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computational intelligence in Natural Language Processing and Social Media (CI-NLPSoMe Companion), ieee symposium on

作者： Charles Alba Division of Computational and Data Sciences Washington University in St Louis St. Louis MO USA

ISBN: (数字)9798331519742

ISBN: (纸本)9798331519759

Topic models are widely used across various domains to effectively cluster text-based documents and reveal their underlying themes. However, representing these clustered topics using keywords poses several challenges: difficulties in interpreting the topics, scalability issues in large web-based applications, and the potential for biased interpretations. To address these limitations, this study proposes complementing topic model outputs - the keywords representing clustered documents - with few-shot in-context learning (ICL) to generate coherent and interpretable topic labels. To assess the effectiveness of this approach, a labeled dataset of topics from Yelp reviews and New York Times (NYT) articles was utilized. Keywords generated from Yelp reviews, spanning five distinct topic modeling techniques - LDA, CorEx, ETM, BERTopic, and TopClus - along with their corresponding labels, were provided as few-shot examples to label topic modeling outputs of the NYT articles, and vice versa. Cosine similarities were calculated to measure the semantic similarities between the generated labels and the ground-truth labels. The results demonstrate that incorporating few-shot ICL can produce coherently labeled topics, achieving an average similarity of up to 80.9%. Overall, few-shot ICL outperformed existing prompt engineering methods in labeling topics from the topic modeling outputs by an average of 8.6%, even when few-shot examples were drawn from drastically different domains. However, performance was significantly correlated with the quality and coherence of the keywords generated by the topic models. Topic models that represented clusters with a less coherent set of keywords typically generated weaker, less interpretable, and semantically less accurate labels.

关键词： Text mining Text analysis Reviews Social networking (online) computational modeling Scalability Large language models Semantics Natural language processing Prompt engineering

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FMRT: Learning Accurate Feature Matching With Reconciliatory Transformer

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11826-11842页

作者： Wang, Li Zhang, Xinyu Jiang, Zhiqiang Dai, Kun Xie, Tao Yang, Lei Yu, Wenhao Shen, Yang Xu, Bin Li, Jun Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Tsinghua Univ Suzhou Automot Res Inst Suzhou 215200 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 119077 Singapore Harbin Inst Technol State Key Lab Robot & Syst Harbin 150006 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

Local Feature Matching, a pivotal component of numerous computer vision tasks (e.g., structure from motion and visual localization), has been effectively addressed by Transformer-based methods. Nevertheless, these methods solely incorporate long-range context information among keypoints with a fixed receptive field, which constrains the network from appropriately reconciling the importance of features with diverse receptive fields to realize complete image perception, hence limiting feature matching accuracy. In addition, these methods employ a conventional handcrafted encoding approach to incorporate positional information of keypoints into visual descriptors, which limits the capability of networks to extract effective positional encoding message. In this study, we propose FMRT, a novel detector-free method that reconciles local features with diverse receptive fields adaptively and utilizes parallel networks to realize reliable positional encoding. Specifically, FMRT proposes a dedicated reconciliatory transformer (RecFormer) that contains a global perception attention layer to identify visual descriptors with different receptive fields and integrate global context information under various scales, a perception weight layer to measure the importance of various receptive fields adaptively, and a local perception feed-forward network to extract deep aggregated multi-scale local feature representation. Moreover, we introduce a novel axis-wise position encoder (AWPE) that views positional encoding as two keypoints encoding tasks along the row and column dimensions, decouples the x- and y-coordinates of keypoints into two independent 1D vectors, and designs two parallel network branches to explicitly encodes geometric correlations among keypoints, hence realizing reliable positional encoding. Extensive experiments indicate that FMRT yields impressive performance on multiple tasks, including relative pose estimation, visual localization, homography estimation, and image

关键词： Feature extraction Visualization Transformers Encoding data mining Location awareness Correlation computational modeling Accuracy Vectors Local feature matching transformer deep learning position encoder

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Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based data

Enhancing Forecasting with a 2D Time Series Approach for Coh...

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computational intelligence for Financial Engineering and Economics Companion (CiFer Companion), ieee symposium on

作者： Yonathan Guttel Orit Moradov Nachi Lieder Asnat Greenstein-Messica Lightricks Jerusalem Israel

ISBN: (数字)9798331519704

ISBN: (纸本)9798331519711

This paper introduces a novel two-dimensional (2D) time series forecasting model that integrates cohort behavior over time, addressing challenges in small data environments. We demonstrate its efficacy using multiple real-world datasets, showcasing superior performance in accuracy and adaptability compared to reference models. The approach offers valuable insights for strategic decision-making across industries facing financial and marketing forecasting challenges.

关键词： Industries Economics Adaptation models Two-dimensional displays Time series analysis Decision making data visualization Predictive models data models Forecasting

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Trust Issues in Active Learning and Their Impact on Real-World Applications

Trust Issues in Active Learning and Their Impact on Real-Wor...

