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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China State Grid Smart Grid Research Institute Co. Ltd. China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9782493814104

Scientific Machine Reading Comprehension (SMRC) aims to facilitate the understanding of scientific texts through human-machine interactions. While existing dataset has significantly contributed to this field, it predominantly focus on single-perspective question-answer pairs, thereby overlooking the inherent variation in comprehension levels among different readers. To address this limitation, we introduce a novel multi-perspective scientific machine reading comprehension dataset, SciMRC, which incorporates perspectives from beginners, students, and experts. Our dataset comprises 741 scientific papers and 6,057 question-answer pairs, with 3,306, 1,800, and 951 pairs corresponding to beginners, students, and experts respectively. Extensive experiments conducted on SciMRC using pre-trained models underscore the importance of considering diverse perspectives in SMRC and highlight the challenging nature of our scientific machine comprehension tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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Himalayan Glacier and the Possibility of Past Ice Streams on Martian Outflow Channels using Remote Sensing imagery

Himalayan Glacier and the Possibility of Past Ice Streams on...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Indranil Misra Mukesh Kumar Rohil S. Manthira Moorthi Debajyoti Dhar Signal and Image Processing Area Space Applications Centre Indian Space Research Organisation (ISRO) Ahmedabad India Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Martian outflow channels are extensive, long features on the planet's surface, running for thousands of kilometers and up to a kilometer deep. Scientific consensus attributes the formation of Martian outflow channels to catastrophic floods from overflowing lakes during Mars' early history. However, we present an alternative perspective, suggesting the possibility of past glacier ice streams on Mars, influenced by recent scientific studies indicating movements in Amazonian ice streams as potential carvers of Martian outflow channels. The paper proposes a hypothesis of a past cryosphere on Mars resulting in ice stream outflow channels. The study compares and analyzes Martian outflow channels and Himalayan glaciers using a novel remote sensing image processing workflow developed to support this proposed idea. The methodology involves topographic correction to normalize radiance measurements in uneven terrain, Principal Component Analysis (PCA) to extract maximum variance information, and feature matching using Scale Invariant Feature Transform (SIFT) with Random Sample Consensus (RANSAC) outlier removal to assign Feature Similarity Metric (FSM) scores for determining analogous sites. The paper highlights intriguing observations from Himalayan glaciers, which lend credence to the idea of an ice flow mechanism for the origin of Martian outflow channels.

关键词： Mars Visualization Transforms Feature extraction Glaciers Surface topography Streams

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Monitoring Riparian Vegetation Change Dynamics using Kullback-Leibler Divergence: A Case Study in the Vicinity of Sabarmati River, Gandhinagar, India

Monitoring Riparian Vegetation Change Dynamics using Kullbac...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Riparian Vegetation monitoring using remote sensing images involves using satellite or aerial imagery to assess the health, extent, and changes in vegetation along water bodies such as rivers and streams. This monitoring is important to balance the aquatic ecosystems. The paper presents a novel methodology to detect Riparian Vegetation change dynamics using multi-temporal satellite imagery. The workflow performs the image pre-processing task that includes multi-temporal image registration, top-of-atmosphere reflectance computation, and NDVI geophysical parameters retrieved to quantify the amount and health of vegetation in a Riparian zone buffer. The changes in Riparian Vegetation are detected using the Kullback-Leibler (KL) divergence technique and a change detection map is generated. The proposed approach is evaluated using recent INS-2B Nano-MX and historical IRS-1C LISS-3 imagery near Sabarmati River, Gandhinagar, India. It is found from the visual assessment that there is an increase in healthy Riparian Vegetation around the Sabarmati River. The results are cross-validated with Landsat NDVI time series analysis in the Riparian Vegetation Region of Interest (ROI) for the last two decades and it is found that vegetation change estimated around the river stream closely follows the trend with the proposed method.

