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A Prompt-Based Topic-Modeling Method for Depression Detection on Low-Resource data

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第1期11卷 1430-1439页

作者： Guo, Yanrong Liu, Jilong Wang, Lei Qin, Wei Hao, Shijie Hong, Richang Hefei Univ Technol HFUT Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Peoples R China Hefei Univ Technol HFUT Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 178902 Singapore

Depression has a large impact on one's personal life, especially during the COVID-19 pandemic. People have been trying to develop reliable methods for the depression detection task. Recently, methods based on deep learning have attracted much attention from the research community. However, they still face the challenge that data collection and annotation are difficult and expensive. In many real-world applications, only a small number of or even no training data are available. In this context, we propose a Prompt-based Topic-modeling method for Depression Detection (PTDD) on low-resource data, aiming to establish an effective way of depression detection under the above challenging situation. Instead of learning discriminating features from a small amount of labeled data, the proposed framework turns to leverage the generalization power of pretrained language models. Specifically, based on the question-and-answer routine during the interview, we first reorganize the text data according to the predefined topics for each interviewee. Via the prompt-based framework, we then predict whether the next-sentence prompt is emotionally positive or not. Finally, the depression detection task can be achieved based on the obtained topicwise predictions through a simple voting process. In the experiments, we validate the effectiveness of our model under several low-resource data settings. The results and analysis demonstrate that our PTDD achieves acceptable performance when only a few training samples or even no training samples are available.

关键词： Depression Task analysis Interviews Bit error rate Predictive models Feature extraction data models Depression detection low resource prompt learning

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Sampling-based Estimation of the Number of Distinct Values in Distributed Environment 22

Sampling-based Estimation of the Number of Distinct Values i...

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28th ACM SIGKDD Conference on knowledge Discovery and data mining (KKD)

作者： Li, Jiajun Wei, Zhewei Ding, Bolin Dai, Xiening Lu, Lu Zhou, Jingren Renmin Univ China Beijing Peoples R China Alibaba Grp Hangzhou Peoples R China Gaoling Sch Artificial Intelligence Peng Cheng Lab Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450393850

In data mining, estimating the number of distinct values (NDV) is a fundamental problem with various applications. Existing methods for estimating NDV can be broadly classified into two categories: i) scanning-based methods, which scan the entire data and maintain a sketch to approximate NDV;and ii) sampling-based methods, which estimate NDV using sampling data rather than accessing the entire data warehouse. Scanning-based methods achieve a lower approximation error at the cost of higher I/O and more time. Sampling-based estimation is preferable in applications with a large data volume and a permissible error restriction due to its higher scalability. However, while the sampling-based method is more effective on a single machine, it is less practical in a distributed environment with massive data volumes. For obtaining the final NDV estimators, the entire sample must be transferred throughout the distributed system, incurring a prohibitive communication cost when the sample rate is significant. This paper proposes a novel sketch-based distributed method that achieves sub-linear communication costs for distributed sampling-based NDV estimation under mild assumptions. Our method leverages a sketch-based algorithm to estimate the sample's frequency of frequency in the distributed streaming model, which is compatible with most classical sampling-based NDV estimators. Additionally, we provide theoretical evidence for our method's ability to minimize communication costs in the worstcase scenario. Extensive experiments show that our method saves orders of magnitude in communication costs compared to existing sampling- and sketch-based methods.

关键词： sampling distributed environment NDV communication

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Gene-environment interaction identification via penalized robust divergence

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BIOMETRICAL JOURNAL 2022年第3期64卷 461-480页

作者： Ren, Mingyang Zhang, Sanguo Ma, Shuangge Zhang, Qingzhao Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Yale Sch Publ Hlth Dept Biostat New Haven CT USA Xiamen Univ Wang Yanan Inst Studies Econ Sch Econ Dept Stat & Data SciFujian Key Lab Stat Fujian 361005 Peoples R China

In high-throughput cancer studies, gene-environment interactions associated with outcomes have important implications. Some commonly adopted identification methods do not respect the "main effect, interaction" hierarchical structure. In addition, they can be challenged by data contamination and/or long-tailed distributions, which are not uncommon. In this article, robust methods based on gamma-divergence and density power divergence are proposed to accommodate contaminated data/long-tailed distributions. A hierarchical sparse group penalty is adopted for regularized estimation and selection and can identify important gene-environment interactions and respect the "main effect, interaction" hierarchical structure. The proposed methods are implemented using an effective group coordinate descent algorithm. Simulation shows that when contamination occurs, the proposed methods can significantly outperform the existing alternatives with more accurate identification. The proposed approach is applied to the analysis of The Cancer Genome Atlas (TCGA) triple-negative breast cancer data and Gene Environment Association Studies (GENEVA) Type 2 Diabetes data.

