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arXiv 2022年

作者： Tahir, Barzan Hussein Rashid, Tarik A. Rauf, Hafiz Tayyab Bacanin, Nebojsa Chhabra, Amit Vimal, S. Yaseen, Zaher Mundher Department of Computer Science and Engineering University of Kurdistan Helwer Erbil Iraq Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia USQ's Advanced Data Analytics Research Group School of Mathematics Physics and Computing University of Southern Queensland QLD4350 Australia New era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq

Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced Fitness Dependent Optimizer. Correspondingly, various standard estimations are used to prove the stability

关键词： Decision making

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Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval

arXiv

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arXiv 2022年

作者： Dong, Jianfeng Wang, Yabing Chen, Xianke Qu, Xiaoye Li, Xirong He, Yuan Wang, Xun The College of Computer and Information Engineering Zhejiang Gongshang University Hangzhou310035 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Electronic Information and Communications Huazhong University of Science and Technology Hubei430074 China The Key Lab of Data Engineering and Knowledge Engineering Renmin University of China The AIMC Lab. School of Information Renmin University of China Beijing100872 China The Alibaba Group Beijing100102 China

This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos. The success of this task depends on cross-modal representation learning that projects both videos and sentences into common spaces for semantic similarity computation. In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval. Inspired by the reading strategy of humans, we propose a Reading-strategy Inspired Visual Representation Learning (RIVRL) to represent videos, which consists of two branches: a previewing branch and an intensive-reading branch. The previewing branch is designed to briefly capture the overview information of videos, while the intensive-reading branch is designed to obtain more in-depth information. Moreover, the intensive-reading branch is aware of the video overview captured by the previewing branch. Such holistic information is found to be useful for the intensive-reading branch to extract more fine-grained features. Extensive experiments on three datasets are conducted, where our model RIVRL achieves a new state-of-the-art on TGIF and VATEX. Moreover, on MSR-VTT, our model using two video features shows comparable performance to the state-of-the-art using seven video features and even outperforms models pre-trained on the large-scale HowTo100M dataset. Code is available at https://***/LiJiaBei-7/rivrl. © 2022, CC BY.

关键词： Video recording

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Understanding the predictability of path flow distribution in urban road networks using an information entropy approach

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Multimodal Transportation 2024年第2期3卷

作者： Guo, Bao Huang, Zhiren Zheng, Zhihao Zhang, Fan Wang, Pu School of Traffic and Transportation Engineering Rail Data Research and Application Key Laboratory of Hunan Province Central South University Changsha China Shandong Provincial Communications Planning and Design Institute Group Co. Ltd Jinan China Department of Computer Science Aalto University Helsinki Finland Department of Civil Engineering McGill University Montreal H3A 0C3 QC Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved. © 2024

关键词： Human mobility Information entropy Path flow distribution Predictability

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Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

arXiv

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arXiv 2022年

作者： Chandra, Rohitash Bansal, Chaarvi Kang, Mingyue Blau, Tom Agarwal, Vinti Singh, Pranjal Wilson, Laurence O.W. Vasan, Seshadri Transitional Artificial Intelligence Research Group School of Mathematics and Statistics Unsw Sydney Sydney Australia Australian E-Health Research Centre Commonwealth Scientific and Industrial Research Organisation North Ryde Australia Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Rajasthan India Data 61 Csiro Sydney Australia Department of Computer Science and Engineering Indian Institute of Technology Guwathi Assam India Department of Health Sciences University of York York United Kingdom

Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron, emerged with altered viral properties leading to severe transmission and death rates. These variants burdened the medical systems worldwide with a major impact to travel, productivity, and the world economy. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and further visualises and compares the results using selected dimensionality reduction methods that include principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for selected variants (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We find that the proposed framework can effectively distinguish between the major variants and has the potential to identify emerging variants in the future. © 2022, CC BY.

关键词： COVID-19

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Learning analogy-preserving sentence embeddings for answer selection 23

Learning analogy-preserving sentence embeddings for answer s...