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Trustworthy, Explainable and Responsible computational intelligence (CITREx Companion), ieee symposium on

作者： Bjarne Jaster Martin Kohlhase Center for Applied Data Sciences University of Applied Sciences and Arts Bielefeld Bielefeld Germany

ISBN: (数字)9798331519728

ISBN: (纸本)9798331519735

The research area of active learning (AL) has been a subject of continuous attention over the past few decades. However, the application of AL in real-world scenarios remains considerably less popular than might be expected, given the success and advances of the research in this area. As this discrepancy has already been identified by previous researchers, an overview of the literature concerning this issue is given. The identified challenges are grouped and categorized, and existing approaches and solutions that address them are presented. Based on the identified challenges, we propose the hypothesis that a lack of trust in the success of AL is a key factor contributing to the disparity between the advancements of applications and research. In particular, the difficulty of evaluating the performance of an AL process, due to the absence of independent test data, is a major contributor to this trust issue. Finally, we identify research areas and questions that should receive greater attention in order to enhance trust in AL methods and encourage their adoption.

关键词： Active learning computational intelligence

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Exploring Open Dimensions in Artificial Immune Systems for High-Dimensional Tasks

Exploring Open Dimensions in Artificial Immune Systems for H...

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computational intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), ieee symposium on

作者： Axel Luiggi-Gørrissen Trygve Johansen Woldseth Pauline Catriona Haddow Depart. of Computer Science CRAB Lab NTNU Trondheim Norway

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

High-dimensional (HD) data is relevant to a wide range of fields e.g. remote sensing, network security, medicine and e-commerce. Improved methods are needed to handle such HD data. Artificial Immune Systems (AIS) have shown promise across various classification tasks. However, they are challenged for applications with HD spaces that thus limit their application to such fields. The work herein proposes a novel AIS-based model, the Flexible-Dimension-AIS (FD-AIS), incorporating open dimensions into antibody recognition regions (where antigens can be discovered) so as to address this challenge. Open dimensions enable ABs to extend coverage without being constrained by fixed boundaries, thereby allowing for improved coverage with reduced computational overhead. Key contributions of the model include the introduction of flexible, adaptable recognition regions. Experimental results highlighted the positive effect of unbounded recognition regions in FD-AIS, particularly with respect to efficiency - important when working with high-dimensional data.

关键词： Antigens High dimensional data computational modeling Artificial immune systems Network security Antibodies Electronic commerce Intelligent systems Remote sensing computational intelligence

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Robust European Call Option Pricing via Linear Regression

Robust European Call Option Pricing via Linear Regression

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computational intelligence for Financial Engineering and Economics Companion (CiFer Companion), ieee symposium on

作者： Ahmad W. Bitar Laboratory of Computer Science and Digital Society Université de Technologie de Troyes Troyes France

ISBN: (数字)9798331519704

ISBN: (纸本)9798331519711

The one-period trinomial option pricing model is well known in the literature as it considers three possible movement directions of the asset price. However, by equating the price of the option with the self-financing hedging portfolio at maturity, this yields a linear system of three equations with two unknowns that correspond to the coefficients for the delta hedging portfolio. Hence, the trinomial model is said to be incomplete, that is, there exists an infinite number of equivalent martingale measures. To deal with this incompleteness, this paper aims to price options via some robust linear regression techniques in order to mainly handle the problem of outliers that the least squares fails to consider. The proposed robust techniques are evaluated on numerical data, the results of which demonstrate their effectiveness for European call option pricing.

关键词： Linear systems Economics computational modeling Linear regression Europe Pricing Mathematical models Numerical models Portfolios computational intelligence

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Enhanced Bitcoin Price Prediction using RNN-GRU Algorithm with Optimized Parameters: Overcoming LSTM Ambiguities for Improved Accuracy and Efficiency 3

Enhanced Bitcoin Price Prediction using RNN-GRU Algorithm wi...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kathiravan, M. Meenakshi, M. Kaviya, M. Sreesubha, S. Sathyadurga, V. Neela Gandhi, V. Chennai India Department of CSE Department of Computational Intelligence SRM Institute of Science and Technology Kattangulathur Chennai India Department of Artificial Intelligence And Data Science Panimalar Engineering College Chennai India Department of AIML Rajalakshmi Engineering College Thandalam Chennai India Department of CSE Hindustan Institute of Technology and Science padur Chennai India Department of CSE Panimalar Engineering college Chennai India

ISBN: (纸本)9798331527549

Bitcoin's decentralised character and introduction in January 2009 can be mostly attributed to Satoshi Nakamoto, the unidentified designer of the digital money. Unlike regular money, Bitcoin has no value in and of itself and is not supported by any government or financial institution. The "mining" process consumes a lot of resources thus it cannot manage trades without a network of computers, often known as "nodes" or "miners." The Bitcoin blockchain, a catalogue of all the past transactions, is maintained by these nodes. Like equities, bitcoin is getting more and more well-known even if it is somewhat erratic as an investment. Its value varies greatly;hence it is difficult to predict when prices will adjust. Since Bitcoin is so erratic, automated approaches are growing more crucial for future projections regarding it. Often utilised for prediction, long short-term memory (LSTM) networks have constraints. LSTM focusses on short-term input and has a complex and occasionally ambiguous design, thus some claim it does not operate well when prior data is not significant to current forecasts. This has caused some to wonder if one can forecast Bitcoin's price using this approach. Made as a substitute for standard RNNs, the Gated Recurrent Unit (GRU) helps to prevent the fading gradient issue. Bitcoin price projections made by the GRU design are far more accurate than those derived from more antiquated methods. © 2025 ieee.

