检索结果-内蒙古大学图书馆

37th Annual European Simulation and Modelling Conference, ESM 2023

作者： Zhang, Xiao Paz, Iván Nebot, Àngela Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9789492859280

When confronted with imbalanced datasets, traditional classifiers frequently struggle to correctly categorize samples from the minority class, adversely impacting the overall predictive performance of machine learning models. Current oversampling techniques generally focus on data interpolation through neighbor selection, often neglecting to uncover underlying data structures and relationships. This study introduces a novel application for RuLer, an algorithm originally developed for identifying sound patterns in the artistic domain of live coding. When adapted for data oversampling (as Ad-RuLer), the algorithm shows significant promise in addressing the challenges associated with imbalanced class distribution. We undertake a thorough comparative evaluation of Ad-RuLer against established oversampling algorithms such as SMOTE, ADASYN, Tomek-links, Borderline-SMOTE, and KmeansSMOTE. The evaluation employs various classifiers including logistic regression, random forest, and XGBoost, and is conducted over six real-world biomedical datasets with varying degrees of imbalance. © 2023 ESM. All Rights Reserved.

关键词： Classification data synthesis Imbalanced data Oversampling Rule-based approach

来源：评论

学校读者我要写书评

暂无评论

Debate-to-Write: A Persona-Driven Multi-Agent Framework for Diverse Argument Generation 31

Debate-to-Write: A Persona-Driven Multi-Agent Framework for ...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Hu, Zhe Chan, Hou Pong Li, Jing Yin, Yu Department of Computing The Hong Kong Polytechnic University Hong Kong DAMO Academy Alibaba Group China Research Centre for Data Science & Artificial Intelligence Hong Kong Department of Computer and Data Sciences Case Western Reserve University United States

ISBN: (纸本)9798891761964

Writing arguments is a challenging task for both humans and machines. It entails incorporating high-level beliefs from various perspectives on the topic, along with deliberate reasoning and planning to construct a coherent narrative. Current language models often generate outputs autoregressively, lacking explicit integration of these underlying controls, resulting in limited output diversity and coherence. In this work, we propose a persona-based multi-agent framework for argument writing. Inspired by the human debate, we first assign each agent a persona representing its high-level beliefs from a unique perspective, and then design an agent interaction process so that the agents can collaboratively debate and discuss the idea to form an overall plan for argument writing. Such debate process enables fluid and nonlinear development of ideas. We evaluate our framework on argumentative essay writing. The results show that our framework generates more diverse and persuasive arguments by both automatic and human evaluations. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Topological Identification of Agent Status in Information Contagions: Application to Financial Markets

arXiv

引用

arXiv 2024年

作者： Goel, Anubha Hansen, Henri Kanniainen, Juho Computing Science/Financial Computing and Data Analytics Group Tampere University Tampere33720 Finland

Cascade models serve as effective tools for understanding the propagation of information and diseases within social networks. Nevertheless, their applicability becomes constrained when the states of the agents (nodes) are hidden and can only be inferred through indirect observations or symptoms. This study proposes a Mapper-based strategy to infer the status of agents within a hidden information cascade model using expert knowledge. To verify and demonstrate the method we identify agents who are likely to take advantage of information obtained from an inside information network. We do this using data on insider networks and stock market transactions. Recognizing the sensitive nature of allegations of insider trading, we design a conservative approach to minimize false positives, ensuring that innocent agents are not wrongfully implicated. The Mapper-based results systematically outperform other methods, such as clustering and unsupervised anomaly detection, on synthetic data. We also apply the method to empirical data and verify the results using a statistical validation method based on persistence homology. Our findings highlight that the proposed Mapper-based technique successfully identifies a subpopulation of opportunistic agents within the information cascades. The adaptability of this method to diverse data types and sizes is demonstrated, with potential for tailoring for specific applications. © 2024, CC BY-SA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第1期49卷 81-92页

作者： Abdullah, Lazim Awang, Noor Azzah Qiyas, Muhammad Special Interest Group of Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Mathematics Riphah International University Failsabad Campus Pakistan

