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Correction to: Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

Archives of Computational Methods in engineering 2021年第7期28卷 5039-5039页

作者： Ismail, Leila Materwala, Huned Tayefi, Maryam Ngo, Phuong Karduck, Achim P. Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Norwegian Centre for E-Health Research Tromsø Norway Faculty of Informatics Furtwangen University Furtwangen Germany

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Guiding Cross-lingual Entity Alignment via Adversarial Knowledge Embedding

Guiding Cross-lingual Entity Alignment via Adversarial Knowl...

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IEEE International Conference on Data Mining (ICDM)

作者： Xixun Lin Hong Yang Jia Wu Chuan Zhou Bin Wang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence School of Software FEIT University of Technology Sydney Sydney Australia Department of Computing Macquarie University Sydney Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Xiaomi AI Lab Beijing China

Cross-lingual Entity Alignment (CEA) aims at identifying entities with their counterparts in different language knowledge graphs. Knowledge embedding alignment plays an important role in CEA due to its advantages of easy implementation and run-time robustness. However, existing embedding alignment methods haven't considered the problem of embedding distribution alignment which refers to the alignment of spatial shapes of embedding spaces. To this end, we present a new Adversarial Knowledge Embedding framework (AKE for short) that jointly learns the representation, mapping and adversarial modules in an end-to-end manner. By reducing the discrepancy of embedding distributions, AKE can approximately preserve an isomorphism between source and target embeddings. In addition, we introduce two new orthogonality constraints into mapping to obtain the self-consistency and numerical stability of transformation. Experiments on real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines.

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10 quick tips for making your software outlive your job

arXiv

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arXiv 2025年

作者： Littauer, Richard Wilson, Greg Ainali, Jan AlOmar, Eman Abdullah Arabas, Sylwester Saibene, Yanina Bellini Bubendorfer, Kris Champion, Kaylea Dillon, Clare Helske, Jouni Huybrechts, Pieter Katz, Daniel S. Liao, Chang Lippert, David Liu, Fang Marshall, Pierre McCloy, Daniel R. McInerney, Ian Mkaouer, Mohamed Wiem Ojha, Priyanka Treude, Christoph White, Ethan P. CURIOSS SustainOSS GNOME Foundation Te Herenga Waka Victoria University of Wellington Te Whanganui-a-Tara Aotearoa Wellington New Zealand Third Bit TorontoON Canada Open By Default Italy Department of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States AGH University of Krakow Poland rOpenSci LP Santa Rosa Argentina Te Herenga Waka Victoria University of Wellington Wellington New Zealand Computing and Software Systems University of Washington Bothell BothellWA United States CURIOSS Lero University of Galway Ireland INVEST Research Flagship Centre University of Turku Finland Brussels Belgium University of Illinois Urbana-ChampaignIL United States Pacific Northwest National Laboratory RichlandWA United States Open Source Program Office George Washington University WashingtonDC United States Georgia Institute of Technology GA United States Oxford University United Kingdom Institute for Learning & Brain Sciences University of Washington SeattleWA United States Department of Mechanical Engineering Imperial College London London United Kingdom Department of Computer Science University of Michigan-Flint FlintMI United States Singapore Management University Singapore Department of Wildlife Ecology and Conservation University of Florida GainesvilleFL United States

Loss of key personnel has always been a risk for research software projects. Key members of the team may have to step away due to illness or burnout, to care for a family member, from a loss of financial support, or because their career is going in a new direction. Today, though, political and financial changes are putting large numbers of researchers out of work simultaneously, potentially leaving large amounts of research software abandoned. This article presents ten tips to help researchers ensure that the software they have built will continue to be usable after they have left their present job—whether in the course of voluntary career moves or researcher mobility, but particularly in cases of involuntary departure due to political or institutional changes. © 2025, CC BY-SA.

关键词： Human engineering

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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A Flexible Keyphrase Extraction Technique for Academic Literature

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Procedia Computer Science 2018年 135卷 553-563页

作者： Gollam Rabby Saiful Azad “ Mufti Mahmud Kamal Z. Zamli Mohammed Mostafizur Rahman Faculty of Computer Systems & Software Engineering University Malaysia Pahang Gambang Kuantan Malaysia IBM Centre of Excellence Gambang Kuantan Malaysia Department of Computing and Technology Nottingham Trent University Nottingham United Kingdom Department of Mathematics American International University - Bangladesh (AIUB) Dhaka Bangladesh

