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Unsupervised graph-clustering learning framework for financial news summarization 21

Unsupervised graph-clustering learning framework for financi...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Wang, Jun Tan, Jinghua Jin, Hanlei Qi, Shuo Southwestern Univ Finance & Econ Chengdu Peoples R China

ISBN: (纸本)9781665424271

Financial news shows significant influence on the inflection point of stock market. To condense the news texts with exponential growth, Automatic Text Summarization(ATS) becomes urgent. However, ATS tailored for financial news summarization has not been explored. In this paper, we propose a Graph-Clustering framework(FinGC) to extract financial news summarization. Our framework jointly learns the graph embedding and performs clustering in an unsupervised way. By combining graph structure which contains cross-sentence relations into text embedding, FinGC enriches the representation of financial news and highlights important sentences that point to specific events. Clustering is incorporated to group the financial news in terms of diversity and non-redundancy. We evaluated our best FinGC model on Fin-News and standard Multi-News. Experiments demonstrate it achieves state-of-the-art performance on standard datasets by ROUGE scores. Finally, we validated our results with human evaluation and show that our model achieves human performance on financial news.

关键词： Financial news Summarization Graph Clustering Joint learning

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Determining the Most Significant Metadata Features to Indicate Defective Software Commits

Determining the Most Significant Metadata Features to Indica...

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international conference on Software engineering Research, Management and Applications (SERA)

作者： Rupam Kumar Dey Anahita Khojandi Kalyan Perumalla Industrial & Systems Engineering The University of Tennessee Knoxville USA

Defects are largely inevitable in the software development life cycle. Since we cannot avoid them during the development process, we can only desire to fight back with our limited resources in terms of time and monetary investment. Like in many other fields, machine learning models can be of help to mitigate the problem of defects by predicting both bug frequency and defective modules at different granularity levels. However, machine learning models are as good as the quality of the pre-selected set of features under consideration. Therefore, importance must be given while selecting only the necessary features from the original set of features. In this study, we compared various machine learning models with varying feature selection techniques and found the superiority of random forest-based machine learning techniques with wrapper methods. Random forest-based models with the wrapper method were able to detect all the buggy classes successfully on the validation data set.

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20th international conference on Grid, Cloud, and Cluster Computing, GCC 2024, 3rd international conference on Quantum Technologies, ICEQT 2024, and 21st international conference on Modeling, Simulation and Visualization Methods, MSV 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

20th International Conference on Grid, Cloud, and Cluster Co...

引用

20th international conference on Grid, Cloud, and Cluster Computing, GCC 2024, 3rd international conference on Quantum Technologies, ICEQT 2024, and 21st international conference on Modeling, Simulation and Visualization Methods, MSV 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031858833

The proceedings contain 20 papers. The special focus in this conference is on Grid, Cloud, and Cluster Computing. The topics include: Scientific Workflow Provenance Management: System Requirements and a Reference Architecture;Enhancing data Security with Decentralized Cloud storage: An IPFS Approach;learning Robust Observable to Address Noise in Quantum Machine learning;evaluating Cost-Effective Reconfigurable Hardware for Quantum Simulation;an Efficient Quantum Solver for Multidimensional Partial Differential Equations;explaining the Design of the Quantum Fourier Transform;Optimizing Depth of Quantum Circuit for Generating GHZ states;a Comparative Analysis of Hybrid-Quantum Classical Neural Networks;NRQNN: The Role of Observable Selection in Noise-Resilient Quantum Neural Networks;studying the Impact of Quantum-Specific Hyperparameters on Hybrid Quantum-Classical Neural Networks;Eclipse Qrisp QAOA: Description and Preliminary Comparison with Qiskit Counterparts;federal Cloud Computing Adoption Case study: A Retrospective Analysis as a Precursor to Optimized Quantum Adoption Methodologies;integrating Secure Quantum Digital Signature into Quantum Communications;methodology to Accelerate Federal Agency Adoption of Quantum Technologies;understanding Public Policy Effects on Alcohol-Related Behaviors and Outcomes Using System Dynamics;Performance Investigation of Small UAV Attitude Control Based on Optimized Nonlinear Dynamic Inversion;Prediction of 1st Year Registration Renewal of General-Use JP Domain Names with the Use of Machine learning;data Clustering and Visualization with Recursive Max k-Cut Algorithm.