Failure Modes and Effects Analysis (FMEA) is a widely used tool for risk analysis, primarily to identify risk factors affecting system quality. Due to the limitations of the traditional FMEA model, several recent models incorporating advanced fuzzy set extensions have been developed to enhance the reliability of risk assessment outcomes. However, most of these models limit expert flexibility in expressing preferences and often overlook the impact of unequal expert weights and the stability of risk ranking results. This study introduces a new FMEA model based on Q-Rung Orthopair Fuzzy Sets (Q-ROFSs), termed Q-ROFSs-FMEA. Q-ROFSs, an extension of intuitionistic fuzzy sets, introduce a new linguistic term. The Q-ROFSs-FMEA model considers the unequal weights of experts, enabling a dynamic representation of expert preferences. These weights and the linguistic evaluation of risk factors are integrated through an aggregation operator, facilitating consensus among experts. The model is applied to a case study on COVID-19 risk factors, revealing that ‘older age’ (risk priority number 0.000012) is the highest risk factor, while ‘gender’ (risk priority number -0.0037) is the lowest. It is found that the ranking of risk factors determined by the Q-ROFSs-FMEA model is obtained as FM1 FM3 FM6 FM4 FM5 FM7 FM8 FM2 . Furthermore, a comparative analysis indicates consistent ranking results across different models, demonstrating the reliability of the proposed model. The case study and comparative analysis validate the effectiveness and applicability of the Q-ROFSsbased risk assessment model. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Failure modes

来源：评论

学校读者我要写书评

暂无评论

CONTINUAL LEARNING WITH EMBEDDING LAYER SURGERY AND TASK-WISE BEAM SEARCH USING WHISPER

arXiv

引用

arXiv 2025年

作者： Kwok, Chin Yuen Yip, Jia Qi Chng, Eng Siong College of Computing and Data Science Nanyang Technological University Singapore Alibaba Group Singapore

Current Multilingual ASR models only support a fraction of the world’s languages. Continual Learning (CL) aims to tackle this problem by adding new languages to pre-trained models while avoiding the loss of performance on existing languages, also known as Catastrophic Forgetting (CF). However, existing CL methods overlook the adaptation of the token embedding lookup table at the decoder, despite its significant contribution to CF. We propose Embedding Layer Surgery where separate copies of the token embeddings are created for each new languages, and one of the copies is selected to replace the old languages embeddings when transcribing the corresponding new language. Unfortunately, this approach means LID errors also cause incorrect ASR embedding selection. Our Task-wise Beam Search allows self-correction for such mistakes. By adapting Whisper to 10 hours of data for each of 10 unseen languages from Common Voice, results show that our method reduces the Average WER (AWER) of pre-trained languages from 14.2% to 11.9% compared with Experience Replay, without compromising the AWER of the unseen languages. © 2025, CC BY-NC-ND.

关键词： Table lookup

来源：评论

学校读者我要写书评

暂无评论

Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

引用

Computers, Materials & Continua 2023年第3期74卷 5431-5446页

作者： Muhammad Umair Zafar Saeed Faisal Saeed Hiba Ishtiaq Muhammad Zubair Hala Abdel Hameed Faculty of Information Technology University of Central PunjabLahore54590Pakistan DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirmingham B47XGUK Faculty of Computer and Information Systems Fayoum University63514Egypt Khaybar Applied College Taibah UniversitySaudi Arabia

As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology(ICT)and cloud *** a result of the complicated architecture of cloud computing,the distinctive working of advanced metering infrastructures(AMI),and the use of sensitive data,it has become challenging tomake the SG *** of the SG are categorized into two main categories,Technical Losses(TLs)and Non-Technical Losses(NTLs).Hardware failure,communication issues,ohmic losses,and energy burnout during transmission and propagation of energy are ***’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft,along with tampering with AMI for bill reduction by fraudulent *** research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load *** our proposed methodology,a hybrid Genetic Algorithm and Support Vector Machine(GA-SVM)model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London,UK,for theft detection.A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%,compared to studies conducted on small and limited datasets.

