hongyang Kang, Jiawen Huang, Kaibin Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Automation Guangdong University of Technology Guangzhou510006 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless networks. However, achieving a performance balance between communication and sensing in the dual-function radar communication (DFRC)-based ISAC system remains a significant challenge. In this paper, an aerial intelligent reflecting surface (IRS)-assisted ISAC system is explored, where a base station (BS) supports dual-functional operations, enabling both data transmission for multiple users and sensing for a blocked target, with the channel quality enhanced by an IRS mounted on the unmanned aerial vehicle (UAV). Moreover, we formulate an integrated communication, sensing, and energy efficiency multi-objective optimization problem (CSEMOP), which aims to maximize the communication rate of the users and the echo rate of the target, while minimizing UAV propulsion energy consumption by jointly optimizing the BS beamforming matrix, IRS phase shifts, the flight velocity and angle of the UAV. Considering the non-convexity, trade-off, and dynamic nature of the formulated CSEMOP, we propose a generative diffusion model-based deep deterministic policy gradient (GDMDDPG) method to solve the problem. Specifically, the diffusion model is incorporated into the actor network of DDPG to improve the action quality, with noise perturbation mechanism for better exploration and recent prioritized experience replay (RPER) sampling mechanism for enhanced training efficiency. Simulation results indicate that the GDMDDPG method delivers superior performance compared to the existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

arXiv

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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Deep Learning-based Identification and Classification of Rice Plant Diseases for Precision Agriculture

Deep Learning-based Identification and Classification of Ric...

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

作者： N. Venkatesh Swagata Sarkar A Sunil T.Guru Priya R Aarthi K.Mekala Devi School of CS&AI SR University &Warangal Artificial intelligence and Data Science Sri Sairam Engineering College Sai Leo Nagar West Tambaram Chennai computer science and engineering rajarajeswari college of engineering Ramohalli cross Bangalore Mysore road Kumbalgod Karnataka Electronics and Communication Engineering Periyar Maniammai Institute of Science and Technology PMIST VALLAM Department of ECE R.M.D Engineering College Chennai ECE Department S.A. Engineering college Chennai

Because they reduce both the quality and yield of crops, plant diseases seriously threaten our food supply’s reliability. Plant diseases often result in a significant reduction or complete absence of grain yield in various agricultural contexts. Classifying rice plant diseases and insects pose a significant challenge due to the intricate structure of their composition and the striking resemblance 5bserved among various disease types and insect species. Diseases and insects in crops must be discovered and categorized as soon as possible, ideally before they may spread. There are a number of methods for spotting plant diseases, but deep learning is now the gold standard. The authors provide a novel deep-learning approach for detecting diseases in rice fields using photographs. Convolutional Neural Networks with attention mechanism (CNNAM) can be used to detect diseases in rice plants. Due to its reliability in picture identification and classification, CNNs have been widely employed by researchers for plant disease identification. The identification of rice plant diseases, however, has only been the subject of a small number of studies. This research illuminates the present state of rice plant disease and the methods of disease detection based on Deep Learning. Deep learning models excel about more conventional machine learning methods. With the suggested model, authors were able to categorize rice photos with a 99.8 percent accuracy by learning latent patterns in the raw images.

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An AI-Enabled Framework Within Reach for Enhancing Healthcare Sustainability and Fairness

arXiv

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arXiv 2024年

作者： Huang, Bin Zhao, Changchen Liu, Zimeng Hong, Shenda Zhang, Baochang Lu, Hao Liu, Zhijun Wang, Wenjin Liu, Hui AI Research Center Hangzhou Innovation Institute Beihang University Zhejiang Hangzhou China School of Computer Science Hangzhou Dianzi University China School of Automation Science and Electrical Engineering Beihang University China Institute of Medical Technology Health Science Center of Peking University National Institute of Health Data Science Peking University China Institute of Artificial Intelligence Beihang University China Information Hub Hong Kong University of Science and Technology China Department of Biomedical Engineering City University of Hong Kong China Department of Biomedical Engineering Southern University of Science and Technology China Institute of Medical Information Chinese Academy of Medical Sciences Peking Union Medical College China

Good health and well-being is among key issues in the United Nations 2030 Sustainable Development Goals. The rising prevalence of large-scale infectious diseases and the accelerated aging of the global population are driving the transformation of healthcare technologies. In this context, establishing large-scale public health datasets, developing medical models, and creating decision-making systems with a human-centric approach are of strategic significance. Recently, by leveraging the extraordinary number of accessible cameras, groundbreaking advancements have emerged in AI methods for physiological signal monitoring and disease diagnosis using camera sensors. These approaches, requiring no specialized medical equipment, offer convenient manners of collecting large-scale medical data in response to public health events. Therefore, we outline a prospective framework and heuristic vision for a camera-based public health (CBPH) framework utilizing visual physiological monitoring technology. The CBPH can be considered as a convenient and universal framework for public health, advancing the United Nations Sustainable Development Goals, particularly in promoting the universality, sustainability, and equity of healthcare in low- and middle-income countries or regions. Furthermore, CBPH provides a comprehensive solution for building a large-scale and human-centric medical database, and a multi-task large medical model for public health and medical scientific discoveries. It has a significant potential to revolutionize personal monitoring technologies, digital medicine, telemedicine, and primary health care in public health. Therefore, it can be deemed that the outcomes of this paper will contribute to the establishment of a sustainable and fair framework for public health, which serves as a crucial bridge for advancing scientific discoveries in the realm of AI for medicine (AI4Medicine). Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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Enhancing Robustness of Time Series Predictions with Deep Learning Techniques

Enhancing Robustness of Time Series Predictions with Deep Le...

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International Conference on Futuristic Technologies (INCOFT)

作者： Nithya M Neeraj Sharma K.S. Bhuvaneshwari AR Aravind Snehal R. Rathi Umesh.M. Daivagna Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Artificial Intelligence and Data scienceKarpagam College of Engineering Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Department of uGDX ATLAS SkillTech University Mumbai Maharastra India

The use of deep studying techniques for making time collection predictions has multiplied whole, because of the truth that these techniques have proven to have excellent effects on obligations, which include type, object detection, and herbal language processing. This paper looks at how deep mastering methods can enhance the robustness and accuracy of time collection predictions. The authors explore various processes using extended short-term reminiscence networks, characteristic engineering, and switch learning to make more correct and robust predictions. They also examine the consequences of including noise in the dataset and combining deep getting to know architectures with different devicegaining knowledge of algorithms. Effects show that the deep getting-to-know techniques can lessen prediction mistakes, even if the facts are noisy or incomplete while outperforming conventional strategies. In conclusion, the authors indicate that those deeply gaining systems knowledge can make more fantastic and accurate time collection predictions.

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