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Visible-Thermal Cross-Modality Class-Incremental Learning

Expert Systems with Applications 2025年

作者： Xinjie Yao Yingxue Wang Yu Wang Pengfei Zhu Ruipu Zhao Weihao Li Shenglei Pei Wanyu Lin College of Intelligence and Computing Tianjin University Tianjin 300350 China Engineering Research Center of City Intelligence and Digital Governance Ministry of Education of the People’s Republic of China Tianjin 300350 China Haihe Lab of ITAI Tianjin 300350 China National Engineering Research Center Public Safety Risk Perception and Control by Big Data Beijing 100041 China School of New Media and Communication Tianjin University Tianjin 300350 China School of Intelligence Science and Engineering Qinghai Minzu University Xining 810007 China Department of Computing The Hong Kong Polytechnic University Hong Kong 999077 China

Visible-thermal cross-modality learning has gained significant success by utilizing information from different sources to improve performance in downstream tasks. To make cross-modality learning methods more practical in open-ended environments, such as autonomous driving, a straightforward question arises on how we can incrementally update the model with such cross-modality data. In response, we propose a practical task, namely cross-modality class-incremental learning (CIL). We first provide a cross-modality CIL benchmark, which encompasses datasets abundant in scene diversity and environmental variability, including urban, residential, and diverse temporal settings. Then we thoroughly analyze its challenges and find that: (1) simply using cross-modality learning or CIL methods cannot achieve satisfactory performance in cross-modality CIL; (2) combining existing cross-modality learning methods and CIL strategies benefits cross-modality CIL to a very limited extent; (3) modality forgetting, i.e., neurons favoring a dominant modality and diminishing the weaker, is the significant factor that leads to forgetting in cross-modality CIL. To address the challenges of cross-modality CIL, we design a simple yet effective method, termed Hybrid-fused Forgetting Prevention (HFP). Specifically, HFP first introduces a block-diagonal structured sparsity on the channel dimension and implements the information interchange mechanism by shuffling the feature maps of different groups and changing the sequence of channels, thus fully exploiting the complementarity of cross-modality data. Then, with representation fusion and forgetting prevention strategies, more complementary features can be learned and exploited in retaining old knowledge, which can alleviate modality forgetting. Extensive experiments validate that HFP contributes a favorable framework to cross-modality CIL and alleviates forgetting effectively.

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Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

arXiv

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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Correction to: An empirical study on the joint impact of feature selection and data resampling on imbalance classification

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Applied Intelligence 2022年第7期53卷 8506-8506页

作者： Zhang, Chongsheng Soda, Paolo Bi, Jingjun Fan, Gaojuan Almpanidis, George García, Salvador Ding, Weiping Henan Key Lab of Big Data Analysis and Processing Henan University Henan China Department of Engineering University Campus Bio-Medico of Rome Rome Italy Department of Radiation Sciences Radiation Physics Biomedical Engineering Umea University Umea Sweden DaSCI Andalusian Research Institute University of Granada Granada Spain School of Information Science and Technology Nantong University Nantong China

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A SURVEY OF AUTOMATIC GENERATION OF SOURCE CODE COMMENTS: ALGORITHMS AND TECHNIQUES

arXiv

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arXiv 2019年

作者： Song, Xiaotao Sun, Hailong Wang, Xu Yan, Jiafei School of Software Taiyuan University of Technology China SKLSDE Lab School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

As an integral part of source code files, code comments help improve program readability and comprehension. However, developers sometimes do not comment on their program code adequately due to the incurred extra efforts, lack of relevant knowledge, unawareness of the importance of code commenting or some other factors. As a result, code comments can be inadequate, absent or even mismatched with source code, which affects the understanding, reusing and the maintenance of software. To solve these problems of code comments, researchers have been concerned with generating code comments automatically. In this work, we aim at conducting a survey of automatic code commenting researches. First, we generally analyze the challenges and research framework of automatic generation of program comments. Second, we present the classification of representative algorithms, the design principles, strengths and weaknesses of each category of algorithms. Meanwhile, we also provide an overview of the quality assessment of the generated comments. Finally, we summarize some future directions for advancing the techniques of automatic generation of code comments and the quality assessment of comments. Copyright © 2019, The Authors. All rights reserved.