关键词： divergence gene-environment interaction hierarchical structure penalized identification robustness

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Evaluating Search Result Diversity using Intent Hierarchies 16

Evaluating Search Result Diversity using Intent Hierarchies

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39th International ACM SIGIR conference on Research and Development in Information Retrieval

作者： Wang, Xiaojie Dou, Zhicheng Sakai, Tetsuya Wen, Ji-Rong Renmin Univ China Sch Informat Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Waseda Univ Dept Comp Sci & Engn Tokyo Japan MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450340694

Search result diversification aims at returning diversified document lists to cover different user intents for ambiguous or broad queries. Existing diversity measures assume that user intents are independent or exclusive, and do not consider the relationships among the intents. In this paper, we introduce intent hierarchies to model the relationships among intents. Based on intent hierarchies, we propose several hierarchical measures that can consider the relationships among intents. We demonstrate the feasibility of hierarchical measures by using a new test collection based on TREC Web Track 2009-2013 diversity test collections. Our main experimental findings are: (1) Hierarchical measures are generally more discriminative and intuitive than existing measures using flat lists of intents;(2) When the queries have multilayer intent hierarchies, hierarchical measures are less correlated to existing measures, but can get more improvement in discriminative power;(3) Hierarchical measures are more intuitive in terms of diversity or relevance. The hierarchical measures using the whole intent hierarchies are more intuitive than only using the leaf nodes in terms of diversity and relevance.

关键词： Ambiguity Diversity Evaluation Novelty Hierarchy

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Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need 21

Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Nee...

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30th ACM International Conference on Information and knowledge management (CIKM)

作者： Ma, Zhengyi Dou, Zhicheng Xu, Wei Zhang, Xinyu Jiang, Hao Cao, Zhao Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Huawei Distributed & Parallel Software Lab Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450384469

Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especially in the ad-hoc retrieval area. Existing pre-training approaches tailored for IR tried to incorporate weak supervised signals, such as query-likelihood based sampling, to construct pseudo query-document pairs from the raw textual corpus. However, these signals rely heavily on the sampling method. For example, the query likelihood model may lead to much noise in the constructed pre-training data. In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval. Since the anchor texts are created by webmasters and can usually summarize the target document, it can help to build more accurate and reliable pre-training samples than a specific algorithm. Considering different views of the downstream ad-hoc retrieval, we devise four pre-training tasks based on the hyperlinks. We then pre-train the Transformer model to predict the pair-wise preference, jointly with the Masked Language Model objective. Experimental results on two large-scale ad-hoc retrieval datasets show the significant improvement of our model compared with the existing methods.

关键词： Ad-hoc Retrieval Pre-training Hyperlinks Anchor Texts

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Robust Tracking via Uncertainty-Aware Semantic Consistency

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第4期33卷 1740-1751页

作者： Ma, Jie Lan, Xiangyuan Zhong, Bineng Li, Guorong Tang, Zhenjun Li, Xianxian Ji, Rongrong Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Xiamen Univ Sch Informat Dept Artificial Intelligence Media Analyt & Comp Lab Xiamen Peoples R China

Robust tracking has a variety of practical applications. Despite many years of progress, it is still a difficult problem due to enormous uncertainties in real-world scenes. To address this issue, we propose a robust anchor-free based tracking model with uncertainty estimation. Within the model, a new data-driven uncertainty estimation strategy is proposed to generate uncertainty-aware features with promising discriminative and descriptive power. Then, a simple yet effective pyramid-wise cross correlation operation is constructed to extract multi-scale semantic features that provide rich correlation information for uncertainty-aware estimation and thus enhances the tracking robustness. Finally, a semantic consistency checking branch is designed to further estimate uncertainty of output results from the classification and regression branches by adaptively generating semantically consistent labels. Experiments on six benchmarks (i.e., OTB100, VOT2018, VOT2020, TrackingNet, GOT-10K and LaSOT) show the competing performance of our tracker with 130 FPS.