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Understanding the Resilience of Neural Network Ensembles against Faulty Training data 21

Understanding the Resilience of Neural Network Ensembles aga...

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21st IEEE international conference on Software Quality, Reliability and Security (QRS)

作者： Chan, Abraham Narayanan, Niranjhana Gujarati, Arpan Pattabiraman, Karthik Gopalakrishnan, Sathish Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781665458139

Machine learning is becoming more prevalent in safety-critical systems like autonomous vehicles and medical imaging. Faulty training data, where data is either mislabelled, missing, or duplicated, can increase the chance of misclassification, resulting in serious consequences. In this paper, we evaluate the resilience of ML ensembles against faulty training data, in order to understand how to build better ensembles. To support our evaluation, we develop a fault injection framework to systematically mutate training data, and introduce two diversity metrics that capture the distribution and entropy of predicted labels. Our experiments find that ensemble learning is more resilient than any individual model and that high accuracy neural networks are not necessarily more resilient to faulty training data. Further, we find that simple majority voting suffices in most cases for resilience in ML ensembles. Finally, we observe diminishing returns for resilience as we increase the number of models in an ensemble. These findings can help machine learning developers build ensembles that are both more resilient and more efficient.

关键词： Error resilience Machine learning Training

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Unsupervised Person Re-identification Based on Skeleton Joints Using Graph Convolutional Networks 21st

Unsupervised Person Re-identification Based on Skeleton Join...

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Khaldi, Khadija Mantini, Pranav Shah, Shishir K. Univ Houston Houston TX 77004 USA

ISBN: (纸本)9783031064333;9783031064326

With the remarkable progress of deep learning methods, person re-identification has received a lot of attention from researchers. However, the majority of previous work mainly focus on supervised learning setting, which requires expensive data annotations. In this paper, we address this problem by proposing a purely unsupervised learning model. Inspired by the effectiveness of modeling the spatio-temporal information of pedestrian video, we mine the relationships between human body joints. Specifically, we propose a novel framework by learning inter-frame and intra-frame relationships for discriminative feature learning via two Graph Convolutional Networks (GCN) modules: spatial and temporal. The spatial module captures the structural information of the human body and the temporal module propagates information across adjacent frames. At the end, we perform hierarchical clustering by selecting P identities and K instances (PK sampling) to generate pseudo-labels for the unlabeled data. By iteratively optimizing these modules, our model extracts robust spatial-temporal information that can alleviate the occlusion problem. We conduct experiments on two benchmarks: MARS and DukeMTMC-VideoReID datasets, where we demonstrate the effectiveness of our proposed method.

关键词： Person re-identification Unsupervised learning Graph neural network

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Review of: Deep learning (DL) for Beamforming Array (BFA) Antenna

Review of: Deep Learning (DL) for Beamforming Array (BFA) An...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Ali A. Naji Thamer M. Jamel Hassan F. Khazaal dept. of Electrical Engineering University of Was it Wasit Iraq dept. of communications engineering University of Technology Baghdad Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

In the realm of wireless communication, smart antennas are widely employed to augment system capacity, mitigate interference, and function with little complexity. The utilization of deep learning algorithms is of paramount importance in the reduction of computational complexity inside smart antenna systems. Beamforming techniques are employed by these algorithms to enhance signal transmission and reception by increasing signal power in desired directions and decreasing it in undesired directions. There are several directional antenna array techniques that utilize deep learning alaorithms in order to enhance performance. This work investigates the utilization of deep learning in the field of wireless communications, with a specific emphasis on beamforming techniques employed in smart antenna system design.

