pwc-archive/papers-with-abstracts huggingface dataset:
Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages
Authors: Silvia García-Méndez, Francisco de Arriba-Pérez, Ana Barros-Vila, Francisco J. González-Castaño
Date: 2024-03-30 00:00:00
Tasks: Constituency Parsing, Emotion Recognition

Microblogging platforms, of which Twitter is a representative example, are valuable information sources for market screening and financial models. In them, users voluntarily provide relevant information, including educated knowledge on investments, reacting to the state of the stock markets in real-time and, often, influencing this state. We are interested in the user forecasts in financial, social media messages expressing opportunities and precautions about assets. We propose a novel Targeted Aspect-Based Emotion Analysis (TABEA) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet (instead of making an overall guess about that whole tweet). It is based on Natural Language Processing (NLP) techniques and Machine Learning streaming algorithms. The system comprises a constituency parsing module for parsing the tweets and splitting them into simpler declarative clauses; an offline data processing module to engineer textual, numerical and categorical features and analyse and select them based on their relevance; and a stream classification module to continuously process tweets on-the-fly. Experimental results on a labelled data set endorse our solution. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter. To the best of our knowledge, no prior work in the literature has addressed this problem despite its practical interest in decision-making, and we are not aware of any previous NLP nor online Machine Learning approaches to TABEA.
Identifying Banking Transaction Descriptions via Support Vector Machine Short-Text Classification Based on a Specialized Labelled Corpus
Authors: Silvia García-Méndez, Milagros Fernández-Gavilanes, Jonathan Juncal-Martínez, Francisco J. González-Castaño, Oscar Barba Seara
Date: 2024-03-29 00:00:00
Tasks: Management, Spam detection, text-classification, Text Classification, text similarity

Short texts are omnipresent in real-time news, social network commentaries, etc. Traditional text representation methods have been successfully applied to self-contained documents of medium size. However, information in short texts is often insufficient, due, for example, to the use of mnemonics, which makes them hard to classify. Therefore, the particularities of specific domains must be exploited. In this article we describe a novel system that combines Natural Language Processing techniques with Machine Learning algorithms to classify banking transaction descriptions for personal finance management, a problem that was not previously considered in the literature. We trained and tested that system on a labelled dataset with real customer transactions that will be available to other researchers on request. Motivated by existing solutions in spam detection, we also propose a short text similarity detector to reduce training set size based on the Jaccard distance. Experimental results with a two-stage classifier combining this detector with a SVM indicate a high accuracy in comparison with alternative approaches, taking into account complexity and computing time. Finally, we present a use case with a personal finance application, CoinScrap, which is available at Google Play and App Store.
FewUser: Few-Shot Social User Geolocation via Contrastive Learning
Authors: Menglin Li, Kwan Hui Lim
Date: 2024-03-28 00:00:00
Tasks: Contrastive Learning, Language Modelling

To address the challenges of scarcity in geotagged data for social user geolocation, we propose FewUser, a novel framework for Few-shot social User geolocation. We incorporate a contrastive learning strategy between users and locations to improve geolocation performance with no or limited training data. FewUser features a user representation module that harnesses a pre-trained language model (PLM) and a user encoder to process and fuse diverse social media inputs effectively. To bridge the gap between PLM's knowledge and geographical data, we introduce a geographical prompting module with hard, soft, and semi-soft prompts, to enhance the encoding of location information. Contrastive learning is implemented through a contrastive loss and a matching loss, complemented by a hard negative mining strategy to refine the learning process. We construct two datasets TwiU and FliU, containing richer metadata than existing benchmarks, to evaluate FewUser and the extensive experiments demonstrate that FewUser significantly outperforms state-of-the-art methods in both zero-shot and various few-shot settings, achieving absolute improvements of 26.95\% and \textbf{41.62\%} on TwiU and FliU, respectively, with only one training sample per class. We further conduct a comprehensive analysis to investigate the impact of user representation on geolocation performance and the effectiveness of FewUser's components, offering valuable insights for future research in this area.
How Does Message Passing Improve Collaborative Filtering?
Authors: Mingxuan Ju, William Shiao, Zhichun Guo, Yanfang Ye, Yozen Liu, Neil Shah, Tong Zhao
Date: 2024-03-27 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, TAG

Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. A branch of research enhances CF methods by message passing used in graph neural networks, due to its strong capabilities of extracting knowledge from graph-structured data, like user-item bipartite graphs that naturally exist in CF. They assume that message passing helps CF methods in a manner akin to its benefits for graph-based learning tasks in general. However, even though message passing empirically improves CF, whether or not this assumption is correct still needs verification. To address this gap, we formally investigate why message passing helps CF from multiple perspectives and show that many assumptions made by previous works are not entirely accurate. With our curated ablation studies and theoretical analyses, we discover that (1) message passing improves the CF performance primarily by additional representations passed from neighbors during the forward pass instead of additional gradient updates to neighbor representations during the model back-propagation and (ii) message passing usually helps low-degree nodes more than high-degree nodes. Utilizing these novel findings, we present Test-time Aggregation for CF, namely TAG-CF, a test-time augmentation framework that only conducts message passing once at inference time. The key novelty of TAG-CF is that it effectively utilizes graph knowledge while circumventing most of notorious computational overheads of message passing. Besides, TAG-CF is extremely versatile can be used as a plug-and-play module to enhance representations trained by different CF supervision signals. Evaluated on six datasets, TAG-CF consistently improves the recommendation performance of CF methods without graph by up to 39.2% on cold users and 31.7% on all users, with little to no extra computational overheads.
Fusion of a machine learning approach and classical orbit predictions
Authors: Hao Peng, Xiaoli Bai
Date: 2021-04-12 00:00:00
Proceeding: Acta Astronautica 2021 4
Tasks: Prediction

Orbit prediction accuracy is often limited by the underlying physics-based models and estimation methods.
Instead of advancing the state-of-the-art from those two aspects, we have recently developed a physics-based
machine learning (ML) methodology to discover useful information from historical orbit predictions errors.
This paper is posed to answer the next question: how can we fuse the ML approach with classical orbit
predictions? The classical method provides valuable and reliable information and should not be abandoned.
Using the extended Kalman filter (EKF) as the orbit estimation and prediction method, this paper presents an
innovative fusion strategy to incorporate the ML output to the conventional framework. We define the pseudophysical meaning of the ML output and derive an analytical model for fusion. Using a simulation-based space
catalog environment, the paper demonstrates that the proposed fusion strategy can improve both the orbit
prediction accuracy and precision. Discussions and insights are presented including the possible causes for
some unsatisfying results. Although EKF is used in this paper, the design of the fusion strategy for the ML
approach can be generalized to systems with different orbit estimation and prediction methods.
Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
Authors: Michael Beukman, Devon Jarvis, Richard Klein, Steven James, Benjamin Rosman
Date: 2023-10-25 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: reinforcement-learning, Reinforcement Learning

While reinforcement learning has achieved remarkable successes in several domains, its real-world application is limited due to many methods failing to generalise to unfamiliar conditions. In this work, we consider the problem of generalising to new transition dynamics, corresponding to cases in which the environment's response to the agent's actions differs. For example, the gravitational force exerted on a robot depends on its mass and changes the robot's mobility. Consequently, in such cases, it is necessary to condition an agent's actions on extrinsic state information and pertinent contextual information reflecting how the environment responds. While the need for context-sensitive policies has been established, the manner in which context is incorporated architecturally has received less attention. Thus, in this work, we present an investigation into how context information should be incorporated into behaviour learning to improve generalisation. To this end, we introduce a neural network architecture, the Decision Adapter, which generates the weights of an adapter module and conditions the behaviour of an agent on the context information. We show that the Decision Adapter is a useful generalisation of a previously proposed architecture and empirically demonstrate that it results in superior generalisation performance compared to previous approaches in several environments. Beyond this, the Decision Adapter is more robust to irrelevant distractor variables than several alternative methods.
HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification
Authors: Judy X Yang, Jun Zhou, Jing Wang, Hui Tian, Alan Wee Chung Liew
Date: 2024-03-30 00:00:00

Classifying hyperspectral images is a difficult task in remote sensing, due to their complex high-dimensional data. To address this challenge, we propose HSIMamba, a novel framework that uses bidirectional reversed convolutional neural network pathways to extract spectral features more efficiently. Additionally, it incorporates a specialized block for spatial analysis. Our approach combines the operational efficiency of CNNs with the dynamic feature extraction capability of attention mechanisms found in Transformers. However, it avoids the associated high computational demands. HSIMamba is designed to process data bidirectionally, significantly enhancing the extraction of spectral features and integrating them with spatial information for comprehensive analysis. This approach improves classification accuracy beyond current benchmarks and addresses computational inefficiencies encountered with advanced models like Transformers. HSIMamba were tested against three widely recognized datasets Houston 2013, Indian Pines, and Pavia University and demonstrated exceptional performance, surpassing existing state-of-the-art models in HSI classification. This method highlights the methodological innovation of HSIMamba and its practical implications, which are particularly valuable in contexts where computational resources are limited. HSIMamba redefines the standards of efficiency and accuracy in HSI classification, thereby enhancing the capabilities of remote sensing applications. Hyperspectral imaging has become a crucial tool for environmental surveillance, agriculture, and other critical areas that require detailed analysis of the Earth surface. Please see our code in HSIMamba for more details.
Structured State Space Models for In-Context Reinforcement Learning
Authors: Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob Foerster, Satinder Singh, Feryal Behbahani
Date: 2023-03-07 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: continuous-control, Continuous Control, In-Context Reinforcement Learning, Meta-Learning, reinforcement-learning, Reinforcement Learning, State Space Models