关键词： Uncertainty Feature extraction Target tracking Semantics Correlation Estimation Adaptation models Object tracking anchor-free uncertainty estimation

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Second-Order Markov Assumption Based Bayes Classifier for Networked data With Heterophily

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IEEE ACCESS 2019年 7卷 34153-34161页

作者： Dong, Sa Liu, Dayou Ouyang, Ruochuan Zhu, Yungang Li, Lina Li, Tingting Liu, Jie Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China Jilin Univ Big Data & Network Management Ctr Changchun 130012 Jilin Peoples R China

The classification of networked data is an interesting and challenging problem. Most traditional relational classifiers that are based on the principle of homophily have an unsatisfactory classification performance in networks with heterophily. This is because these methods treat inhomogeneous networks homogeneously. A progression of a network-only Bayes-classifier-based second-order Markov assumption is proposed for heterophilous networks in this paper to address this problem. First, we estimate the class distribution of an unlabeled node according to the class distribution of its neighbors' neighbors. In this process, we perform this computation on the known and unknown neighbors separately. Second, we combine the two parts using multinomial na i ve Bayesian classification. Meanwhile, we pair a relaxation labeling collective inference method (which imports simulated annealing) with this new method to update the class distributions at each iteration. Comparisons of the experimental results demonstrate that the proposed method performs better when the networks are heterophilous.

关键词： Artificial intelligence data mining heterophilous networks machine learning networked data classification relational classifier

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Robust high-dimensional regression for data with anomalous responses

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ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 2021年第4期73卷 703-736页

作者： Ren, Mingyang Zhang, Sanguo Zhang, Qingzhao Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China Xiamen Univ Dept Stat Wang Yanan Inst Studies Econ MOE Key Lab EconSch Econ Xiamen 361005 Peoples R China Xiamen Univ Fujian Key Lab Stat Xiamen 361005 Peoples R China

The accuracy of response variables is crucially important to train regression models. In some situations, including the high-dimensional case, response observations tend to be inaccurate, which would lead to biased estimators by directly fitting a conventional model. For analyzing data with anomalous responses in the high-dimensional case, in this work, we adopt gamma-divergence to conduct variable selection and estimation methods. The proposed method possesses good robustness to anomalous responses, and the proportion of abnormal data does not need to be modeled. It is implemented by an efficient coordinate descent algorithm. In the setting where the dimensionalitypcan grow exponentially fast with the sample sizen, we rigorously establish variable selection consistency and estimation bounds. Numerical simulations and an application on real data are presented to demonstrate the performance of the proposed method.

关键词： Anomalous responses Robust gamma-divergence High-dimensional data

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Machine learning models for early-stage investment decision making in startups

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MANAGERIAL AND DECISION ECONOMICS 2024年第3期45卷 1259-1279页

作者： Shi, Yong Eremina, Ekaterina Long, Wen Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

This study demonstrates the efficacy of machine learning techniques for evidence-based evaluation of early-stage ventures. Leveraging real-world data on 24,965 startups across diverse sectors and countries sourced from Crunchbase, we develop predictive models using algorithms including random forest, XGBoost, and support vector machines. Rigorous training and testing on a 70-30 split of the data reveal that the algorithms can effectively classify startups as successful or not, achieving over 90% accuracy. Random forest emerges as the top performer, followed closely by XGBoost. This research demonstrates the immense potential of machine learning techniques in forecasting startup success to inform management practice.

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Genre-based Link prediction in Bipartite Graph for Music Recommendation

Genre-based Link prediction in Bipartite Graph for Music Rec...

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4th International Conference on Information Technology and Quantitative management (ITQM) - Promoting Business Analytics and Quantitative management of Technology

作者： Zhao, Daozhen Zhang, Lingling Zhao, Weiqi Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China

The bipartite graph method of link prediction can apply in many fields of recommendations, with the nodes (users and items) and links (interactions between users and items). However, that links cannot represent the users' dual preferences (like and dislike). Some researchers improved that limits by complex number representations, but still not consider the influence of users' similarity recommendation performance. Here, we proposed an improved method to cope with this deficiency, build the relational dualities by complex number representations and computing the users' similarity by genres weight relations. In experiments with the Xiami. com music dataset, the proposed music genre weight- based music recommendation model (MGW) performances better than the CORLP method. (C) 2016 The Authors. Published by Elsevier B. V.

关键词： Music recommendation Link prediction Music genres

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