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Multi-view ontology alignment visualization (MOAV): A human-cognition based ontology alignment visualization technique

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Journal of Network Intelligence

Journal of Network Intelligence 2020年第4期5卷 198-210页

作者： Chen, Jie Zhang, Jie Xue, Xingsi Huang, Yikun Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guangxi Guilin541004 China Department of Information Technology Concord University College Fujian Normal University No.68 Xueyuan Road University Town Minhou FuzhouFujian350117 China

To further improve the quality of ontology alignment, it is necessary for an ontology matcher to introduce a user’s knowledge into its automatic matching process, which yields the development of interactive ontology matching techniques. Since validating problematic entity correspondences is a difficult cognition task, user interaction based on Ontology Alignment Visualization (OAV) has become the critical component of an interactive ontology matcher, which directly affects the quality of validating result and the efficiency of validating process. The existing OAV tools are mainly developed based on their designers’ subjective feelings and experiences, which do not take into consideration the law of human cognition. To improve the efficiency of the ontology alignment validating process, in this paper, a Multi-view OAV (MOAV) is proposed, which synthetically utilizes the human cognitive theory-information visualization and human-computer inter-action. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test the performance of our proposal, and the experimental results show that MOAV can effectively improve a user’s validating efficiency. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Visualization

White paper on broadband connectivity in 6G

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arXiv 2020年

作者： Rajatheva, Nandana Atzeni, Italo Björnson, Emil Bourdoux, André Buzzi, Stefano Doré, Jean-Baptiste Erkucuk, Serhat Fuentes, Manuel Guan, Ke Hu, Yuzhou Huang, Xiaojing Hulkkonen, Jari Jornet, Josep Miquel Katz, Marcos Nilsson, Rickard Panayirci, Erdal Rabie, Khaled Rajapaksha, Nuwanthika Salehi, MohammadJavad Sarieddeen, Hadi Svensson, Tommy Tervo, Oskari Tölli, Antti Wu, Qingqing Xu, Wen Centre for Wireless Communications University of Oulu Finland Linköping University Sweden IMEC Belgium Department of Electrical and Information Engineering University of Cassino and Southern Latium Italy CEA-Leti France Department of Electrical and Electronics Engineering Kadir Has University Turkey Institute of Telecommunications and Multimedia Applications Universitat Politecnica de Valencia Spain State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications China Algorithm Department ZTE Corporation China School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology Sydney Australia Nokia Bell Labs Finland Department of Electrical and Computer Engineering Institute for the Wireless Internet of Things Northeastern University United States Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Department of Engineering Manchester Metropolitan University United Kingdom Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Saudi Arabia Department of Electrical Engineering Chalmers University of Technology Sweden Department of Electrical and Computer Engineering National University of Singapore Singapore Huawei Technologies Germany

This white paper explores the road to implementing broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, from extreme capacity with peak data rates up to 1 Tbps, to raising the typical data rates by orders-of-magnitude, to support broadband connectivity at railway speeds up to 1000 km/h. To achieve these goals, not only the terrestrial networks will be evolved but they will also be integrated with satellite networks, all facilitating autonomous systems and various interconnected structures. We believe that several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric and scalable cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be used for pushing the peak rates of point-to-point links. The latter path will lead to THz communications complemented by visible light communications in specific scenarios. At the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latencies, higher reliability, and reduced complexity. Different options will be needed to optimally support different use cases. The resource efficiency can be further improved by using various combinations of full-duplex radios, interference management based on rate-splitting, machine-learning-based optimization, coded caching, and broadcasting. Finally, the three levels of enablers must be utilized not only to deliver better broadband services in urban areas, but also to provide full-coverage broadband connectivity must be one of the k

关键词： Visible light communication

Single and Multi-Hop Question-Answering datasets for Reticular Chemistry with GPT-4-Turbo

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arXiv 2024年

作者： Rampal, Nakul Wang, Kaiyu Burigana, Matthew Hou, Lingxiang Al-Johani, Juri Sackmann, Anna Murayshid, Hanan S. Al-Sumari, Walaa Abdullah Al-Abdulkarim, Arwa M. Al-Hazmi, Nahla Eid Al-Awad, Majed O. Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. Department of Chemistry University of California BerkeleyCA94720 United States Kavli Energy Nanoscience Institute University of California BerkeleyCA94720 United States Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Data Services Librarian University of California Berkeley Library United States Department of Mathematics University of California BerkeleyCA94720 United States Department of Statistics University of California BerkeleyCA94720 United States Riyadh12354 Saudi Arabia Hydrogen Technologies Institute King Abdulaziz City for Science and Technology P.O. Box 6086 Riyadh11442 Saudi Arabia KACST−UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia

The rapid advancement in artificial intelligence and natural language processing has led to the development of large-scale datasets aimed at benchmarking the performance of machine learning models. Herein, we introduce 'RetChemQA,' a comprehensive benchmark dataset designed to evaluate the capabilities of such models in the domain of reticular chemistry. This dataset includes both single-hop and multi-hop question-answer pairs, encompassing approximately 45,000 Q&As for each type. The questions have been extracted from an extensive corpus of literature containing about 2,530 research papers from publishers including NAS, ACS, RSC, Elsevier, and Nature Publishing Group, among others. The dataset has been generated using OpenAI's GPT-4 Turbo, a cutting-edge model known for its exceptional language understanding and generation capabilities. In addition to the Q&A dataset, we also release a dataset of synthesis conditions extracted from the corpus of literature used in this study. The aim of RetChemQA is to provide a robust platform for the development and evaluation of advanced machine learning algorithms, particularly for the reticular chemistry community. The dataset is structured to reflect the complexities and nuances of real-world scientific discourse, thereby enabling nuanced performance assessments across a variety of tasks. The dataset is available at the following link: https://***/nakulrampal/RetChemQA. © 2024, CC BY.

