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

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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An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2025年

作者： Xiong, Yifei Yang, Xiliang Zhang, Sanguo He, Zhijian Purdue Univ Dept Stat W Lafayette IN USA South China Univ Technol Sch Math Guangzhou 510641 Peoples R China 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

Sequential neural posterior estimation (SNPE) techniques have been recently proposed for dealing with simulation-based models with intractable likelihoods. Unlike approximate Bayesian computation, SNPE techniques learn the posterior from sequential simulation using neural network-based conditional density estimators by minimizing a specific loss function. The SNPE method proposed by Lueckmann et al. (NeurIPS 2017) used a calibration kernel to boost the sample weights around the observed data, resulting in a concentrated loss function. However, the use of calibration kernels may increase the variances of both the empirical loss and its gradient, making the training inefficient. To improve the stability of SNPE, this paper proposes to use an adaptive calibration kernel and several variance reduction techniques. The proposed method greatly speeds up the process of training and provides a better approximation of the posterior than the original SNPE method and some existing competitors as confirmed by numerical experiments. We also managed to demonstrate the superiority of the proposed method for a high-dimensional model with a real-world dataset.

关键词： Approximate Bayesian computation Normalizing flows Simulator-based inference

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Artificial cheerleading in IEO: Marketing campaign or pump and dump scheme

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INFORMATION PROCESSING & management 2024年第1期61卷

作者： Tian, Yingjie Xie, Yuhao Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China MOE Social Sci Lab Digital Econ Forecasts & Policy Beijing 100190 Peoples R China

Initial coin offerings (ICOs) have been vulnerable to pump-and-dump schemes, where fraudsters spread false information on social media to inflate coin prices. However, initial exchange offerings (IEOs) are considered more reliable as exchanges conduct due diligence on projects. This study investigates the effectiveness of IEOs in mitigating pump-and-dump schemes by analyzing over 11,000 tweets posted by different accounts from 54 IEO projects one month prior to the IEO. We select 22 projects with at least 1000 tweets and use topic modeling to perform a descriptive analysis. We use botometer to produce a bot score for each tweet account and a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for sentiment analysis. We introduce penalized sentiment scores and interaction term and use an OLS regression model to identify the relationship between bot scores, sentiment values, and post-IEO performance. Our research finds that bot score is negatively associated with 30-day return and is positively associated with 30-day volatility, indicating the presence of pump-and -dump schemes in IEOs. We suggest that investors, exchanges, and regulators should take steps to assess the risks associated with IEOs and design appropriate interventions to prevent market manipulation.

关键词： Deep learning Social media analysis Initial exchange offering Bot detection

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End-to-End Multitask Learning With Vision Transformer

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第7期35卷 9579-9590页

作者： Tian, Yingjie Bai, Kunlong Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100190 Peoples R China

Multitask learning (MTL) is a challenging puzzle, particularly in the realm of computer vision (CV). Setting up vanilla deep MTL requires either hard or soft parameter sharing schemes that employ greedy search to find the optimal network designs. Despite its widespread application, the performance of MTL models is vulnerable to under-constrained parameters. In this article, we draw on the recent success of vision transformer (ViT) to propose a multitask representation learning method called multitask ViT (MTViT), which proposes a multiple branch transformer to sequentially process the image patches (i.e., tokens in transformer) that are associated with various tasks. Through the proposed cross-task attention (CA) module, a task token from each task branch is regarded as a query for exchanging information with other task branches. In contrast to prior models, our proposed method extracts intrinsic features using the built-in self-attention mechanism of the ViT and requires just linear time on memory and computation complexity, rather than quadratic time. Comprehensive experiments are carried out on two benchmark datasets, including NYU-Depth V2 (NYUDv2) and CityScapes, after which it is found that our proposed MTViT outperforms or is on par with existing convolutional neural network (CNN)-based MTL methods. In addition, we apply our method to a synthetic dataset in which task relatedness is controlled. Surprisingly, experimental results reveal that the MTViT exhibits excellent performance when tasks are less related.

关键词： Task analysis Transformers Neural networks Visualization Biological system modeling Benchmark testing Correlation Deep neural network algorithms machine learning applications multitask learning (MTL)

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

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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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Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 1051-1062页

作者： Li, Kai Wang, Bo Tian, Yingjie Qi, Zhiquan Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance. Fortunately, modification of loss function appears to solve this puzzle once and for all. In this article, we propose a pixel-based adaptive weighted cross-entropy (WCE) loss in conjunction with Jaccard distance to facilitate high-quality pixel-level road crack detection. Our work profoundly demonstrates the influence of loss functions on detection outcomes and sheds light on the sophisticated consecutive improvements in the realm of crack detection. Specifically, to verify the effectiveness of the proposed loss, we conduct extensive experiments on four public databases, that is, CrackForest, AigleRN, Crack360, and BJN260. Compared to the vanilla WCE, the proposed loss significantly speeds up the training process while retaining the performance.

关键词： Roads Training Costs Image edge detection Training data Sampling methods Adaptation models Crack detection Jaccard distance U-Net weighted cross-entropy (WCE)

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Fair Personalized Learner Modeling Without Sensitive Attributes 25

Fair Personalized Learner Modeling Without Sensitive Attribu...

