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arXiv 2021年

作者： Liu, Siyuan Wang, Yusong Wang, Tong Deng, Yifan He, Liang Shao, Bin Yin, Jian Zheng, Nanning Liu, Tie-Yan School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Microsoft Research Asia Beijing100080 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China School of Computer Science Fudan University Shanghai200433 China

The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieved better performance than molecular docking, existing models often neglect certain aspects of the intermolecular information, hindering the performance of prediction. We recognize this problem and propose a novel approach named Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively, and shows superior generalization ability to unseen receptor proteins. Furthermore, IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. © 2021, CC BY-NC-SA.

关键词： Proteins

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Accelerating the design and development of polymeric materials via deep learning: Current status and future challenges

APL Machine Learning

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APL Machine Learning 2023年第2期1卷

作者： Dazi Li Yi Ru Zhudan Chen Caibo Dong Yining Dong Jun Liu College of Information Science and Technology Beijing University of Chemical Technology Beijing 100029 China School of Data Science and Hong Kong Institute for Data Science Centre for Systems Informatics Engineering City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials Beijing University of Chemical Technology Beijing 100029 China

The design and development of polymeric materials have been a hot domain for decades. However, traditional experiments and molecular simulations are time-consuming and labor-intensive, which no longer meet the requirements of new materials development. With the rapid advances of artificial intelligence and materials informatics, machine learning algorithms are increasingly applied in materials science, aiming to shorten the development period of new materials. With the evolution of polymeric materials, the structure of polymers has become more and more complex. Traditional machine learning algorithms often do not perform satisfactorily when dealing with complex data. Presently, deep learning algorithms, including deep neural networks, convolutional neural networks, generative adversarial networks, recurrent neural networks, and graph neural networks, show their uniquely excellent learning capabilities for large and complex data, which will be a powerful tool for the design and development of polymeric materials. This Review introduces principles of several currently popular deep learning algorithms and discusses their multiple applications in the materials field. Applications range from property prediction and molecular generation at the molecular level to structure identification and material synthesis in polymers. Finally, future challenges and opportunities for the application of deep learning in polymeric materials are discussed.

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A review on vision-based analysis for automatic dietary assessment

arXiv

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arXiv 2021年

作者： Wang, Wei Min, Weiqing Li, Tianhao Dong, Xiaoxiao Li, Haisheng Jiang, Shuqiang School of Computer and Engineering Beijing Technology and Business University Beijing100048 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China National Engineering Laboratory for Agri-product Quality Traceability Beijing100048 China University of Chinese Academy of Sciences Beijing100049 China

Background: Maintaining a healthy diet is vital to avoid health-related issues, e.g., undernutrition, obesity and many non-communicable diseases. An indispensable part of the health diet is dietary assessment. Traditional manual recording methods are not only burdensome but timeconsuming, and contain substantial biases and errors. Recent advances in Artificial Intelligence (AI), especially computer vision technologies, have made it possible to develop automatic dietary assessment solutions, which are more convenient, less time-consuming and even more accurate to monitor daily food intake. Scope and approach: This review presents Vision-Based Dietary Assessment (VBDA) architectures, including multi-stage architecture and end-to-end one. The multi-stage dietary assessment generally consists of three stages: food image analysis, volume estimation and nutrient derivation. The prosperity of deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition. The recently proposed end-to-end methods are also discussed. We further analyze existing dietary assessment datasets, indicating that one large-scale benchmark is urgently needed, and finally highlight critical challenges and future trends for VBDA. Key findings and conclusions: After thorough exploration, we find that multi-task end-to-end deep learning approaches are one important trend of VBDA. Despite considerable research progress, many challenges remain for VBDA due to the meal complexity. We also provide the latest ideas for future development of VBDA, e.g., fine-grained food analysis and accurate volume estimation. This review aims to encourage researchers to propose more practical solutions for VBDA. Copyright © 2021, The Authors. All rights reserved.

关键词： Nutrition

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Pre-trained Language Embedding-based Contextual Summary and Multi-scale Transmission Network for Aspect Extraction

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Procedia Computer Science 2020年 174卷 40-49页

作者： Cong Feng Yuan Rao Ambreen Nazir Lianwei Wu Long He Research Institute of Xi’an Jiaotong University Shenzhen China Lab of Social Intelligence and Complex Data Processing School of Software Engineering Xi’an Jiao Tong University Xi’an China Shaanxi Joint Key Laboratory for Artifact Intelligence Xi’an Jiao Tong University Xi’an China

With the development of IOT and 5G technology, people’s demand for information acquisition is more inclined to accuracy, intelligence and timeliness. How to help designer obtain the real-time information of specific product reviews from the massive online consumers and upgrade the new design strategy has become a hot topic for research. In this paper, we define the problem as an aspect extraction task, and propose a novel deep learning model that comprises of three modules: pre-training language model embedding, multi-scale transmission network and contextual summary, which aims to provide an end-to-end solution without any additional supervision. To this end, we adopt BERT to overcome the disadvantage of traditional embedding methods, which cannot combine contextual information. Multi-scale transmission network is proposed to integrate the Bi-GRU and a group of CNN networks to extract sequential and local features of words respectively. Contextual summary is a tailor-made representation distilled from the input sentence, conditioned on each current word, and thus can assist aspect prediction. Experimental results over three benchmark SemEval datasets clearly illustrate that our model can achieve the state-of-the-art performance.