关键词： Surveys

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SenseMag: Enabling low-cost traffic monitoring using non-invasive magnetic sensing

arXiv

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arXiv 2021年

作者： Wang, Kafeng Xiong, Haoyi Zhang, Jie Chen, Hongyang Dou, Dejing Xu, Cheng-Zhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Shenzhen China Big Data Lab Baidu Inc. Haidian Beijing China Key Laboratory of High Confidence Software Technologies Peking University Haidian Beijing China Research Center for Intelligent Network Zhejiang Lab Zhejiang Hangzhou China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Tapia Macau China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, including vehicular types (e.g., cars, trucks, and buses), in the critical roads and highways. While traditional approaches based on vehicular-embedded GPS sensors or camera networks would either invade drivers’ privacy or require high deployment cost, this paper introduces a low-cost method, namely SenseMag, to recognize the vehicular type using a pair of non-invasive magnetic sensors deployed on the straight road section. SenseMag filters out noises and segments received magnetic signals by the exact time points that the vehicle arrives or departs from every sensor node. Further, SenseMag adopts a hierarchical recognition model to first estimate the speed/velocity, then identify the length of vehicle using the predicted speed, sampling cycles, and the distance between the sensor nodes. With the vehicle length identified and the temporal/spectral features extracted from the magnetic signals, SenseMag classify the types of vehicles accordingly. Some semi-automated learning techniques have been adopted for the design of filters, features, and the choice of hyper-parameters. Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i.e., 7 types by SenseMag versus 4 types by the existing work in comparisons). To be specific, our field experiment results validate that SenseMag is with at least 90% vehicle type classification accuracy and less than 5% vehicle length classification error. Copyright © 2021, The Authors. All rights reserved.

关键词： Sensor nodes

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AN EMPIRICAL FEASIBILITY STUDY OF SOCIETAL RISK CLASSIFICATION TOWARD BBS POSTS

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Journal of Systems science and Systems Engineering 2018年第6期27卷 709-726页

作者： Jindong Chen Xiaoji Zhou Xijin Tang School of Economics and Management Beifing Information Science &Technology University Beifing 100192 China China Academy of Aerospace Systems Science and Engineering Beijing 100048 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Beijing Key Lab of Green Development Decision Based on Big Data Beijing 100192 China University of Chinese Academy of Sciences Beijing 100049 China

Societal risk classification is the fundamental issue for online societal risk monitoring. To show the challenge and feasibility of societal risk classification toward BBS posts, an empirical analysis is implemented in this paper. Through effectiveness analysis, Support Vector Machine based on Bag-Of-Words （BOW-SVM） is adopted for challenge validation, and the distributed document embeddings of BBS posts generated by Paragraph Vector are applied to feasibility study. Based on BOW-SVM, cross-validations of BBS posts labeled by different groups and annotators are conducted. The big fluctuation of cross-validation results indicates the differences of individual risk perceptions, which brings more challenges to societal risk classification. Furthermore, based on the distributed document embeddings of BBS posts, the pairwise similarities of more than 300 thousands BBS posts from different societal risk categories are compared. The higher similarities of BBS posts in the same societal risk category reveal that BBS posts in the same societal risk category share more features than BBS posts in different categories, which manifests the feasibility of societal risk classification of BBS posts, and also reflects the possibility to improve the performance of societal risk monitoring.

关键词： Societal risk classification Tianya Forum cross validation pairwise similarity individual risk perception

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Modeling Distributed Stream Processing Systems Under Heavy Workload

Modeling Distributed Stream Processing Systems Under Heavy W...

引用

International Conference on Cyberworlds

作者： Muhammad Mudassar Qureshi Hanhua Chen Hai Jin Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

big data applications play a significant role in diverse fields. Distributed Stream Processing Engines (DSPEs) are widely used to support real time applications efficiently. Partitioning algorithms are used to partition data streams into multiple nodes to process in parallel to gain efficient performance. Aggregation cost is an important factor when process stateful streaming applications using such partitioning algorithms because it plays an important role on performance when final result is being produced in stateful streaming applications. However, impact of aggregation cost in stream processing is not discussed comprehensively in existing literature. We use performance modeling to identify the importance of aggregation cost when workload is high. We implement performance model on a multi-node cluster to predict the same behavior as on single resource performance model. We demonstrate that stateful streaming applications need more resources as compare to stateless applications when workload is high and both stateful and stateless applications are running in the same DSPE. Experiments results show that a stateful streaming application needs more resources compared to a stateless streaming application when both applications are running on the same DSPE when the workload is high. Further experiment results show that the performance modeling may be helpful to predict maximum workload that can be process on a DSPE and increase in parallelism level is not guaranteed to increase the performance of streaming applications.

