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Findings of the Association for Computational Linguistics, ACL 2023

作者： Nguyen, Chau Duc Minh French, Tim Liu, Wei Stewart, Michael ARC Centre for Transforming Maintenance through Data Science Department of Computer Science and Software Engineering School of Physics Mathematics and Computing The University of Western Australia

ISBN: (纸本)9781959429623

Geometric representation of query embeddings (using points, particles, rectangles and cones) can effectively achieve the task of answering complex logical queries expressed in first-order logic (FOL) form over knowledge graphs, allowing intuitive encodings. However, current geometric-based methods depend on the neural approach to model FOL operators (conjunction, disjunction and negation), which are not easily explainable with considerable computation cost. We overcome this challenge by introducing a symbolic modeling approach for the FOL operators, emphasizing the direct calculation of the intersection between geometric shapes, particularly sector-cones in the embedding space, to model the conjunction operator. This approach reduces the computation cost as a non-neural approach is involved in the core logic operators. Moreover, we propose to accelerate the learning in the relation projection operator using the neural approach to emphasize the essential role of this operator in all query structures. Although empirical evidence for explainability is challenging, our approach demonstrates a significant improvement in answering complex logical queries (both non-negative and negative FOL forms) over previous geometric-based models. © 2023 Association for Computational Linguistics.

关键词： Computer circuits

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Benchmarking data Heterogeneity Evaluation Approaches for Personalized Federated Learning

Benchmarking Data Heterogeneity Evaluation Approaches for P...

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International Workshop on Trustworthy Federated Learning, FL 2024

作者： Li, Zhilong Wu, Xiaohu Tang, Xiaoli He, Tiantian Ong, Yew-Soon Chen, Mengmeng Liu, Qiqi Lao, Qicheng Yu, Han Beijing University of Posts and Telecommunications Beijing China College of Computing and Data Science Nanyang Technological University Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore Singapore School of Engineering Westlake University Hangzhou China

ISBN: (纸本)9783031822391

There is growing research interest in measuring the statistical heterogeneity of clients’ local datasets. Such measurements are used to estimate the suitability for collaborative training of personalized federated learning (PFL) models. Currently, these research endeavors are taking place in silos and there is a lack of a unified benchmark to provide a fair and convenient comparison among various approaches in common settings. We aim to bridge this important gap in this paper. The proposed benchmarking framework currently includes six representative approaches. Extensive experiments have been conducted to compare these approaches under five standard non-IID FL settings, providing much needed insights into which approaches are advantageous under which settings. The proposed framework offers useful guidance on the suitability of various data divergence measures in FL systems. It is beneficial for keeping related research activities on the right track in terms of: (1) designing PFL schemes, (2) selecting appropriate data heterogeneity evaluation approaches for specific FL application scenarios, and (3) addressing fairness issues in collaborative model training. The code is available at https://***/Xiaoni-61/DH-Benchmark. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Content-based Art Recommendation Using Multimodal Graph Neural Networks 15

Content-based Art Recommendation Using Multimodal Graph Neur...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Dutta, Haimonti Tiwari, Anushka The State University of New York at Buffalo School of Management and Institute for Artificial Intelligence and Data Science Department of Management Science and Systems BuffaloNY14260 United States The State University of New York at Buffalo Institute for Artificial Intelligence and Data Science BuffaloNY14260 United States

ISBN: (纸本)9798331508821

Graph Neural Network (GNN)-based recommendation systems have become very popular in recent years. Their popularity stems from the fact that nodes can access higher-order neighbor information and there are well-designed algorithms for embedding, message passing, and propagation. In this paper, we present the design of a GNN-based recommendation system on a novel data set collected from field research. Designed for an endangered performing art form, the recommendation system uses multimodal text and image data to suggest similar panels (paintings) to the end-user of the system. We show that multimodal data is particularly helpful in graph representation learning, message passing, and propagation and the panels recommended to users using content-based features are similar to those obtained from systems using user-item bipartite graphs such as MMGCN and GRCN. © 2024 IEEE.

