pwc-archive/papers-with-abstracts huggingface dataset:
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering
Authors: Ricky Fajri, Akrati Saxena, Yulong Pei, Mykola Pechenizkiy
Date: 2022-09-21 00:00:00
Tasks: Active Learning, Clustering, Fairness

Active Learning (AL) techniques have proven to be highly effective in reducing data labeling costs across a range of machine learning tasks. Nevertheless, one known challenge of these methods is their potential to introduce unfairness towards sensitive attributes. Although recent approaches have focused on enhancing fairness in AL, they tend to reduce the model's accuracy. To address this issue, we propose a novel strategy, named Fair Active Learning using fair Clustering, Uncertainty, and Representativeness (FAL-CUR), to improve fairness in AL. FAL-CUR tackles the fairness problem in AL by combining fair clustering with an acquisition function that determines which samples to query based on their uncertainty and representativeness scores. We evaluate the performance of FAL-CUR on four real-world datasets, and the results demonstrate that FAL-CUR achieves a 15% - 20% improvement in fairness compared to the best state-of-the-art method in terms of equalized odds while maintaining stable accuracy scores. Furthermore, an ablation study highlights the crucial roles of fair clustering in preserving fairness and the acquisition function in stabilizing the accuracy performance.
Factual and Informative Review Generation for Explainable Recommendation
Authors: Zhouhang Xie, Sameer Singh, Julian McAuley, Bodhisattwa Prasad Majumder
Date: 2022-09-12 00:00:00
Tasks: Explainable Recommendation, Review Generation

Recent models can generate fluent and grammatical synthetic reviews while accurately predicting user ratings. The generated reviews, expressing users' estimated opinions towards related products, are often viewed as natural language 'rationales' for the jointly predicted rating. However, previous studies found that existing models often generate repetitive, universally applicable, and generic explanations, resulting in uninformative rationales. Further, our analysis shows that previous models' generated content often contain factual hallucinations. These issues call for novel solutions that could generate both informative and factually grounded explanations. Inspired by recent success in using retrieved content in addition to parametric knowledge for generation, we propose to augment the generator with a personalized retriever, where the retriever's output serves as external knowledge for enhancing the generator. Experiments on Yelp, TripAdvisor, and Amazon Movie Reviews dataset show our model could generate explanations that more reliably entail existing reviews, are more diverse, and are rated more informative by human evaluators.
Identifying epidemic related Tweets using noisy learning
Authors: Ramya Tekumalla, Juan M. Banda
Date: 2022-09-10 00:00:00

Supervised learning algorithms are heavily reliant on annotated datasets to train machine learning models. However, the curation of the annotated datasets is laborious and time consuming due to the manual effort involved and has become a huge bottleneck in supervised learning. In this work, we apply the theory of noisy learning to generate weak supervision signals instead of manual annotation. We curate a noisy labeled dataset using a labeling heuristic to identify epidemic related tweets. We evaluated the performance using a large epidemic corpus and our results demonstrate that models trained with noisy data in a class imbalanced and multi-classification weak supervision setting achieved performance greater than 90%.
T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition
Authors: Asahi Ushio, Jose Camacho-Collados
Date: 2022-09-09 00:00:00
Proceeding: EACL 2021 2
Tasks: All, Domain Generalization, Face Model, Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross-lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine-tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.
Subsampling for Knowledge Graph Embedding Explained
Authors: Hidetaka Kamigaito, Katsuhiko Hayashi
Date: 2022-09-13 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding

In this article, we explain the recent advance of subsampling methods in knowledge graph embedding (KGE) starting from the original one used in word2vec.
Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data
Authors: Kazi Nabiul Alam, Md Shakib Khan, Abdur Rab Dhruba, Mohammad Monirujjaman Khan, Jehad F. Al-Amri, Mehedi Masud, Majdi Rawashdeh
Date: 2022-08-26 00:00:00
Tasks: Sentiment Analysis