A keyphrase extraction technique endeavors to extract quality keyphrases from a given document, which provide a high-level summary of that document. Except statistical keyphrase extraction approaches, all other approaches are either domain-dependent or require a sufficient amount of training data, which are rare at present. Therefore, in this paper, a new tree-based automatic keyphrase extraction technique is proposed, which is domain-independent and employs nominal statistical knowledge; but no train data are required. The proposed technique extracts a quality keyphrase through forming a tree from a candidate keyphrase; and later, it is expanded or shrunk or remained in the same state depending on other similar candidate keyphrases. At the end, keyphrases are extracted from the resultant trees based on a value, µ (which is the Maturity Index (MI) of a node in the tree), which enables flexibility in this process. A small µ value would yield many and/or lengthy keyphrases (greedy approach); whereas, a large µ value would yield lower and/or abbreviated keyphrases (conservative approach). Thereby, a user can extract his/her desired-level of keyphrases through tuning µ value. The effectiveness of the proposed technique is evaluated on an actual corpus, and compared with Rapid Automatic Keyphrase Extraction (RAKE) technique.

关键词： Candidate keyphrase keyphrase automatic keyphrase extraction technique tree data structure

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On Autonomous systems: From Reflexive, Imperative and Adaptive Intelligence to Autonomous and Cognitive Intelligence

On Autonomous Systems: From Reflexive, Imperative and Adapti...

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IEEE International Conference on Cognitive Informatics

作者： Yingxu Wang Konstantinos N. Plataniotis Sam Kwong Henry Leung Svetlana Yanushkevich Fakhri Karray Ming Hou Newton Howard Rodolfo A. Fiorini Paolo Soda Edward Tunstel Jianmin Wang Shushma Patel International Institute of Cognitive Informatics and Cognitive Computing (ICIC) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary Canada University of Toronto Canada City University of Hong Kong Hong Kong University of Waterloo Canada Toronto Research Centre DRDC Canada Computational Neurosciences Lab University of Oxford UK Information & Bioengg. Politecnico di Milano University Italy Univ. Campus Bio-Medico of Rome Italy Autonomous and Intelligent Systems President IEEE SMC School of Software Tsinghua University China School of Engineering London South Bank University UK

ISBN: (数字)9781728114194

ISBN: (纸本)9781728104966

Autonomous systems underpinned by cognitive intelligence represent advanced forms of artificial intelligence studied in intelligence science, systems science, and computational intelligence. Traditional theories and technologies of autonomous systems put emphases on human-system interactions and humans in-the-loop. This paper explores the intelligence and system foundations of autonomous systems. It focuses on what structural and behavioral properties constitute the intelligence power of autonomous systems. It explains how system intelligence aggregates from reflexive, imperative, adaptive intelligence to autonomous and cognitive intelligence. A Hierarchical Intelligence Model (HIM) is introduced to elaborate the evolution of human and system intelligence as an inductive process. A set of properties of system autonomy is formally analyzed towards a wide range of autonomous system applications in computational intelligence and systems engineering.

关键词： Frequency modulation Electronic mail Handheld computers Cognitive systems Robots Animals

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Quantum invariants and the graph isomorphism problem

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Physical Review A 2019年第5期100卷 052317-052317页

作者： P. W. Mills R. P. Rundle J. H. Samson Simon J. Devitt Todd Tilma V. M. Dwyer Mark J. Everitt Quantum Systems Engineering Research Group Department of Physics Loughborough University Leicestershire LE11 3TU United Kingdom The Wolfson School Loughborough University Loughborough Leicestershire LE11 3TU England United Kingdom Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia Turing Inc. Berkeley California 94701 USA Department of Physics College of Science Tokyo Institute of Technology H-63 2-12-1 Oookayama Meguro-ku Tokyo 152-8550 Japan Quantum Computing Unit Institute of Innovative Research Tokyo Institute of Technology S1-16 4259 Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan

Three graph invariants are introduced which may be measured from a quantum graph state and form examples of a framework under which other graph invariants can be constructed. Each invariant is based on distinguishing a different number of qubits. This is done by applying different measurements to the qubits to be distinguished. The performance of these invariants is evaluated and compared to classical invariants. We verify that the invariants can distinguish all nonisomorphic graphs with nine or fewer nodes. The invariants have also been applied to “classically hard” strongly regular graphs, successfully distinguishing all strongly regular graphs of up to 29 nodes, and preliminarily to weighted graphs. We have found that, although it is possible to prepare states with a polynomial number of operations, the average number of preparations required to distinguish nonisomorphic graph states scales exponentially with the number of nodes. We have so far been unable to find operators which reliably compare graphs and reduce the required number of preparations to feasible levels.

