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Leaving None Behind: data-Free Domain Incremental Learning for Major Depressive Disorder Detection

IEEE Transactions on Affective Computing 2024年第02期16卷 758-770页

作者： Chen, Tao Guo, Yanrong Hao, Shijie Hong, Richang Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China Hefei University of Technology Ministry of Education and School of Computer Science and Information Engineering Hefei230009 China

While deep learning techniques have shown promising performance in the Major Depressive Disorder (MDD) detection task, they still face limitations in real-world scenarios. Specifically, given the data scarcity, some efforts have resorted to aggregating data from different domains to expand the data volume. However, their effectiveness is currently limited by the domain gap and data privacy. Additionally, the class imbalance issue is particularly severe in our application, leading to biased classifying performance accordingly. To address these challenges, we propose data-Free Domain Incremental Learning for the MDD detection (DIL-MDD) task, accommodating multiple feature distributions by only accessing well-trained models from previous domains and the data in the current domain. Specifically, DIL-MDD consists of two key modules: Adaptive Class-tailored Threshold Learning (ACTL) and data-Free Domain Alignment (DFDA). The first module measures the discrepancy between the outputs of two sequential domains, based on which we learn a class-tailored threshold adaptively. Building on this, we differentiate between samples that either exhibit similarities or dissimilarities with the previous domain, where this similar sample set is identified to investigate the feature distribution of the historical data. The second module imposes an alignment constraint to narrow the gap between these two sample sets, thereby exploring the expertise of the previous domain. To validate the effectiveness of the proposed method, we conduct extensive experiments on the public MDD datasets, i.e., DAIC-WOZ, MODMA, and CMDC. We also apply our method to another mental health condition, Autism Spectrum Disorder (ASD), to further demonstrate its applicability. Finally, the ablation studies validate the superiority of the proposed modules. © 2010-2012 IEEE.

关键词： Adversarial machine learning

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ViGT: proposal-free video grounding with a learnable token in the transformer

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Science China(Information Sciences) 2023年第10期66卷 196-212页

作者： Kun LI Dan GUO Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artificial Intelligence Hefei Comprehensive National Science Center

The video grounding(VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer(ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows.(1) The token is unrelated to the video or the query and avoids data bias toward the original video and query.(2) The token simultaneously performs global context aggregation from video and query ***, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention(i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets:ANet-Captions, TACoS, and YouCookⅡ. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

关键词： video grounding temporal sentence grounding boundary regression token learning proposal-free

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Self-Adaptive Imbalanced Domain Adaptation With Deep Sparse Autoencoder

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1293-1304页

作者： Zhu, Yi Wu, Xindong Li, Yun Qiang, Jipeng Yuan, Yunhao Yangzhou University School of Information Engineering Yangzhou225012 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China Hefei230002 China

Domain adaptation aims to transfer knowledge between different domains to develop an effective hypothesis in the target domain with scarce labeled data, which is an effective method for remedying the problem of labeled data requirement in deep learning. In reality, it is unavoidable that the dataset has a large gap in the number of positive and negative instances across different categories in source and target domains, which is the imbalanced domain adaptation problem. However, since the imbalanced degree always varies greatly in different source- and target-domain datasets, most of the existing imbalanced domain adaptation models fix the imbalanced parameters, which cannot adapt to the change of the proportion between positive and negative instances in different domains. To address this problem, in this article, we propose a self-adaptive imbalanced domain adaptation method via a deep sparse autoencoder, which can adjust the model automatically according to the imbalanced extent for bridging the chasm of domains. More specifically, the self-adaptive imbalanced cross-entropy loss is designed for emphasizing more on minority categories and compensating the bias of training loss automatically. In addition, to alleviate the deficient problem of labeled data, we further propose the unlabeled information incorporating method by minimizing the distribution discrepancy of high-level representation space between the source and target domains. Experiments on several real-world datasets demonstrate the effectiveness of our method compared to other state-of-the-art methods. © 2020 IEEE.

关键词： Noise abatement

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Size-fixed group discovery via multi-constrained graph pattern matching

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Information Sciences 2025年 690卷

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education 230601 Hefei China School of Computer Science and Information Engineering Hefei University of Technology 230601 Hefei China Department of Computing Macquarie University Sydney 2122 Australia

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

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Moment+: Mining closed frequent itemsets over data stream

Moment+: Mining closed frequent itemsets over data stream

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4th International Conference on Advanced data Mining and Applications, ADMA 2008

作者： Li, Haifeng Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)3540881913

Closed frequent itemsets(CFI) mining uses less memory to store the entire information of frequent itemsets thus is much suitable for mining stream. In this paper, we discuss recent CFI mining methods over stream and presents an improved algorithm Moment+ based on the existent one Moment. Moment+ focuses on the problem of mining CFI over data stream sliding window and proposes a new structure Extended Closed Enumeration Tree(ECET) to store the CFIs and nodes' BPN which is introduced to reduce the search space, with which new mining method is designed to mine more rapidly with little memory cost sacrifice. The experimental results show that this method is effective and efficient. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Mining

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Gender Disparity in Expressed Emotions within Health-Related Online Support Groups

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Proceedings of the Association for Information Science and Technology 2021年第1期58卷 883-885页

作者： Zhao, Yuehua Wang, Hao Deng, Sanhong Chen, Ye Nanjing University Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing China Central China Normal University Wuhan China

Online support groups offer a new way to users to communicate with others regarding certain health issues. Taking autism-related support groups on Facebook as an example, we examine whether the expressed emotions differ between female and male users in online health-related support groups and whether such gender disparity varied based on the topics of the groups. Experimental results reveal a significant gender difference of expressed emotions in the groups. We find that female users tended to express more positive emotions in the group discussions than the male group members did. In addition, users appeared to express different sentiments within the groups focused on various topics. Male users tend to convey more negative emotions in the group that related to treatment, while female users were more positive when posted in the research-related group than male users were. This study is beneficial for tracking and moderating the emotional environment in online support groups. 84 Annual Meeting of the Association for Information Science & Technology | Oct. 29 – Nov. 3, 2021 | Salt Lake City, UT. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

关键词： Sentiment analysis

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A sampling-based algorithm for approximating maximum average value region in wireless sensor network

A sampling-based algorithm for approximating maximum average...