关键词： Deep learning Wireless communication Array signal processing Training data Interference data models Arrays

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Customer Segmentation Using Credit Card data Analysis

Customer Segmentation Using Credit Card Data Analysis

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international conference on Software engineering Research, Management and Applications (SERA)

作者： Saikat Raj Santanu Roy Surajit Jana Soumyadip Roy Takaaki Goto Soumya Sen Computer Science and Engineering (Data Science) Future Institute of Engineering and Management Kolkata India A.K. Choudhury School of Information Technology University of Calcutta Kolkata India Faculty of Information Sciences and Arts Toyo University Japan

Customer segmentation is a separation of a market into multiple distinct groups of consumers who share the similar characteristics. Segmentation of market is an effective way to define and meet Customer needs and also to identify the future business plan. Unsupervised machine learning algorithms are suitable to analyze and identify the possible set of customers when the labeled data about the customers are no available. In this research work the spending of different customers who have credit cards are analyzed to segment them into different clusters and also to plan further business improvements based on the different characteristics of these identified clusters.

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ENGINE: Enhancing Neuroimaging and Genetic Information by Neural Embedding 21

ENGINE: Enhancing Neuroimaging and Genetic Information by Ne...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Ko, Wonjun Jung, Wonsik Jeon, Eunjin Mulyadi, Ahmad Wisnu Suk, Heung-Il Korea Univ Dept Brain & Cognit Engn Anam Ro 145 Seoul 02841 South Korea Korea Univ Dept Artificial Intelligence Anam Ro 145 Seoul 02841 South Korea

ISBN: (纸本)9781665423984

Recently, deep learning, a branch of machine learning and data mining, has gained widespread acceptance in many applications thanks to its unprecedented successes. In this regard, pioneering studies employed deep learning frameworks for imaging genetics in virtue of their own representation caliber. But, existing approaches suffer from some limitations: (i) exploiting a simple concatenation strategy for joint analysis, (ii) a lack of extension to biomedical applications, and (iii) insufficient and inappropriate interpretations in the viewpoint of both data science and bio-neuroscience. In this work, we propose a novel deep learning framework to tackle the aforementioned issues simultaneously. Our proposed framework learns to effectively represent the neuroimaging and the genetic data jointly, and achieves state-of-the-art performance in its use for Alzheimer's disease and mild cognitive impairment identification. Further, unlike the existing methods in the literature, the framework allows learning the relation between imaging phenotypes and genotypes in a nonlinear way without any prior neuroscientific knowledge. To demonstrate the validity of our proposed framework, we conducted experiments on a publicly available dataset and analyzed the results from diverse perspectives. Based on our experimental results, we believe that the proposed framework has a great potential to give new insights and perspectives in deep learning-based imaging genetics studies.

关键词： data mining imaging genetics deep learning magnetic resonance imaging single nucleotide polymorphism

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Flexible, Robust, Scalable Semi-supervised learning via Reliability Propagation 21

Flexible, Robust, Scalable Semi-supervised Learning via Reli...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Huang, Chen Pan, Liangxu Yang, Qinli Wang, Hongliang Shao, Junming Univ Elect Sci & Technol China Data Ming Lab Chengdu Peoples R China SiChuan Univ Coll Comp Sci Chengdu Peoples R China Kth Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781665423984

Semi-supervised learning aims to generate a model with a better performance using plenty of unlabeled data. However, most existing methods treat unlabeled data equally without considering whether it is safe or not, which may lead to the degradation of prediction performance. In this paper, towards reliable semi-supervised learning, we propose a data-driven algorithm, called Reliability Propagation (RP), to learn the reliability of each unlabeled instance. The basic idea is to take local label regularity as a prior, and then perform reliability propagation on an adaptive graph. As a result, the most reliable unlabeled instances could be selected to construct a safer classifier. Beyond, the distributed RP algorithm is introduced to scale up to large volumes of data. In contrast to existing approaches, RP exploits the structural information and shed light on the soft instance selection for unlabeled data in a classifier-independent way. Experiments on both synthetic and real-world data have demonstrated that RP allows extracting most reliable unlabeled instances and supports a gained prediction performance compared to other algorithms.

关键词： Semi-supervised learning classification reliability propagation

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Performance improvement of multi-parameter measurement optical fiber sensors based on machine learning methods

Performance improvement of multi-parameter measurement optic...

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international conference on Optical Communications and Networks (ICOCN)

作者： Yi-Fan Zhou Bo Han Po Jin Ya-Nan Zhang Yong Zhao College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao China

A machine learning based spectral data analysis method is proposed to solve the problem of limited measurement range of optical fiber sensors. This method expands the measurement range of the sensor and improves measu...

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