关键词： Big data data analysis feature engineering genetic algorithm machine learning

来源：评论

学校读者我要写书评

暂无评论

Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity 23

Transformer as a hippocampal memory consolidation model base...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Dong-Kyum Kim Jea Kwon Meeyoung Cha C. Justin Lee IBS Data Science Group IBS Center for Cognition and Sociality IBS Data Science Group and KAIST School of Computing

The hippocampus plays a critical role in learning, memory, and spatial representation, processes that depend on the NMDA receptor (NMDAR). Inspired by recent findings that compare deep learning models to the hippocampus, we propose a new nonlinear activation function that mimics NMDAR dynamics. NMDAR-like nonlinearity shifts short-term working memory into long-term reference memory in transformers, thus enhancing a process that is similar to memory consolidation in the mammalian brain. We design a navigation task assessing these two memory functions and show that manipulating the activation function (i.e., mimicking the Mg2+-gating of NMDAR) disrupts long-term memory processes. Our experiments suggest that place cell-like functions and reference memory reside in the feed-forward network layer of transformers and that nonlinearity drives these processes. We discuss the role of NMDAR-like nonlinearity in establishing this striking resemblance between transformer architecture and hippocampal spatial representation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine learning approach to identity resolution for criminal profiling

引用

Journal of Cyber Security Technology 2025年

作者： Kazemian, Hassan Shrestha, Subeksha AI and Data Science Research Group School of Computing and Digital Media London Metropolitan University London United Kingdom

A common dilemma when working on criminal data is that often people manipulate their details to disguise themselves and hide their identities which leads to creating ambiguous and false identities. Deep Neural Network (DNN) is applied to work well on fraudulent and imbalanced data. Two subcategories of DNN, the Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) with Python libraries such as TensorFlow and Keras are applied to the dataset for the detection of suspects with false identities to assist the process of analytical investigation by law enforcement agencies. Upon application of this approach to the anonymized policing dataset from SPIRIT Project funded by European Union’s Horizon, 5 main suspects with false identities were identified out of 23 targets on 39 million records. As working on criminal data is quite sensitive so to avoid data leakage while training and testing the data, K-fold validation has been applied. Furthermore, cascading both MLP and LSTM models on the policing dataset resulted in improved model prediction and accuracy compared to using each model individually. The cascaded model notably reduced false predictions for the recurring criminal patterns such as age-specific trends and most prevalent crime activities. © 2025 Informa UK Limited, trading as Taylor & Francis group.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

dataset of Quotation Attribution in German News Articles

arXiv

引用

arXiv 2024年

作者： Petersen-Frey, Fynn Biemann, Chris House of Computing and Data Science Language Technology Group Universität Hamburg Germany

Extracting who says what to whom is a crucial part in analyzing human communication in today’s abundance of data such as online news articles. Yet, the lack of annotated data for this task in German news articles severely limits the quality and usability of possible systems. To remedy this, we present a new, freely available, creative-commons-licensed dataset for quotation attribution in German news articles based on WIKINEWS. The dataset provides curated, high-quality annotations across 1000 documents (250,000 tokens) in a fine-grained annotation schema enabling various downstream uses for the dataset. The annotations not only specify who said what but also how, in which context, to whom and define the type of quotation. We specify our annotation schema, describe the creation of the dataset and provide a quantitative analysis. Further, we describe suitable evaluation metrics, apply two existing systems for quotation attribution, discuss their results to evaluate the utility of our dataset and outline use cases of our dataset in downstream tasks. © 2024, CC BY-NC-ND.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Continual Learning With Embedding Layer Surgery and Task-Wise Beam Search Using Whisper

Continual Learning With Embedding Layer Surgery and Task-Wis...

引用

IEEE Spoken Language Technology Workshop

作者： Chin Yuen Kwok Jia Qi Yip Eng Siong Chng College of Computing and Data Science Nanyang Technological University Singapore Alibaba Group Singapore

ISBN: (数字)9798350392258

ISBN: (纸本)9798350392265

关键词： Continuing education Adaptation models Emotion recognition Conferences Surgery Speech recognition Multilingual Decoding

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：