This COVID-19 pandemic is so dreadful that it leads to severe anxiety, phobias, and complicated feelings or emotions. Even after vaccination against Coronavirus has been initiated, people feelings have become more diverse and complex, and our goal is to understand and unravel their sentiments in this research using some Deep Learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of it, specifically Twitter, one can have a better idea of what is trending and what is going on in people minds. Our motivation for this research is to understand the sentiment of people regarding the vaccination process, and their diverse thoughts regarding this. In this research, the timeline of the collected tweets was from December 21 to July 21, and contained tweets about the most common vaccines available recently from all across the world. The sentiments of people regarding vaccines of all sorts were assessed by using a Natural Language Processing (NLP) tool named Valence Aware Dictionary for sEntiment Reasoner (VADER). By initializing the sentiment polarities into 3 groups (positive, negative and neutral), the overall scenario was visualized here and our findings came out as 33.96% positive, 17.55% negative and 48.49% neutral responses. Recurrent Neural Network (RNN) oriented architecture such as Long Short-Term Memory (LSTM and Bi-LSTM) is used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving an accuracy of 90.83%. Other performance metrics such as Precision, Recall, F-1 score, and Confusion matrix were also shown to validate our models and findings more effectively. This study will help everyone understand public opinion on the COVID-19 vaccines and impact the aim of eradicating the Coronavirus from our beautiful world.
Evaluation of Question Answering Systems: Complexity of judging a natural language
Authors: Amer Farea, Zhen Yang, Kien Duong, Nadeesha Perera, Frank Emmert-Streib
Date: 2022-09-10 00:00:00
Tasks: Question Answering, Word Embeddings

Question answering (QA) systems are among the most important and rapidly developing research topics in natural language processing (NLP). A reason, therefore, is that a QA system allows humans to interact more naturally with a machine, e.g., via a virtual assistant or search engine. In the last decades, many QA systems have been proposed to address the requirements of different question-answering tasks. Furthermore, many error scores have been introduced, e.g., based on n-gram matching, word embeddings, or contextual embeddings to measure the performance of a QA system. This survey attempts to provide a systematic overview of the general framework of QA, QA paradigms, benchmark datasets, and assessment techniques for a quantitative evaluation of QA systems. The latter is particularly important because not only is the construction of a QA system complex but also its evaluation. We hypothesize that a reason, therefore, is that the quantitative formalization of human judgment is an open problem.
Predicting Customer Lifetime Value in Free-to-Play Games
Authors: Paolo Burelli
Date: 2022-09-06 00:00:00
Tasks: Game Design

As game companies increasingly embrace a service-oriented business model, the need for predictive models of players becomes more pressing. Multiple activities, such as user acquisition, live game operations or game design need to be supported with information about the choices made by the players and the choices they could make in the future. This is especially true in the context of free-to-play games, where the absence of a pay wall and the erratic nature of the players' playing and spending behavior make predictions about the revenue and allocation of budget and resources extremely challenging. In this chapter we will present an overview of customer lifetime value modeling across different fields, we will introduce the challenges specific to free-to-play games across different platforms and genres and we will discuss the state-of-the-art solutions with practical examples and references to existing implementations.
Cognitive Architecture for Co-Evolutionary Hybrid Intelligence
Authors: Kirill Krinkin, Yulia Shichkina
Date: 2022-09-05 00:00:00

This paper questions the feasibility of a strong (general) data-centric artificial intelligence (AI). The disadvantages of this type of intelligence are discussed. As an alternative, the concept of co-evolutionary hybrid intelligence is proposed. It is based on the cognitive interoperability of man and machine. An analysis of existing approaches to the construction of cognitive architectures is given. An architecture seamlessly incorporates a human into the loop of intelligent problem solving is considered. The article is organized as follows. The first part contains a critique of data-centric intelligent systems. The reasons why it is impossible to create a strong artificial intelligence based on this type of intelligence are indicated. The second part briefly presents the concept of co-evolutionary hybrid intelligence and shows its advantages. The third part gives an overview and analysis of existing cognitive architectures. It is concluded that many do not consider humans part of the intelligent data processing process. The next part discusses the cognitive architecture for co-evolutionary hybrid intelligence, providing integration with humans. It finishes with general conclusions about the feasibility of developing intelligent systems with humans in the problem-solving loop.
A Novel Method for Adaptive SAR Barrage Jamming Suppression
Authors: FengZhou, GuangcaiSun, XueruBai, ZhengBao.
Date: 2012-03-02 00:00:00
Proceeding: journal(IEEE GEOSCIENCE AND REMOTE SENSING LETTERS) 2012 3

Based on the difference in statistical distribution
between the target and jamming signal in the synthetic aperture
radar (SAR) image, this letter proposes a novel adaptive method
for barrage jamming suppression. In this method, the covariance
matrix of jamming is estimated from the SAR image. Then, the
2-D sinc function of the ideal point target is utilized as the steering
vector to obtain the optimal adaptive filter. This filter can sup-
press the random barrage jamming effectively, thus improving
the image contrast and interpretability. Additionally, this letter
analyzes in detail the theoretical basis and performance of the pro-
posed method. Finally, simulations are provided to demonstrate its
effectiveness
Current-Limiting Droop Control Design and Stability Analysis for Paralleled Boost Converters in DC Microgrids
Authors: Braitor, Konstantopoulos, Kadirkamanathan
Date: 2020-01-03 00:00:00
Proceeding: journal 2020 1