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International Workshop on Scheduling and Resource Management for Parallel and Distributed Systems

作者： Zhang, Hao Wu, Zhongbo Li, Deying Chen, Hong School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

ISBN: (纸本)9780769541570

in wireless sensor network, sensory readings are often noisy due to the imprecision of measuring hardware and the disturbance of deployment environment, so it is often inaccurate if we use individual sensor readings to answer queries. In this paper, we consider a useful application of sensor network: maximum average value region query. This query returns the region with the maximum average value among all possible regions in the network, where the region is a fix-sized circle pre-defined by users. Using the average value of a region to answer the query, noises between sensors will be neutralized with each other, which will make the results more reliable. However, because of the huge amount of possible regions in the network, it is costly to process the query exactly. Therefore, w e propose a sampling-based algorithm AMAVR to deal with the problem approximately. AMAVR uses a background value to prun e the useless regions which cannot be the result. A further optimization strategy is also given to handle the situation that, background value based filter does not work when some individual sensor nodes have higher values than their neighbors. By using both of the two techniques, the scale of the sampling population can be effectively reduced, that is, we cost less energy to get a satisfying result. At last, the conducted simulations demonstrate the energy efficiency of the proposed methods in our paper. © 2010 IEEE.

关键词： Sensor nodes

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Cutting Learned Index into Pieces: An In-depth Inquiry into Updatable Learned Indexes 39

Cutting Learned Index into Pieces: An In-depth Inquiry into ...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Ge, Jiake Shi, Boyu Chai, Yanfeng Luo, Yuanhui Guo, Yunda He, Yinxuan Chai, Yunpeng Moe Key Laboratory of Data Engineering and Knowledge Engineering China Renmin University of China School of Information China

ISBN: (纸本)9798350322279

Numerous high-performance updatable learned indexes have recently been designed to support the writing requirements in practical systems. Researchers have proposed various strategies to improve the availability of updatable learned indexes. However, it is unclear which strategy is more profitable. Therefore, we deconstruct the design of learned indexes into multiple dimensions and in-depth evaluate their impacts on the overall performance, respectively. Through the in-depth exploration of learned indexes, we reckon that the approximation algorithm is the most crucial design dimension for improving the performance of the learned indexes rather than the popular works that focus on the learned index structure. Moreover, this paper makes a comprehensive end-to-end evaluation based on a high-performance key-value store to answer people's concerns about which learned index is better and whether learned indexes can outperform traditional ones. Finally, according to end-to-end and in-depth evaluation results, we give some constructive suggestions on designing a better learned index in these dimensions, especially how to design an excellent approximate algorithm to improve the lookup and insertion performance of learned indexes. © 2023 IEEE.

关键词： Approximation algorithms

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A framework for personalized management of domain ontologies

A framework for personalized management of domain ontologies

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2012 IEEE/ACIS 11th International Conference on Computer and Information Science, ICIS 2012

作者： Liu, Runnan Wang, Puwei Zheng, Shixian Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China

ISBN: (纸本)9780769546940

As the sharable and reusable domain knowledge, domain ontology increasingly serves as a foundation for semantic Web. Personalized management of domain ontologies is to provide personalized views of domain ontologies to users during run time according to user preferences. It helps that users can focus on their interested parts, instead of the whole. It increases the efficiency of ontology-based application. This paper proposes a framework for personalized management of domain ontologies. In this framework, a user model firstly is introduced to describe user preferences. Secondly, domain ontologies are decomposed into moderate-scale modules with high cohesion and low coupling. During run time, modules that a user is interested in are selected out based on the user's preferences. Finally, the selected modules are combined to construct personalized views of domain ontologies for the user. © 2012 IEEE.

关键词： Ontology

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ETD-cache: An expiration-time driven cache scheme to make SSD-based read cache endurable and cost-efficient 15

ETD-cache: An expiration-time driven cache scheme to make SS...

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12th ACM International Conference on Computing Frontiers, CF 2015

作者： Dai, Ningwei Chai, Yunpeng Liang, Yushi Wang, Chunling Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing100872 China

ISBN: (纸本)9781450333580

Recently flash-based solid-state drives (SSDs) have been widely deployed as cache devices to boost system performance. However, classical SSD cache algorithms (e.g. LRU) replace the cached data frequently to maintain high hit rates. Such aggressive data updating strategies result in too many writing operations on SSDs and make them wear out quickly, which finally leads to high costs of SSDs for enterprise applications. In this paper, we propose a novel Expiration-Time Driven Cache (ETD-Cache) method to solve this problem. In ETD-Cache, an active data eviction mechanism is adopted. An already cached block leaves the SSD cache if and only if there is no access to it for a time longer than a specified expiration time. This mechanism gives more time for the cached contents to wait for their following accesses and limits the admission of newly arrived blocks to generate less SSD writes. In addition, a low-overhead candidate management module is designed to maintain the most popular data in the system for the potential cache replacement. The simulations driven by a series of typical real-world traces indicate that due to the great reduction on data updating frequency, ETD-Cache lowers the total SSD costs by 98.45% compared with LRU under the same cache hit rate. Copyright 2015 ACM.

关键词： Costs

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