关键词： multi-scale transmission network pre-trained language embedding contextual summary

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On Ε-insensitive simplification of fuzzy rules for fetal distress assessment

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Expert Systems with Applications 2021年 179卷 115052-115052页

作者： Jezewski, Michal Czabanski, Robert Leski, Jacek M. Matonia, Adam Kahankova, Radana Silesian University of Technology Department of Cybernetics Nanotechnology and Data Processing 16 Akademicka Str. Gliwice44-100 Poland Lukasiewicz Research Network – Institute of Medical Technology and Equipment 118 Roosevelt Str. Zabrze41-800 Poland VSB – Technical University of Ostrava Faculty of Electrical Engineering and Computer Science 17. listopadu 2172/15 Str. Ostrava708 00 Czech Republic

Objective: The work introduces Ε-insensitive distance based approach to simplification (by reducing the rules number) of the fuzzy classifier rule base. To obtain premises of the initial rules, a modified clustering with pairs of Ε-hyperballs procedure is used. The goal of the presented solutions is to achieve high quality support for fetal distress assessment, based on cardiotocographic (CTG) signals classification with a reduced number of fuzzy rules. Methods: In the presented rule base simplification solution, two rules are considered similar (or contradictory) when the distances between their premises do not exceed the assumed value of Ε. The proposed simplification process consists of two phases: the first with combining similar rules into representative rules, and the second (optional) with the removal of contradictory rules. In addition, two methods of determining conclusions for representative rules are considered: a representative rule retains the original (unchanged) conclusion, or from the conclusions of similar rules the one with the highest absolute value is chosen. In the introduced clustering with pairs of Ε-hyperballs the sizes of object classes are taken into account, to reduce the potential adverse impact of the unbalanced classes in the considered research material. In experiments, two reference assessments for CTG signals based on the retrospective fetal state evaluation were considered. Results and conclusions: In the two-stage classification, the modified clustering outperformed the original procedure in terms of classification sensitivity and the QI index being the geometric mean of sensitivity and specificity. Among the examined rule base simplification methods, we consider the best to be the one based only on combining similar rules, with the unchanged value of conclusion for a representative rule. With the smallest number of rules (after simplification), an increased sensitivity, and in the case of pH-based reference assessment also increas

关键词： Fuzzy rules

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DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

arXiv

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arXiv 2023年

作者： Qian, Bo Chen, Hao Wang, Xiangning Che, Haoxuan Kwon, Gitaek Kim, Jaeyoung Choi, Sungjin Shin, Seoyoung Krause, Felix Unterdechler, Markus Hou, Junlin Feng, Rui Li, Yihao Daho, Mostafa El Habib Wu, Qiang Zhang, Ping Yang, Xiaokang Cai, Yiyu Li, Huating Jia, Weiping Sheng, Bin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ophthalmology Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China VUNO Inc. Seoul Korea Republic of AI/DX Convergence Business Group KT Korea Republic of Johannes Kepler University Linz Linz Austria School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China LaTIM UMR 1101 Inserm Brest France University of Western Brittany Brest France Department of Computer Science and Engineering The Ohio State University Ohio United States Department of Biomedical Informatics The Ohio State University Ohio United States Translational Data Analytics Institute The Ohio State University Ohio United States MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Shanghai China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Endocrinology and Metabolism Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China

Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

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Fractional Modeling and Dynamic Analysis of Two Kinds of Viscoelastic Thin Plates with Gaussian White Noise

SSRN

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SSRN 2023年

作者： Deng, Jinna Liu, Qiumei Yang, Aimin Chen, Yiming College of Science North China University of Science and Technology Tangshan063210 China Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Tangshan063210 China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Tangshan063210 China School of Science Yanshan University Qinhuangdao066004 China LE STUDIUM Loire Valley Institute for Advanced Studies Orléans & Tours Orléans45000 France

According to fractional order model, the governing equation of viscoelastic plate is established. Using the properties of the shifted Bernstein polynomials, the fractional order equation of viscoelastic plate is resolved, the conversion of fractional differential equations to algebraic equations, and operator matrices represented by Kronecker products are solved in the domain. The convergence study validates the algorithm's efficacy and precision in solving differential equations. The displacement of viscoelastic metal thin plate under different external loads (uniform distribution and linear distribution), the effect of white Gaussian noise on the thin metal plate displacement, the effect of different boundary conditions on thin metal plate displacement under harmonic load are studied, the displacements of metal and polymer plates under different loads are contrasted and stress and strain relationship of two kinds of plates are discussed. Based on the outcomes, it can be concluded that the shift Bernstein polynomial approach is useful for numerically simulating viscoelastic plates. © 2023, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Efficient spatialtemporal context modeling for action recognition?

