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arXiv 2024年

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China MOE KLINNS Lab School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Accelerating exploitation and integration of global renewable energy

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Innovation 2025年

作者： Wang, Jianxiao Chen, Xinjiang Zhuang, Minghao Li, Yan Ruan, Ziwen Wang, Yuhan Zhang, Ning Song, Jie He, Kebin Lu, Xi National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China Peking University Ordos Research Institute of Energy Ordos017000 China State Key Laboratory of Nutrient Use and Management College of Resources and Environmental Sciences China Agricultural University Beijing100193 China College of Environmental Science and Engineering Tongji University Shanghai200092 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing100084 China State Key Lab of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China

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Automatic Diagnosis of Glaucoma on Color Fundus Images Using Adaptive Mask Deep Network 1

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Yang, Gang Li, Fan Ding, Dayong Wu, Jun Xu, Jie Key Lab of Data Engineering and Knowledge Engineering Beijing China School of Information Renmin University of China Beijing China Vistel Inc. Beijing China School of Electronics and Information Northwestern Polytechnical University Xi’an China Beijing Tongren Hospital Beijing China

ISBN: (数字)9783030678357

ISBN: (纸本)9783030678340

Glaucoma, a disease characterized by the progressive and irreversible defect of the visual field, requires a lifelong course of treatment once it is confirmed, which highlights the importance of glaucoma early detection. Due to the diversity of glaucoma diagnostic indicators and the diagnostic uncertainty of ophthalmologists, deep learning has been applied to glaucoma diagnosis by automatically extracting characteristics from color fundus images, and that has achieved great performance recently. In this paper, we propose a novel adaptive mask deep network to obtain effective glaucoma diagnosis on retinal fundus images, which fully utilizes the prior knowledge of ophthalmologists on glaucoma diagnosis to synthesize attention masks of color fundus images to locate a reasonable region of interest. Based on the synthesized masks, our method could pay careful attention to the effective visual representation of glaucoma. Experiments on several public and private fundus datasets illustrate that our method could focus on the significant area of glaucoma diagnosis and simultaneously achieve great performance in both academic environments and practical medical applications, which provides a useful contribution to improve the automatic diagnosis of glaucoma. © 2021, Springer Nature Switzerland AG.

关键词： Diagnosis

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Unified Abstract Syntax Tree Representation Learning for Cross-Language Program Classification

arXiv

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arXiv 2022年

作者： Wang, Kesu Yan, Meng Zhang, He Hu, Haibo State Key Lab for Novel Software Technology Nanjing University Nanjing China School of Big Data & Software Engineering Chongqing University Chongqing China

Program classification can be regarded as a high-level abstraction of code, laying a foundation for various tasks related to source code comprehension, and has a very wide range of applications in the field of software engineering, such as code clone detection, code smell classification, defects classification, etc. The cross-language program classification can realize code transfer in different programming languages, and can also promote cross-language code reuse, thereby helping developers to write code quickly and reduce the development time of code transfer. Most of the existing studies focus on the semantic learning of the code, whilst few studies are devoted to cross-language tasks. The main challenge of cross-language program classification is how to extract semantic features of different programming languages. In order to cope with this difficulty, we propose a Unified Abstract Syntax Tree (namely UAST in this paper) neural network. In detail, the core idea of UAST consists of two unified mechanisms. First, UAST learns an AST representation by unifying the AST traversal sequence and graph-like AST structure for capturing semantic code features. Second, we construct a mechanism called unified vocabulary, which can reduce the feature gap between different programming languages, so it can achieve the role of cross-language program classification. Besides, we collect a dataset containing 20,000 files of five programming languages, which can be used as a benchmark dataset for the cross-language program classification task. We have done experiments on two datasets, and the results show that our proposed approach outperforms the state-of-the-art baselines in terms of four evaluation metrics (Precision, Recall, F1-score, and Accuracy). Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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InfoNorm: Mutual Information Shaping of Normals for Sparse-View Reconstruction

arXiv

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arXiv 2024年

作者： Wang, Xulong Dong, Siyan Zheng, Youyi Yang, Yanchao State Key Lab of CAD&CG Zhejiang University China Chohotech Co. Ltd. China Institute of Data Science The University of Hong Kong Hong Kong Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