关键词： Graph neural networks

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DeepRisk:A deep learning approach for genome-wide assessment of common disease risk

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Fundamental Research 2024年第4期4卷 752-760页

作者： Jiajie Peng Zhijie Bao Jingyi Lia Ruijiang Han Yuxian Wang Lu Han Jinghao Peng Tao Wang Jianye Hao Zhongyu Wei Xuequn Shang AI for Science Interdisciplinary Research Center Schoolof Computer ScienceNorthwestern Polyechnical UniversityXi'an 129China Key Laboratoryof Bg Data Sorage and Management Northwestern Polytechnical UniversityMinisty of Industy and Information TechnologyX'an 129Chin College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of Data Science Fudan UniversityShanghai 200433China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518000China

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant *** widely applied,traditional polygenic risk scoring methods fall short,as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms(SNPs).This presents a limitation,as genetic diseases often arise from complex interactions between multiple *** address this challenge,we developed DeepRisk,a biological knowledge-driven deep learning method for modeling these complex,nonlinear associations among SNPs,to provide a more effective method for scoring the risk of common diseases with genome-wide genotype *** demonstrated that DeepRisk outperforms existing PRs-based methods in identifying individuals at high risk for four common diseases:Alzheimer's disease,inflammatory bowel disease,type 2diabetes,and breast cancer.

关键词： Disease risk prediction Deep learning Polygenic risk score Common disease risk Disease prevention

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Heterogeneous Edge Service Deployment for Cyber Physical Social Intelligence in Internet of Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-13页

作者： Meng, Ke Liu, Zhongjian Xu, Xiaolong Xia, Xiaoyu Tian, Hao Qi, Lianyong Zhou, Xiaokang School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Software Nanjing University of Information Science and Technology Nanjing China School of Computing Technologies RMIT University Melbourne Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Qingdao China Faculty of Data Science Shiga University Otsu Japan

Cyber Physical Social Intelligence (CPSI) integrates the social intelligence and cyber-physical systems, enabling machines to better interact and respond to human social behaviors. Under CPSI, the Internet of Vehicles (IoV) has given rise to an increasing number of latency-sensitive services. Edge computing, as a distributed computing paradigm, enhances data processing capabilities, reduces data transmission latency, and minimizes bandwidth consumption. However, due to the limited resources of edge servers, striking a balance between service latency and deployment costs remains a highly challenging issue in the process of service deployment. In this paper, we propose a heterogeneous edge service deployment method for CPSI in IoV. Firstly, considering the heterogeneity of IoV services and edge servers, communication model, computational model, and heterogeneous service deployment cost model are constructed. Secondly, to maximize service deployment efficiency and minimize communication latency, a distance and workload-based edge server cluster division method is proposed. Subsequently, heterogeneous service deployment is performed in different clusters based on service category prioritization and minimal deployment quantity prioritization principles. Furthermore, an Analytic hierarchy process-based Heterogeneous edge Service dePloyment algorithm for CPSI in IoV, named AHSP, has been designed to determine optimal service deployment strategies. Finally, extensive numerical experimental results demonstrate the effectiveness of AHSP. IEEE

关键词： Computation theory

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Multiparameter Sensor Based on the Long-Period Fiber Gratings Inscribed in a Pre-Twisted Polarization-Maintaining Fiber

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IEEE Sensors Journal 2025年第11期25卷 19315-19322页

作者： Chen, Siyu Jiang, Chen Ma, Yuehui Fan, Xiaolong Zhao, Yunhe Mou, Chengbo Liu, Yunqi Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai200444 China Nanjing210023 China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai201306 China

We demonstrate the fabrication of pre-twisted long-period fiber gratings (PT-LPFGs) in polarization-maintaining fibers (PMFs) using a CO2 laser. This technique leverages pre-twisted fibers combined with laser exposure, eliminating the need for precise alignment along the PMF axis, a key limitation of conventional single-side CO2 laser methods. The PT-LPFGs with the periods of 420–480 µm were successfully fabricated, achieving a maximum polarization-dependent loss (PDL) exceeding 20 dB at the resonance wavelength. The dual-dips resonance of the LP11 core mode was observed in the 1.0 µm wavelength band. Further chemical etching was applied to adjust the cladding mode resonance wavelength near the dispersion turning point (DTP). This adjustment enabled dual-dips resonance with LP14 and LP15 cladding modes along the PMF’s slow and fast axes, respectively, within the 2.0 µm wavelength band. The sensing performance of PT-LPFGs near the DTP was evaluated. Experimental results demonstrate that these gratings exhibit a high sensitivity of up to 14100 nm/RIU for the surrounding refractive index range of 1.448–1.457. Furthermore, the temperature and strain sensitivities were measured to be at 713 pm/°C and −49.4 pm/ µΕ, respectively. These findings highlight the potential of PT-LPFGs for high-sensitivity fiber sensors, PDL compensators, and other advanced optical applications. © 2001-2012 IEEE.

关键词： Polarization-maintaining fiber

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Multilayer Network Analysis of Brain Signals for Detecting Alzheimer’s Disease 12th

Multilayer Network Analysis of Brain Signals for Detecting...