arXiv

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arXiv 2021年

作者： Cao, Congqi Lu, Yue Zhang, Yifan Jiang, Dongmei Zhang, Yanning Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Lab on Speech and Image Information Processing School of Computer Science Northwestern Poly-technical University Xi'an710129 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China

Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextual information between any two points brings huge cost in computation and memory, especially for action recognition, where there is an additional temporal dimension. Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition. The global context is factorized into sparse relation maps. We model the relationship between points in the same line along the direction of horizon, vertical and depth at each time, which forms a 3D criss-cross structure, and duplicate the same operation with recurrent mechanism to transmit the relation between points in a line to a plane finally to the whole spatiotemporal space. Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25% and 30% for video context modeling. We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps. Comparisons with other state-of-the-art methods demonstrate the effectiveness and efficiency of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Big data Integration Design for General Election in Indonesia

Big Data Integration Design for General Election in Indonesi...

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International Conference on Informatics and Computing (ICIC)

作者： Gede Karya Wikan Danar Sunindyo Benhard Sitohang Saiful Akbar Adi Mulyanto School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Parahyangan Catholic University Bandung Indonesia PUI-PT Artificial Intelligence for Vision Natural Language Processing & Big Data Analytics (AI-VLB) Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9781728180472

ISBN: (纸本)9781728180489

The use of big-data analysis in elections in Indonesia has been started since the Governor Election of DKI-Jakarta in 2012 until the Presidential Election in 2019. However, its use is limited to using analytical sentiment to map support and predict election results using social-media data. We see that there is a great opportunity to use big data for a broader election, which is to facilitate the fulfillment of the information and analysis needs of all election stakeholders. But the main problem in using big-data is the integration of big data from various sources with a variety of different formats and large volumes, in addition to the issues of analysis and visualization. For this reason, in this paper, we propose a big-data integration design to meet the needs of elections in Indonesia. This big-data integration design was developed based on election regulations in Indonesia, knowledge of big-data, and the use of a NoSQL database to store unstructured data. The election big-data integration design that we propose includes (1) the information needs of each election stakeholder; (2) the potential for big-data in fulfilling the information needs of every election stakeholder; (3) big-data analysis architecture for elections; (4) big-data integration architecture for elections; (5) crawler architecture; and (5) technology architecture that can implement big-data integration design for elections. Currently, the implementation of this design is in progress in the P3MI-ITB research project.

关键词： Voting NoSQL databases Computer architecture Big data Regulation Stakeholders Informatics

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Basic Knowledge Construction Technique to Reduce The Volume of Low-Dimensional Big data

Basic Knowledge Construction Technique to Reduce The Volume ...

引用

International Conference on Informatics and Computing (ICIC)

作者： Gede Karya Benhard Sitohang Saiful Akbar Veronica S. Moertini Parahyangan Catholic University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia PUI-PT Artificial Intelligence for Vision Natural Language Processing & Big Data Analytics (AI-VLB) Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9781728180472

ISBN: (纸本)9781728180489

Big-data has the characteristics of high volume, velocity, and variety (3v) and continues to grow exponentially following the development of the use of world information and communication technology. The main problem in the use of big data is data deluge. The need for technology and big-data storage and processing methods to offset the exponential data growth rate is potentially unlimited, giving rise to the problem of increasing exponential technology requirements as well. In this paper, we propose a new approach in the realm of big-data analysis, through separating the basic-knowledge construction process from the original data into knowledge with much smaller velocity and volume. There are three problems to be solved, such as formulating basic-knowledge, developing a method for constructing basic-knowledge from initial data, and developing a technique for analyzing basic-knowledge into final knowledge. In this study, the technique used to build basic-knowledge is clustering-based. Analysis of basic-knowledge into final-knowledge is limited to the clustering-based analysis process. The main contributions in this paper are basic-knowledge formulation, new big-data analytic architecture, basic-knowledge construction algorithms (DSC4BKC), and analysis algorithms from basic-knowledge (BDAfBK) to final-knowledge. To test our proposed method, we use the BIRCH clustering algorithm with O(n) complexity as the baseline. We also used the artificial test-data generated from WEKA, and the IRIS4D and Diabetes data from the UCI Machine Learning data Set for validation. Our test shows that the proposed method much more efficient in using data storage (84.69% up to 99.80%), faster in processing (20.84% up to 86.91%, and produces final-knowledge that is similar to the baseline.

关键词： Machine learning algorithms Clustering algorithms Memory Machine learning Big data Information and communication technology Informatics

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