Structured state space sequence (S4) models have recently achieved state-of-the-art performance on long-range sequence modeling tasks. These models also have fast inference speeds and parallelisable training, making them potentially useful in many reinforcement learning settings. We propose a modification to a variant of S4 that enables us to initialise and reset the hidden state in parallel, allowing us to tackle reinforcement learning tasks. We show that our modified architecture runs asymptotically faster than Transformers in sequence length and performs better than RNN's on a simple memory-based task. We evaluate our modified architecture on a set of partially-observable environments and find that, in practice, our model outperforms RNN's while also running over five times faster. Then, by leveraging the model's ability to handle long-range sequences, we achieve strong performance on a challenging meta-learning task in which the agent is given a randomly-sampled continuous control environment, combined with a randomly-sampled linear projection of the environment's observations and actions. Furthermore, we show the resulting model can adapt to out-of-distribution held-out tasks. Overall, the results presented in this paper show that structured state space models are fast and performant for in-context reinforcement learning tasks. We provide code at https://github.com/luchris429/popjaxrl.
No More Ambiguity in 360° Room Layout via Bi-Layout Estimation
Authors: Yu-Ju Tsai, Jin-Cheng Jhang, Jingjing Zheng, Wei Wang, Albert Y. C. Chen, Min Sun, Cheng-Hao Kuo, Ming-Hsuan Yang
Date: 2024-04-15 00:00:00
Tasks: Room Layout Estimation

Inherent ambiguity in layout annotations poses significant challenges to developing accurate 360{\deg} room layout estimation models. To address this issue, we propose a novel Bi-Layout model capable of predicting two distinct layout types. One stops at ambiguous regions, while the other extends to encompass all visible areas. Our model employs two global context embeddings, where each embedding is designed to capture specific contextual information for each layout type. With our novel feature guidance module, the image feature retrieves relevant context from these embeddings, generating layout-aware features for precise bi-layout predictions. A unique property of our Bi-Layout model is its ability to inherently detect ambiguous regions by comparing the two predictions. To circumvent the need for manual correction of ambiguous annotations during testing, we also introduce a new metric for disambiguating ground truth layouts. Our method demonstrates superior performance on benchmark datasets, notably outperforming leading approaches. Specifically, on the MatterportLayout dataset, it improves 3DIoU from 81.70% to 82.57% across the full test set and notably from 54.80% to 59.97% in subsets with significant ambiguity. Project page: https://liagm.github.io/Bi_Layout/
Comprehensive Library of Variational LSE Solvers
Authors: Nico Meyer, Martin Röhn, Jakob Murauer, Axel Plinge, Christopher Mutschler, Daniel D. Scherer
Date: 2024-04-15 00:00:00

Linear systems of equations can be found in various mathematical domains, as well as in the field of machine learning. By employing noisy intermediate-scale quantum devices, variational solvers promise to accelerate finding solutions for large systems. Although there is a wealth of theoretical research on these algorithms, only fragmentary implementations exist. To fill this gap, we have developed the variational-lse-solver framework, which realizes existing approaches in literature, and introduces several enhancements. The user-friendly interface is designed for researchers that work at the abstraction level of identifying and developing end-to-end applications.
Parsing All Adverse Scenes: Severity-Aware Semantic Segmentation with Mask-Enhanced Cross-Domain Consistency
Authors: Fuhao Li, Ziyang Gong, Yupeng Deng, Xianzheng Ma4, Renrui Zhang, Zhenming Ji, Xiangwei Zhu, Hong Zhang
Date: 2024-03-28 00:00:00
Proceeding: AAAI 2024 3
Tasks: All, Domain Adaptation, Semantic Segmentation, SSIM, Unsupervised Domain Adaptation

Although recent methods in Unsupervised Domain Adaptation (UDA) have achieved success in segmenting rainy or snowy scenes by improving consistency, they face limitations when dealing with more challenging scenarios like foggy and night scenes. We argue that these prior methods excessively focus on weather-specific features in adverse scenes, which exacerbates the existing domain gaps. To address this issue, we propose a new metric to evaluate the severity of all adverse scenes and offer a novel perspective that enables task unification across all adverse scenarios. Our method focuses on Severity, allowing our model to learn more consistent features and facilitate domain distribution alignment, thereby alleviating domain gaps. Unlike the vague descriptions of consistency in previous methods, we introduce Cross-domain Consistency, which is quantified using the Structure Similarity Index Measure (SSIM) to measure the distance between the source and target domains. Specifically, our unified model consists of two key modules: the Merging Style Augmentation Module (MSA) and the Severity Perception Mask Module (SPM). The MSA module transforms all adverse scenes into augmented scenes, effectively eliminating weather-specific features and enhancing Cross-domain Consistency. The SPM module incorporates a Severity Perception mechanism, guiding a Mask operation that enables our model to learn highly consistent features from the augmented scenes. Our unified framework, named PASS (Parsing All adverSe Scenes), achieves significant performance improvements over state-of-the-art methods on widely-used benchmarks for all adverse scenes. Notably, the performance of PASS is superior to Semi-Unified models and even surpasses weather-specific models.
Learning Task-Aware Language-Image Representation for Class-Incremental Object Detection
Authors: Bin-Bin Gao
Date: 2024-03-24 00:00:00
Proceeding: AAAI 2024 2024 3
Tasks: Class-Incremental Object Detection, object-detection, Object Detection