关键词： Visualization Normalized difference vegetation index Time series analysis Vegetation mapping Landsat Rivers Satellite images

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Perceptual Quality Assessment of TTS-Synthesized Speech 9th

Perceptual Quality Assessment of TTS-Synthesized Speech

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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Chen, Zidong Min, Xiongkuo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789819908554

The evaluation of a Text-to-Speech (TTS) system is typically labor-intensive and highly biased because there is no golden standard of the generated speech or objective evaluation metrics. To improve the performance of TTS systems, it is highly desirable to explore the perceptual quality assessment of TTS-synthesized speech and propose a relatively valid evaluation method. In this paper, we introduce a deep-learning-based approach to predict human labeled perceptual quality scores of the generated speech. Our approach is based on ResNet and self-attention, where the former addresses the issue of deep feature extraction and integration and the latter takes advantage of the natural relationship between the input sequences. The experiment results indicate that the proposed method performs better on test tasks in terms of various accuracy evaluation criteria than the state-of-the-art methods. Additionally, the experiment demonstrates a strong correlation between the predicted scores and the true (human) MOS scores. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality control

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Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

Selection of Cancer Biomarkers from Microarray Gene Expressi...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Hanjing Lin, Jianqing Zhu, Huachao Huang, Yabing Huazhong University of Science and Technology Key Lab. of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University School of Computer Science Wuhan430064 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350337488

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy. © 2023 IEEE.

关键词： Bi-objective optimization Cancer biomarker Gene selection NSGA-II

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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A Framework for Realistic Paired Dataset Generation for Deep Learning Based Restoration of Satellite images

A Framework for Realistic Paired Dataset Generation for Deep...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ashutosh Gupta Subhajit Paul Arghya Bhattacharya Prachi Jain Signal and Image Processing Area Space Applications Centre (SAC) ISRO Ahmedabad Dept. of Computer Science Vellore Institute of Tech. Chennai Dept. of Computer Science Indus University Ahmedabad

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Satellite images suffer from inevitable degradations such as blur, noise, and other artifacts making them less suitable for final applications unless they are restored using processing techniques. Modern methods based on deep learning have achieved impressive results in key tasks such as deblurring, denoising, super-resolution, etc. in the domain of terrestrial images but their applicability for satellite image restoration remains limited due to the unavailability of paired datasets for supervised learning. In this work, we propose a framework to address this problem by utilizing a Generative Adversarial Network along with conventional degradation estimation techniques to generate a paired dataset for deep learning. The paired dataset is generated from a set of images with desirable characteristics and hence acts as a surrogate for the actual clean data. We perform experiments to demonstrate that our strategy is able to employ recent deep learning models for standard datasets to achieve impressive restoration results on satellite images for which no paired datasets are available. Visual as well as quantitative results using 30 cm Worldview-3 and Cartosat-3 images show that our strategy is a simple, yet effective way of generating a realistic dataset for utilizing deep-learning solutions for satellite image restoration.

关键词： Degradation Deep learning Visualization Superresolution Noise reduction Supervised learning Estimation

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Improved Power Conversion Efficiency and Stability of Perovskite Solar Cells Induced by Dual Interface Passivation

Improved Power Conversion Efficiency and Stability of Perovs...

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2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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Spatially Adaptive Super-Resolution Via Deep Flow Estimation And Wiener Filtering For Satellite imagery

Spatially Adaptive Super-Resolution Via Deep Flow Estimation...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Jatin Dhall Ashutosh Gupta School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India Signal And Image Processing Area Space Applications Centre ISRO Ahmedabad India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Applications based on satellite imagery depend heavily on the resolution of the images captured by Earth observation satellites. The higher the resolution of the captured images, the better the precision in differentiating fine details in the acquired image, which is a key requirement for critical downstream applications. Though hardware-based methods for improving resolution exist, physical and sensor-based limitations such as optical and motion blur, and noise are difficult to avoid and result in low-resolution images. Super Resolution (SR) is one of the most promising fields in Remote Sensing (RS), which aims to computationally improve the resolution of an image, using one or many input Low Resolution (LR) images. This paper presents a spatially adaptive multiimage super-resolution approach using deep flow estimation and wiener filtering. Our method uses the best of both deep learning and classical approaches. The deep-flow estimation improves the registration performance whereas the spatially adaptive wiener filtering efficiently interpolates the information from low-resolution images to form the super-resolved output. Our method can process an arbitrary number of input LR frames and outputs a super-resolved image. We demonstrate the ability of the method to reconstruct high-frequency details from multiple low-resolution frames using Skysat imagery.

关键词： Optical filters Deep learning Wiener filters Satellites Superresolution Estimation Satellite images Optical sensors Spatial resolution Remote sensing

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