3D surface reconstruction from multi-view images is essential for scene understanding and interaction. However, complex indoor scenes pose challenges such as ambiguity due to limited observations. Recent implicit surface representations, such as Neural Radiance Fields (NeRFs) and signed distance functions (SDFs), employ various geometric priors to resolve the lack of observed information. Nevertheless, their performance heavily depends on the quality of the pre-trained geometry estimation models. To ease such dependence, we propose regularizing the geometric modeling by explicitly encouraging the mutual information among surface normals of highly correlated scene points. In this way, the geometry learning process is modulated by the second-order correlations from noisy (first-order) geometric priors, thus eliminating the bias due to poor generalization. Additionally, we introduce a simple yet effective scheme that utilizes semantic and geometric features to identify correlated points, enhancing their mutual information accordingly. The proposed technique can serve as a plugin for SDF-based neural surface representations. Our experiments demonstrate the effectiveness of the proposed in improving the surface reconstruction quality of major states of the arts. Our code is available at: https://***/Muliphein/InfoNorm. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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Magnetic fluid enabled hexagonal fiber grating for vector magnetic field sensing

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Photonics Research 2025年第6期13卷 1726-1735页

作者： Siyu Chen Chen Jiang Yuehui Ma Yunhe Zhao Lilong Dai Qianqian Huang Wei Chen Chengbo Mou Yunqi Liu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai 200444 China Advanced Photonic Technology Lab College of Electronics and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing 210023 China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai 201306 China e-mail: mouc1@***

Optical fiber magnetic field sensors play a crucial role in aerospace and medical fields due to their high sensitivity, fast response time, and resistance to electromagnetic interference. Most current research primarily focuses on detecting magnetic field intensity; however, the magnetic field is a vector field with both intensity and direction, making vector magnetic field measurement significantly important in various fields. Here, we experimentally demonstrated a vector magnetic field sensor using a magnetic fluid (MF) enabled hexagonal fiber grating. Such a specialty optical fiber device features strong asymmetric evanescent field distribution along the index perturbed area, from which the overcoated MF can sense the external magnetic field. When the fiber magnetometer operated at the dispersion turning point, a maximum sensitivity of 10.48 nm/mT was achieved within a range of 0–20.7 mT, which is one order of magnitude greater than that of conventional fiber grating sensors. Utilizing the polygon optical fiber, our demonstrated device simultaneously achieves a maximum orientation sensitivity of 1.17 nm/deg within a range of 0°–30°. This hexagonal fiber grating as an excellent vector magnetic field sensor may be used in military, aerospace, medical sectors, etc.

关键词： Effective refractive index Fiber Bragg gratings Fiber optic sensors Long period fiber gratings Medical fields Optical fibers

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Research on Power Grid Disturbance Signal Identification Method Based on Energy Difference

Research on Power Grid Disturbance Signal Identification Met...

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2022 International Conference on Power Energy Systems and Applications, ICoPESA 2022

作者： Ji, Ping Zhu, Quan Key Lab. of Unmanned Aer. Vehicle Development Data Application of Anhui Higher Education Institutes Ma'anshan China Maanshan Wireless Sensor Network and Intelligent Perception Engineering Technology Research Center Wanjiang China University of Technology Maanshan China Maanshan University Ma'anshan China

ISBN: (纸本)9781665410977

With the rapid development of clean energy generation technology, a large number of distributed power sources are integrated into the local power grid, resulting in the intensification of disturbance signals in the power grid. It is particularly important to effectively classify and identify the disturbance signals in the power grid. This paper presents a recognition method based on energy difference and support vector machine. The method extracts signal feature vectors by calculating the energy difference of power grid signals and sends them to support vector machine for classification. Experiments were carried out on the power grid signals with and without noise. Experiments show that this method can effectively classify and recognize the disturbance signals, and the recognition rate is greatly improved. The effectiveness of the method is proved. This paper provides a theoretical basis for the improvement of power grid quality and intelligent monitoring management in the future. © 2022 IEEE.