关键词： Quantum algorithms Graph theory Phase space methods

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Artificial Intelligence Powered MOOCs: A Brief Survey

Artificial Intelligence Powered MOOCs: A Brief Survey

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IEEE International Conference on Agents (ICA)

作者： Simon Fauvel Han Yu Chunyan Miao Lizhen Cui Hengjie Song Liang Zhang Xiaoming Li Cyril Leung Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Engineering Shandong University Jinan China School of Software Engineering South China University of Technology Guangzhou China Institute of Network Computing and Information Systems Peking University Beijing China Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada

Massive Open Online Courses (MOOCs) have gained tremendous popularity in the last few years. Thanks to MOOCs, millions of learners from all over the world have taken thousands of high-quality courses for free. Artificial intelligence (AI) has played an important role in making MOOCs what they are today. By exploiting the vast amount of data generated by learners engaging in MOOCs, AI techniques have been proposed to improve our understanding of MOOC participants and enable MOOC practitioners to deliver better courses. These approaches have also greatly improved student experience and learning outcomes through constructing intelligent and personalized learning trajectories. In this paper, we first review the state-of-the-art AI research making an impact on MOOCs education, emphasizing on works which aim to enhance our understanding of student learning behaviours, improve student engagement, and improve learning outcomes. We then offer an overview of important future research to carry out in sub-fields of AI to enable MOOCs to reach their full potential.

关键词： Videos Artificial intelligence Tools Navigation Computational modeling Trajectory Education

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A Direct Proof of Improved Biased Random Walk with Gastric Cancer Dataset

A Direct Proof of Improved Biased Random Walk with Gastric C...

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International Conference on Applied Mathematics & Computer Science

作者： Choon Sen Seah Shahreen Kasim Mohd Farhan Md Fudzee Mohd Saberi Mohamad Rd Rohmat Saedudin Deden Witarsyah Rohayanti Hassan Mohd Arfian Ismail Rodziah Atan Soft Computing and Data Mining Centre Faculty of Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan School of Industrial Engineering Telkom University Laboratory of Biodiversity and Bioinformatics Universiti Teknologi Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Department of Software Engineering & Information System Faculty of Computer Science and Information Technology University Putra Malaysia (UPM)

ISBN: (数字)9781728132587

ISBN: (纸本)9781728132594

The relationship between the weight of a single genes and the connection between the genes had been studied. Chemistry and physical sciences have proved the attraction between molecules. Molecules are attracted to each other by bond. This paper is presented to disclose the relationship between weight and connectivity of nodes with biased random walk. An equation of biased random walk which named as significant directed random walk is formed to enhance the connectivity of nodes in directed graph via weigh. To be completely biased to the random walk, references data is implement as directed graph. Weight of genes will be used as one of the parameter in the formula. While the adjacency matrix is further enhanced by Warshall's algorithm to increases the accessibility of nodes via vector. The evolution of random walk is disclosed in this paper as well. Significant directed random walk will be used to prove the importance of weight in this paper. Comparison of the result between biased random walk is presented to prove the enhancement of random walk.

关键词： Biased random walk Cancer classification Gene expression dataset Random walk cancer classification Biased Ganglia one-parameter Mentoring Physical science

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Joint product numerical range and geometry of reduced density matrices

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Science China(Physics,Mechanics & Astronomy) 2017年第2期60卷 9-17页

作者： Jianxin Chen Cheng Guo Zhengfeng Ji Yiu-Tung Poon Nengkun YU Bei Zeng Jie Zhou Joint Center for Quantum Information and Computer Science University of Maryland College Park 20740 USA Institute for Advanced Study Tsinghua University Beijing 100084 China Centre for Quantum Computation ＆ Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 Australia State Key Laboratory of Computer Science.Institute of Software Chinese Academy of Sciences Beijing 100084 China Department of Mathematics Iowa State University Ames 50011-2140 USA Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Canada Department of Mathematics ＆ Statistics University of Guelph Guelph N1G 2 W1 Canada Perimeter Institute for Theoretical Physics Waterloo N2L 2Y5 Canada

The reduced density matrices of a many-body quantum system form a convex set, whose three-dimensional projection is convex in R3. The boundary of may exhibit nontrivial geometry, in particular ruled surfaces. Two physical mechanisms are known for the origins of ruled surfaces： symmetry breaking and gapless. In this work, we study the emergence of ruled surfaces for systems with local Hamiltonians in infinite spatial dimension, where the reduced density matrices are known to be separable as a consequence of the quantum de Finetti＇s theorem. This allows us to identify the reduced density matrix geometry with joint product numerical range II of the Hamiltonian interaction terms. We focus on the case where the interaction terms have certain structures, such that a ruled surface emerges naturally when taking a convex hull of ∏. We show that, a ruled surface on sitting in ∏ has a gapless origin, otherwise it has a symmetry breaking origin. As an example, we demonstrate that a famous ruled surface, known as the oloid, is a possible shape of , with two boundary pieces of symmetry breaking origin separated by two gapless lines.

关键词： reduced density matrices quantum tomography numerical range quantum information oloid ruled surface

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