关键词： Partitioning algorithms data models Predictive models Heuristic algorithms big data applications Throughput

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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

引用

15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Containers

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Experimental analyses on 2-hop-based and 3-hop-based link prediction algorithms

arXiv

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arXiv 2019年

作者： Zhou, Tao Lee, Yan-Li Wang, Guannan CompleX Lab University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Ant Financial Services Group Hangzhou310099

Link prediction is a significant and challenging task in network science. The majority of known methods are similarity-based, which assign similarity in-dices for node pairs and assume that two nodes of larger similarity have higher probability to be connected by a link. Due to their simplicity, interpretability and high efficiency, similarity-based methods, in particular those based only on local information, have already found successful applications on disparate fields. In this research domain, an intuitive consensus is that two nodes sharing common neighbors are very likely to have a link, while some recent evidences argue that the number of 3-hop paths more accurately predicts missing links than the number of common neighbors. In this paper, we implement extensive experimental comparisons between 2-hop-based and 3-hop-based similarity in-dices on 128 real networks. Our results indicate that the 3-hop-based indices perform slightly better with a winning rate about 55.88%, but which index is the best one still depends on the target network. Overall speaking, the class of Cannistraci-Hebb indices performs the best among all considered candidates. Copyright © 2019, The Authors. All rights reserved.

关键词： Complex networks

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Gut microbiota and diabetes: From correlation to causality and mechanism

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World Journal of Diabetes 2020年第7期11卷 293-308页

作者： Wei-Zheng Li Kyle Stirling Jun-Jie Yang Lei Zhang Microbiome-X Beijing Advanced Innovation Center for Big Data-Based Precision MedicineSchool of Medicine and EngineeringKey Laboratory of Big Data-Based Precision MedicineBeihang UniversityBeijing 100191China School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China Luddy School of Informatics Computing and EngineeringIndiana UniversityBloomingtonIN 47408United States The Crisis Technologies Innovation Lab Indiana UniversityThe Information Technology Services and the Pervasive Technology InstituteBloomingtonIN 47408United States Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine Jinan 250000Shandong ProvinceChina College of Life Science Qilu Normal UniversityJinan 250000Shandong ProvinceChina Microbiome Research Center Shandong Institutes for Food and Drug ControlJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Qilu Children's Hospital of Shandong UniversityJinan 250000Shandong ProvinceChina Microbiological Laboratory Lin Yi People’s HospitalLinyi 276000Shandong ProvinceChina Qingdao Human Microbiome Center The Affiliated Central Hospital of Qingdao UniversityQingdao 266000Shandong ProvinceChina Institute for Medical Dataology Cheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Department of Biostatistics School of Public HealthCheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Research Institute of PediatricsQilu Children's HospitalCheeloo College of MedicineShandong Universityand Jinan Children's HospitalJinan 250022Shandong ProvinceChina

In this review,we summarize the recent microbiome studies related to diabetes disease and discuss the key findings that show the early emerging potential causal roles for *** a global scale,diabetes causes a significant negative impact to the health status of human *** review covers type 1 diabetes and type 2 *** examine promising studies which lead to a better understanding of the potential mechanism of microbiota in diabetes *** appears that the human oral and gut microbiota are deeply interdigitated with *** is that *** studies of the human microbiome are capturing the attention of scientists and healthcare practitioners worldwide by focusing on the interplay of gut microbiome and *** studies focus on the role and the potential impact of intestinal microflora in *** paint a clear picture of how strongly microbes are linked and associated,both positively and negatively,with the fundamental and essential parts of diabetes in *** microflora seems to have an endless capacity to impact and transform *** conclude that there is clear and growing evidence of a close relationship between the microbiota and diabetes and this is worthy of future investments and research efforts.

关键词： Diabetes Microbiota Causality Mechanism Type 1 diabetes Type 2 diabetes Insulin resistance Inflammation Metabolites

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