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34th ACM Web Conference, WWW 2025

作者： Xu, Hefei Hou, Min Wu, Le Liu, Fei Yang, Yonghui Bai, Haoyue Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei China Institute of Dataspace Hefei Comprehensive National Science Center Anhui Hefei China

ISBN: (纸本)9798400712746

Personalized learner modeling uses learners’ historical behavior data to diagnose their cognitive abilities, a process known as Cognitive Diagnosis (CD). This is essential for web-based learning services such as learning resource recommendation and adaptive testing. However, prior studies have shown that CD models may unfairly correlate learners’ abilities with sensitive attributes (e.g., gender, region), leading to biased outcomes. While existing approaches mitigate this issue by decorrelating sensitive attributes from the modeling process, privacy concerns make collecting such attributes impractical. Furthermore, the presence of multiple sensitive attributes complicates fairness improvements. In this paper, we explore how to achieve fair personalized learner modeling without relying on any sensitive attribute input. We introduce a novel fairness objective tailored for personalized learner modeling and design a max-min strategy to facilitate both sensitive information inference and fair CD modeling. In the max step, we infer pseudo-labels by maximizing the fairness objective, while in the min step, we retrain the CD model by minimizing it. Additionally, we provide a theoretical guarantee that our framework reduces the upper bound of fairness generalization error. Extensive experiments demonstrate that the proposed framework significantly outperforms existing methods. © 2025 Copyright held by the owner/author(s).

关键词： Supervised learning

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DBKEM-AACS: a distributed key escrow model in blockchain with anonymous authentication and committee selection

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Science China(Information Sciences) 2023年第3期66卷 289-290页

作者： Axin XIANG Hongfeng GAO Youliang TIAN Liang WAN College of Computer Science and Technology Guizhou University Institute of Cryptography and Data Security Guizhou University Network and Information Management Center Guizhou University State Key Laboratory of Public Big Data Guizhou University Institute of Computer Software and Theory Guizhou University

Dear editor, key security is of great practical significance and demand to guarantee the security of digital assets in the blockchain system. At present, users prefer to escrow their assets on centralized institutions, but this phenomenon has gone against the unique characteristics of decentralization and anonymity in the blockchain. Among them, the suspense incidents of assets lock or lost are enough to prove that the security of escrowed keys on the exchanges is questionable.

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PCQPR: Proactive Conversational Question Planning with Reflection

PCQPR: Proactive Conversational Question Planning with Refle...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Guo, Shasha Liao, Lizi Zhang, Jing Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Singapore Management University Singapore

ISBN: (纸本)9798891761643

Conversational Question Generation (CQG) enhances the interactivity of conversational question-answering systems in fields such as education, customer service, and entertainment. However, traditional CQG, focusing primarily on the immediate context, lacks the conversational foresight necessary to guide conversations toward specified conclusions. This limitation significantly restricts their ability to achieve conclusion-oriented conversational outcomes. In this work, we redefine the CQG task as Conclusion-driven Conversational Question Generation (CCQG) by focusing on proactivity, not merely reacting to the unfolding conversation but actively steering it towards a conclusion-oriented question-answer pair. To address this, we propose a novel approach, called Proactive Conversational Question Planning with self-Refining (PCQPR). Concretely, by integrating a planning algorithm inspired by Monte Carlo Tree Search (MCTS) with the analytical capabilities of large language models (LLMs), PCQPR predicts future conversation turns and continuously refines its questioning strategies. This iterative self-refining mechanism ensures the generation of contextually relevant questions strategically devised to reach a specified outcome. Our extensive evaluations demonstrate that PCQPR significantly surpasses existing CQG methods, marking a paradigm shift towards conclusion-oriented conversational question-answering systems. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Identification of Core Technological Topics in the New Energy Vehicle Industry: The SAO-BERTopic Topic Modeling Approach Based on Patent Text Mining 5

Identification of Core Technological Topics in the New Energ...

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Joint Workshop of the 5th Extraction and Evaluation of knowledge Entities from Scientific Documents and the 4th AI + Informetrics, EEKE-AII 2024

作者： Zhu, Jianxin Chuang, Yutong Wang, Zhinan Li, Yunke Harbin Engineering University School of Economics and Management 150001 China Key Laboratory of Big Data and Business Intelligence Technology Ministry of Industry and Information Technology China

In the new energy vehicle industry, precise identification of core technologies is the key to promoting innovation and maintaining market competitiveness. In this article, a comprehensive approach combining information weight method and SAO-BERTopic topic model is proposed to extract and analyze core technologies from large-scale patent data. Through in-depth analysis of Incopat patent database, we use the information weight method to select high-quality core patents from four dimensions: technological, strategic, law and market value. These selected patents form the basis of the research data, which is then applied to the SAO-BERTopic model, which combines the advanced semantic understanding capabilities of BERTopic with the fine-structured characteristics of SAO analysis, greatly improving the efficiency and accuracy of identifying complex technical topics. This innovation of this research is not only applicable to the analysis of the technological development of the new energy vehicle industry, but also can provide valuable reference for other high-tech industries such as biomedicine, renewable energy and information technology. These fields also need to identify core technologies from a large number of patents. SAO-BERTopic's structured analysis framework can help these industries to insight into technology development trends, identify innovation opportunities, and provide data-driven decision support for enterprises, research institutions and governments, thus playing an important role in technology planning and market commercialization. © Copyright 2024 for this paper by its authors.

关键词： Commerce

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