Class-incremental object detection (CIOD) is a real-world desired capability, requiring an object detector to continuously adapt to new tasks without forgetting learned ones, with the main challenge being catastrophic forgetting. Many methods based on distillation and replay have been proposed to alleviate this problem. However, they typically learn on a pure visual backbone, neglecting the powerful representation capabilities of textual cues, which to some extent limits their performance. In this paper, we propose task-aware language-image representation to mitigate catastrophic forgetting, introducing a new paradigm for language-image-based CIOD. First of all, we demonstrate the significant advantage of language-image detectors in mitigating catastrophic forgetting. Secondly, we propose a learning task-aware language-image representation method that overcomes the existing drawback of directly utilizing the language-image detector for CIOD. More specifically, we learn the language-image representation of different tasks through an insulating approach in the training stage, while using the alignment scores produced by task-specific language-image representation in the inference stage. Through our proposed method, language-image detectors can be more practical for CIOD. We conduct extensive experiments on COCO 2017 and Pascal VOC 2007 and demonstrate that the proposed method achieves state-of-the-art results under the various CIOD settings.
FSPEN: AN ULTRA-LIGHTWEIGHT NETWORK FOR REAL TIME SPEECH ENAHNCMENT
Authors: Lei Yang1, Wei Liu1, Ruijie Meng1, Gunwoo Lee2, Soonho Baek2, Han-gil Moon2
Date: 2024-04-15 00:00:00
Proceeding: Conference 2024 4
Tasks: Speech Enhancement

Deep learning-based speech enhancement methods have shown promising result in recent years. However, in practical applications, the model size and computational complexity are important factors that limit their use in end-products.
Therefore, in products that require real-time speech enhancement with limited resources, such as TWS headsets, hearing
aids, IoT devices, etc., ultra-lightweight models are necessary. In this paper, an ultra-lightweight network FSPEN is proposed for real-time speech enhancement task. We propose a full-band and sub-band network structure for extracting global and local features, and an inter-frame path extension method that can enhance network modeling capacity while preserving complexity. Experiments demonstrate that the proposed FSPEN achieves a performance of PESQ 2.97 on the VoiceBank+Demand dataset at 89M multiply-accumulate operation per second (MAC) and 79k parameters.
Congruent Learning for Self-Regulated Federated Learning in 6G
Authors: Jalil Taghia, Farnaz Moradi, Hannes Larsson, Xi Lan, Adam Orucu, Masoumeh Ebrahimi, Andreas Johnsson
Date: 2024-01-10 00:00:00
Proceeding: IEEE Transactions on Machine Learning in Communications and Networking 2024 1
Tasks: Federated Learning, Management, Privacy Preserving

Future 6G networks are expected to be AI-native with distributed machine learning functionalities responsible for improving and automating a variety of network- and service-management tasks. To enable a privacy-preserving approach to distributed learning, federated learning (FL) has become prevalent in the communication-and-networking domain. However, for efficient management of the networks, FL needs to be automated requiring minimal hyperparameter tuning. An outstanding challenge towards automation of FL is regarding difficulties in handling overfitting. Existing techniques tackle overfitting via regularization heuristics that rely on hyperparameter tuning and as such presume availability of representative validation data. However, in the dynamic and heterogeneous network environments, this assumption is limiting. Even if existence of validation data can be assumed, hyperparameter tuning comes with added communication and compute overhead cost which grows prohibitively as the federation scales in size. Here, we propose the congruent federated learning (CFL) as a self-regulated method of learning that is robust to overfitting and achieves the robustness without reliance on hyperparameter tuning. CFL employs a self-taught regularization mechanism that refrains local models from overfitting to the local data. This is enabled via introduction of the congruent activation functions as a class of similarity-promoting activation functions that discourage learning local models which differ excessively from the global (federated) model. Across four networking use cases on several tasks, reflecting different profiles of data heterogeneity and limited availability of data, it is shown that CFL greatly reduces overfitting and in nearly all cases improves the performance—a relative gain of about 21% averaged across all use cases.