关键词： Long short-term memory

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Robust Unified Graphical Lasso Based on t Distribution

Robust Unified Graphical Lasso Based on t Distribution

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computational intelligence in Health and Medicine (CIHM), ieee symposium on

作者： Ryohei Moriya Kensuke Tanioka Mariko Takagishi Satoru Hiwa Tomoyuki Hiroyasu Graduate School of Life and Medical Sciences Doshisha University Kyoto Japan Department of Biomedical Sciences and Informatics Doshisha University Kyoto Japan School of Engineering Okayama University Okayama Japan

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

Estimating networks among variables from multi-variate data is often important to clarify relationships among variables. Graphical Lasso (GL) is a useful tool for network exploration and is used in genetic data analysis. However, when dealing with genetic data, there are cases where multi-block data observed from several different databases for the same set of variables are handled. Unified Graphical Lasso (UGL) is a method to estimate the common network structure of each block data from such multi-block data. However, it is known that the presence of noise in such data can affect the results of network structure estimation using ordinary UGL. To solve such problems, we propose Robust Unified Graphical Lasso based on t-distribution (robust UGL). In robust UGL, we also developed an algorithm to estimate parameters based on the EM algorithm. We compared and evaluated the results of UGL and robust UGL on artificial and genetic data. Experimental results show that robust UGL performs better than UGL on both artificial and genetic data. These results suggest that robust UGL may be effective for noisy data such as biometric data.

关键词： data analysis databases Noise Estimation Network analyzers Genetics Numerical simulation Noise measurement Unsupervised learning computational intelligence

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Identifying Aspect-Regimes for Enhanced ESG-Investing Through News data

Identifying Aspect-Regimes for Enhanced ESG-Investing Throug...

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computational intelligence for Financial Engineering and Economics (CiFer), ieee symposium on

作者： Fabian Billert Stefan Conrad Institute for Informatics Heinrich-Heine University of Düsseldorf Düsseldorf Germany

ISBN: (数字)9798331508319

ISBN: (纸本)9798331508326

ESG (Environmental, Social, Governance)-thematics play an important role for investing as they can represent additional risk for a company and subsequently its stock price. These ESG-thematics can be extracted from alternative data sources such as news data. However, the extend to which an ESG-event influences a company can vary greatly depending on the ESG-aspect of the event and the current expectations of the affected company. In this paper, we present an approach where we leverage Dynamic Time Warping (DTW) in order to identify periods in which certain ESG-aspects are closely aligned with the stockprice movement of a company. We demonstrate this approach using more than 50,000 news articles mined for 42 german companies between January 2023 and September 2024, (i) answering the question of which aspects are important for a company in what time-frames and (ii) showing that this approach increases the performance of an ESG-sensitive portfolio by a large amount.

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Analyzing the Cognitive Impact of Trauma from a Metaphorical Perspective: A Case Study on the Attempted Assassination of Donald Trump

Analyzing the Cognitive Impact of Trauma from a Metaphorical...

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computational intelligence in Natural Language Processing and Social Media (CI-NLPSoMe), ieee symposium on

作者： Meihui Jia Rui Mao Yunhe Xie Shen Ren Erik Cambria Nanyang Technological University Singapore Harbin Institute of Technology China Continental Automotive Singapore

ISBN: (数字)9798331508418

ISBN: (纸本)9798331508425

Research indicates that trauma can significantly affect human cognition. While previous studies have extensively examined the psychological consequences of trauma, the majority have focused on the long-term effects, particularly in relation to post-traumatic stress disorder, due to challenges in data collection. This paper seeks to fill this gap by introducing an interdisciplinary approach that combines cognitive linguistics and trauma theory to investigate the immediate cognitive effects of trauma, specifically through the analysis of metaphorical language. We explore the cognitive impact of trauma by analyzing Donald Trump's presidential campaign speeches delivered before and after the 2024 attempted assassination. Using MetaPro, a computational tool designed for metaphor analysis, we identify cognitive patterns from a large corpus of speeches. Our findings suggest that the assassination had a certain effect on Mr. Trump's cognitive processing, as reflected in changes in his metaphorical concept mappings. This traumatic event may prompt Mr. Trump to psychologically retreat into the cognitive patterns in 2016.

关键词： Social networking (online) Psychology Human factors Linguistics data collection Stability analysis Natural language processing Cognition computational intelligence

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