关键词： Support vector machines

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SPA: a graph spectral alignment perspective for domain adaptation 23

SPA: a graph spectral alignment perspective for domain adapt...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhiqing Xiao Haobo Wang Ying Jin Lei Feng Gang Chen Fei Huang Junbo Zhao College of Computer Science and Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University CUHK-SenseTime Joint Lab The Chinese University of Hong Kong School of Computer Science and Engineering Nanyang Technological University Alibaba Group

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a finegrained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA.

关键词：

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Confidence-oriented Contrastive Graph Clustering

Confidence-oriented Contrastive Graph Clustering

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International Joint Conference on Neural Networks (IJCNN)

作者： Yan-Di Huang Guang-Yu Zhang Dong Huang Chang-Dong Wang Yang Liu Enbo Huang College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Contrastive clustering has recently been an emerging topic in deep unsupervised learning. Nevertheless, the previous works mostly adopt the stochastic data augmentations, which easily leads to the semantic drift problem by limited transformations. Moreover, these approaches ignore the data distribution information when generating the positive and negative pair-wise samples. In light of this, this paper proposes a simple yet effective unsupervised clustering network termed Confidence-oriented Contrastive Graph Clustering (CoCGC). Particularly, we design an end-to-end network paradigm with un-shared weights, among which a hybrid graph filter is utilized to generate two views of reliable augmentations. Guided by the non-dominated sorting theory, we further construct a confidence-oriented sample set from the latent data distribution perspective. By considering the local density and cluster distribution of the embedding representations, the discriminative sample pairs can be derived from the confidence-oriented sets in a two-view contrastive manner. Finally, a cross-view neighbor contrastive loss is devised for better exploiting the self-supervised network signals. Extensive experimental results on five benchmark datasets demonstrate the effectiveness of our method against the existing state-of-the-art deep graph clustering methods.

关键词： Network topology Semantics Neural networks Reliability theory Reliability engineering Information filters Filtering theory

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UniCATS: A Unified Context-Aware Text-to-Speech Framework with Contextual VQ-Diffusion and Vocoding

arXiv

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arXiv 2023年

作者： Du, Chenpeng Guo, Yiwei Shen, Feiyu Liu, Zhijun Liang, Zheng Chen, Xie Wang, Shuai Zhang, Hui Yu, Kai MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China AISpeech Ltd Beijing China

The utilization of discrete speech tokens, divided into semantic tokens and acoustic tokens, has been proven superior to traditional acoustic feature mel-spectrograms in terms of naturalness and robustness for text-to-speech (TTS) synthesis. Recent popular models, such as VALL-E and SPEAR-TTS, allow zero-shot speaker adaptation through auto-regressive (AR) continuation of acoustic tokens extracted from a short speech prompt. However, these AR models are restricted to generate speech only in a left-to-right direction, making them unsuitable for speech editing where both preceding and following contexts are provided. Furthermore, these models rely on acoustic tokens, which have audio quality limitations imposed by the performance of audio codec models. In this study, we propose a unified context-aware TTS framework called UniCATS, which is capable of both speech continuation and editing. UniCATS comprises two components, an acoustic model CTX-txt2vec and a vocoder CTX-vec2wav. CTX-txt2vec employs contextual VQ-diffusion to predict semantic tokens from the input text, enabling it to incorporate the semantic context and maintain seamless concatenation with the surrounding context. Following that, CTX-vec2wav utilizes contextual vocoding to convert these semantic tokens into waveforms, taking into consideration the acoustic context. Our experimental results demonstrate that CTXvec2wav outperforms HifiGAN and AudioLM in terms of speech resynthesis from semantic tokens. Moreover, we show that UniCATS achieves state-of-the-art performance in both speech continuation and editing. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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