引用

23rd Conference on Computational Natural Language Learning, CoNLL 2019

作者： Diallo, Aïssatou Zopf, Markus Fürnkranz, Johannes Research Training Group AIPHES Department of Computer Science Technische Universität Darmstadt Germany Knowledge Engineering Group Department of Computer Science Technische Universität Darmstadt Germany

ISBN: (纸本)9781950737727

Answer selection aims at identifying the correct answer for a given question from a set of potentially correct answers. Contrary to previous works, which typically focus on the semantic similarity between a question and its answer, our hypothesis is that question-answer pairs are often in analogical relation to each other. Using analogical inference as our use case, we propose a framework and a neural network architecture for learning dedicated sentence embeddings that preserve analogical properties in the semantic space. We evaluate the proposed method on benchmark datasets for answer selection and demonstrate that our sentence embeddings indeed capture analogical properties better than conventional embeddings, and that analogy-based question answering outperforms a comparable similarity-based technique. © 2019 Association for Computational Linguistics.

关键词： Embeddings

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StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder

arXiv

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arXiv 2022年

作者： Chatterjee, Soumick Sciarra, Alessandro Dünnwald, Max Tummala, Pavan Agrawal, Shubham Kumar Jauhari, Aishwarya Kalra, Aman Oeltze-Jafra, Steffen Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany MedDigit Department of Neurology Medical Faculty University Hospital Magdeburg Germany German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI - even more than one simultaneously, which renders a representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out-of-distribution samples. Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642±0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859±0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522 ±0.135 and 0.783 ±0.111, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Tumors

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Social group Query Based on Multi-fuzzy-constrained Strong Simulation

arXiv

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arXiv 2021年

作者： Liu, Guliu Li, Lei Liu, Guanfeng Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology China Department of Computing Macquarie University Australia Mininglamp Academy of Sciences Mininglamp Technology China

Traditional social group analysis mostly uses interaction models, event models, or other methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interaction between members of the group. Generally, graph pattern matching (GPM) is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this paper, a social group query problem that needs to consider the trust between members of the group is proposed. To solve this problem, we propose a Strong Simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence. Aiming at the inefficiency of the NTSS algorithm when matching pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matched edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Constrained optimization

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SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition

arXiv

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arXiv 2021年

作者： Fan, Zhaoxin Song, Zhenbo Liu, Hongyan Lu, Zhiwu He, Jun Du, Xiaoyong Key Laboratory of Data Engineering Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Management Science and Engineering Tsinghua University Beijing100084 China

Simultaneous Localization and Mapping (SLAM) and Autonomous Driving are becoming increasingly more important in recent years. Point cloud-based large scale place recognition is the spine of them. While many models have been proposed and have achieved acceptable performance by learning short-range local features, they always skip long-range contextual properties. Moreover, the model size also becomes a serious shackle for their wide applications. To overcome these challenges, we propose a super light-weight network model termed SVT-Net. On top of the highly efficient 3D Sparse Convolution (SP-Conv), an Atom-based Sparse Voxel Transformer (ASVT) and a Cluster-based Sparse Voxel Transformer (CSVT) are proposed respectively to learn both short-range local features and long-range contextual features. Consisting of ASVT and CSVT, SVT-Net can achieve state-of-the-art performance in terms of both recognition accuracy and running speed with a super-light model size (0.9M parameters). Meanwhile, for the purpose of further boosting efficiency, we introduce two simplified versions, which also achieve state-of-the-art performance and further reduce the model size to 0.8M and 0.4M respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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A Real-time Hand Motion Detection System for Unsupervised Home Training

A Real-time Hand Motion Detection System for Unsupervised Ho...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Jiahua Xu Priyanka Mohan Faxing Chen Andreas Nürnberger Data & Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Ping An Smart City Technology Co.

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Hand motion tracking plays a vital role in health-care like Physical therapy (PT) rehabilitation that helps the patients restore their physical movement of the hand. These treatments are taken by patients suffering from stroke, accidents, and any other kind of neurological disorder. We have developed a low-cost system to track hand movements and detect the gestures of hand for unsupervised home training. The system integrated a convolutional neural network-based hand motion system and a gesture detection system to serve a training session sequential for hand movement rehabilitation. We combined part of open datasets and our novel dataset(total: 16605, 4 labels) for the final training, six directions, and four gestures were predicted in real-time based on our proposed model. An adaptive GUI application was developed to respond to individual performance patterns. This system can be deployed easily on a laptop/PC with a web camera, which makes it easier and low-costs for a doctor to track the hand movement of the patients and also support to quantify the improvements index after several training sessions. These tracked data can also be stored and sent to the remote clinic center and used for further studies. This kind of system would be beneficial to the patients and the training center intelligently. It could be deployed at home or the clinic with more optimization of functions such as safety measures.

关键词： Training Tracking Face recognition Training data Medical treatment Stroke (medical condition) Real-time systems

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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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