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12th International Conference on Computational Advances in Bio and Medical sciences, ICCABS 2023

作者： Nguyen, Sean M. Basiri, Mohammad Amin Khanmohammadi, Sina School of Computer Science University of Oklahoma NormanOK73019 United States Data Science and Analytics Institute University of Oklahoma NormanOK73019 United States

ISBN: (纸本)9783031827679

Human neuroimaging datasets provide rich multi-scale spatiotemporal information about the state of the brain. Most current methods, such as spectral analysis, focus on a single facet of these datasets and do not take full advantage of the inherent spatiotemporal information. Here, we consider a multilayer cross-frequency functional connectivity analysis to capture the complex spatiotemporal features of neural datasets at multiple scales and show that such features could potentially provide a better description of the neural activity. We demonstrate the effectiveness of this approach by applying the proposed method to capture disruptions of cross-frequency brain connections in Alzheimer’s patients. More specifically, we compared the multi-scale features extracted from electroencephalogram (EEG) data with traditional features in a machine learning framework to distinguish Alzheimer’s patients from control subjects. Our results show that such multi-scale features improve the prediction accuracy when compared to traditional feature extraction methods in EEG analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multilayer neural networks

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Universality class of machine learning for critical phenomena

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science China(Physics,Mechanics & Astronomy) 2023年第12期66卷 63-70页

作者： Gaoke Hu Yu Sun Teng Liu Yongwen Zhang Maoxin Liu Jingfang Fan Wei Chen Xiaosong Chen School of Systems Science and Institute of Nonequilibrium Systems Beijing Normal UniversityBeijing 100875China Data Science Research Center Faculty of ScienceKunming University of Science and TechnologyKunming 650500China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Herein,percolation phase transitions on a two-dimensional lattice were studied using machine learning *** reveal that different phase transitions belonging to the same universality class can be identified using the same neural networks(NNs),whereas phase transitions of different universality classes require different *** on this finding,we proposed the universality class of machine learning for critical ***,we investigated and discussed the NNs of different universality *** research contributes to machine learning by relating the NNs with the universality class.

关键词： universality class machine learning percolation

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Broadband Optical Coupler Based on the Helical Long-Period Gratings Inscribed in Seven-Core Fibers

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Journal of Lightwave Technology 2025年第12期43卷 5820-5828页

作者： Chen, Siyu Jiang, Chen Ma, Yuehui Zhao, Yunhe Fan, Xiaolong Mou, Chengbo Liu, Yunqi Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai200444 China Nanjing210023 China Shanghai Maritime University Institute of Logistics Science and Engineering Shanghai201306 China

We propose a broadband optical coupler based on cladding mode coupling of identical helical long-period gratings (HLPGs) inscribed in different cores of seven-core fibers (SCFs). The influence of the surrounding refractive index and offset distance between the input grating and tapping grating on the coupling efficiency of the coupler was investigated numerically and experimentally. A total peak coupling efficiency of 75.7% was achieved experimentally under the conditions of a surrounding refractive index of 1.454 and an offset distance of 90 mm when the light is launched into the center core of the SCF. The proposed optical coupler could have promising applications in multi-core fiber amplifiers and space-division multiplexing optical communications. © 1983-2012 IEEE.

关键词： Space division multiple access

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Clustering analysis for Pythagorean fuzzy sets and its application in multiple attribute decision making

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 7897-7907页

作者： Yang, Lei Li, Deqing Zeng, Wenyi Ma, Rong Xu, Zeshui Yu, Xianchuan School of Data Science and Intelligent Engineering Xiamen Institute of Technology Xiamen China Academy of Foundational Education Neusoft Institute Guangdong Foshan China School of Artificial Intelligence Beijing Normal University Beijing China Business School Sichuan University Chengdu China

Pythagorean fuzzy sets, as a generalization of intuitionistic fuzzy sets, have a wide range of applications in many fields including image recognition, data mining, decision making, etc. However, there is little research on clustering algorithms of Pythagorean fuzzy sets. In this paper, a novel clustering idea under Pythagorean fuzzy environment is presented. Firstly, the concept of feature vector of Pythagorean fuzzy number (PFN) is presented by taking into account five parameters of PFN, and some new methods to compute the similarity measure of PFNs by applying the feature vector are proposed. Furthermore, a fuzzy similarity matrix by utilizing similarity measure of PFNs is established. Later, the fuzzy similarity matrix is transformed into a fuzzy equivalent matrix which is utilized to establish a novel Pythagorean fuzzy clustering algorithm. Based on the proposed clustering algorithm, a novel multiple attribute decision making (MADM) method under Pythagorean fuzzy environment is presented. To illustrate the effectiveness and feasibility of the proposed technique, an application example is offered. © 2024 – IOS Press. All rights reserved.

关键词： Image recognition

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