关键词： Machine learning

Lecture Notes in Networks and Systems

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Preface

Lecture Notes in Networks and Systems 2023年 725 LNNS卷 xiii-xiv页

作者： Das, Asit Kumar Nayak, Janmenjoy Naik, Bighnaraj Vimal, S. Pelusi, Danilo Department of Computer Science and Technology Indian Institute of Engineering Science and Technology West Bengal Howrah India Department of Computer Applications Veer Surendra Sai University of Technology Odisha Burla India Department of Communication Sciences University of Teramo Teramo Italy Odisha Baripada India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajapalayam India

ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis

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arXiv 2023年

作者： Zheng, Zhiling Zhang, Oufan Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. Department of Chemistry University of California BerkeleyCA94720 United States Kavli Energy Nanoscience Institute University of California BerkeleyCA94720 United States Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Mathematics University of California BerkeleyCA94720 United States Department of Statistics University of California BerkeleyCA94720 United States School of Information University of California BerkeleyCA94720 United States KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information-an issue that previously made the use of Large Language Models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different tradeoffs between labor, speed, and accuracy. We deploy this system to extract 26,257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the dataset built by text mining, we constructed a machine-learning model with over 87% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions on chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format, while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry subdisciplines. Copyright © 2023, The Authors. All rights reserved.

关键词： data mining

Robust frequent directions with application in online learning

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2019年第1期20卷

作者： Luo Luo Cheng Chen Zhihua Zhang Wu-Jun Li Tong Zhang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University Beijing China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University Nanjing China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms.

关键词： frequent directions matrix approximation online Newton algorithm online convex optimization sketching

RETRACTED ARTICLE: An enhanced pairing-based authentication scheme for smart grid communications

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Journal of Ambient Intelligence and Humanized Computing 2021年第1期15卷 165-165页

作者： Wu, Tsu-Yang Lee, Yu-Qi Chen, Chien-Ming Tian, Yuan Al-Nabhan, Najla Abdulrahman College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Computer Engineering Nanjing Institute of Technology Nanjing China Computer Science Department College of Computer and Information Sciences King Saud University Riyadh Kingdom of Saudi Arabia

Deep learning with quantized neural networks for gravitational wave forecasting of eccentric compact binary coalescence

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arXiv 2020年

作者： Wei, Wei Huerta, E.A. Yun, Mengshen Loutrel, Nicholas Shaikh, Md Arif Kumar, Prayush Haas, Roland Kindratenko, Volodymyr National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States NCSA Center for Artificial Intelligence Innovation University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States University of Chicago ChicagoIL60637 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore560089 India Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We present the first application of deep learning forecasting for binary neutron stars, neutron star-black hole systems, and binary black hole mergers that span an eccentricity range e ≤ 0.9. We train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced LIGO noise available at the Gravitational Wave Open science Center to: 1) quantify how fast neural networks identify these signals before the binary components merge;2) quantify how accurately neural networks estimate the time to merger once gravitational waves are identified;and 3) estimate the time-dependent sky localization of these events from early detection to merger. Our findings show that deep learning can identify eccentric signals from a few seconds (for binary black holes) up to tens of seconds (for binary neutron stars) prior to merger. A quantized version of our neural networks achieves 4x reduction in model size, and up to 2.5x inference speed up. These novel algorithms may be used to facilitate time-sensitive multi-messenger astrophysics observations of compact binaries in dense stellar environments. Copyright © 2020, The Authors. All rights reserved.

关键词： Gravity waves

Probing nuclear physics with supernova gravitational waves and machine learning

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arXiv 2023年

作者： Mitra, Ayan Orel, Daniil Abylkairov, Y. Sultan Shukirgaliyev, Bekdaulet Abdikamalov, Ernazar Center for Astrophysical Surveys National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Physics Nazarbayev University 53 Kabanbay Batyr ave Astana010000 Kazakhstan Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States Kazakh-British Technical University 59 Tole Bi Street Almaty050000 Kazakhstan Department of Computer Science Nazarbayev University 53 Kabanbay Batyr ave Astana010000 Kazakhstan Energetic Cosmos Laboratory Nazarbayev University 53 Kabanbay Batyr ave Astana010000 Kazakhstan Heriot-Watt International Faculty Zhubanov University 263 Zhubanov brothers str Aktobe030000 Kazakhstan Fesenkov Astrophysical Institute 23 Observatory str Almaty050020 Kazakhstan Department of Computation and Data Science Astana IT University 55/11 Mangilik El ave Astana010000 Kazakhstan

Core-collapse supernovae are sources of powerful gravitational waves (GWs). We assess the possibility of extracting information about the equation of state (EOS) of high density matter from the GW signal. We use the bounce and early post-bounce signals of rapidly rotating supernovae. A large set of GW signals is generated using general relativistic hydrodynamics simulations for various EOS models. The uncertainty in the electron capture rate is parametrized by generating signals for six different models. To classify EOSs based on the GW data, we train a convolutional neural network (CNN) model. Even with the uncertainty in the electron capture rates, we find that the CNN models can classify the EOSs with an average accuracy of about 87 percent for a set of four distinct EOS models. © 2023, CC BY.

关键词： Gravity waves