In this brief, a novel current-limiting droop controller for paralleled dc-dc boost converters loaded by constant impedance Z, constant current I, or constant power P loads in a dc microgrid is proposed to guarantee closed-loop stability and power sharing. Using an improved version of the recently proposed nonlinear current-limiting controller, an inherent current-limiting property is guaranteed for each converter independently of the load type or magnitude variations. Then, sufficient conditions to ensure closed-loop stability for the entire dc microgrid system with a constant Z, I, or P load are analytically obtained. Hence, compared with existing droop control methodologies, the proposed controller ensures accurate power sharing, tight voltage regulation, and closed-loop stability with a current limitation when connected to Z, I, or P load, for multiple paralleled boost converters, which introduce nonlinear dynamics. To verify the effectiveness of the proposed controller and the stability analysis, simulation results for the three parallel operated dc-dc boost converters with Z, I, and P loads and experimental results for two parallel operated dc-dc boost converters with a P load are performed under several changes of the load power demand.
LungRN+NL: An Improved Adventitious Lung Sound Classification Using Non-Local Block ResNet Neural Network with Mixup Data Augmentation
Authors: Yi Ma, Xinzi Xu, Yongfu Li
Date: 2020-08-01 00:00:00
Proceeding: Interspeech 2020 8
Tasks: Audio Classification, Data Augmentation, Lung Sound Classification, Sound Classification

Performing an automated adventitious lung sound detection is a challenging task since the sound is susceptible to noises (heart-beat, motion artifacts, and audio sound) and there is subtle discrimination among different categories. An adventitious lung sound classification model, LungRN+NL, is proposed in this work, which has demonstrated a drastic improvement compared to our previous work and the state-of-the-art models. This new model has incorporated the non-local block in the ResNet architecture. To address the imbalance problem and to improve the robustness of the model, we have also incorporated the mixup method to augment the training dataset. Our model has been implemented and compared with the state-of-the-art works using the official ICBHI 2017 challenge dataset and their evaluation method. As a result, LungRN+NL has achieved a performance score of 52.26%, which is improved by 2.1-12.7% compared to the state-of-the-art models.
ARSC-Net: Adventitious Respiratory Sound Classification Network Using Parallel Paths with Channel-Spatial Attention
Authors: Lei Xu, Jianhong Cheng, Jin Liu, Hulin Kuang, Fan Wu, Jianxin Wang
Date: 2022-01-14 00:00:00
Proceeding: IEEE International Conference on Bioinformatics and Biomedicine 2022 1
Tasks: Audio Classification, Classification, Sound Classification

Automatic identification of adventitious respiratory sound has still been a challenging problem in recent years. To address this challenge, we propose an adventitious respiratory sound classification network (ARSC-Net), which combines residual block with channel-spatial attention for accurate classification. Specifically, we extract two types of features from adventitious respiratory sound, including Mel-Frequency Cepstral Coefficients (MFCCs) and Mel-spectrogram. The two types of features are entered into the parallel encoders paths with residual attention for extracting feature representation, and then fused into a channel-spatial attention module to adaptively focus on the important features between channel and spatial part for the classification task. Moreover, the channel-spatial attention can enhance the feature representation, in which the channel attention explores the inter-channel relationship of the spectrums, and then the inter-spatial correlation mapping is generated by the spatial attention introduced serially. We evaluate our proposed method on ICBHI 2017 database. Experimental results show that our proposed method achieves encouraging predictive performance with an accuracy of 80.0% for identifying abnormal sounds from normal sounds, and with an accuracy of 92.4% for distinguishing crackles from wheezes. In addition, our method also achieves a score of 56.76% for the four-class sound classification of adventitious sounds and outperforms several state-of-the-art methods.
End-to-end neural networks for subvocal speech recognition
Authors: Pol Rosello, Pamela Toman, Nipun Agarwala
Date: 2017-06-11 00:00:00
Proceeding: CS 224S 2017 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Electromyography (EMG), speech-recognition, Speech Recognition

Subvocalization is a phenomenon observed while subjects read or think, characterized by involuntary facial and laryngeal muscle movements. By measuring this muscle activity using surface electromyography (EMG), it may be possible to perform automatic speech recognition (ASR) and enable silent, handsfree human-computer interfaces. In our work, we describe the first approach toward end-to-end, session-independent subvocal speech recognition by leveraging character-level recurrent neural networks (RNNs) and the connectionist temporal classification loss (CTC). We attempt to address challenges posed by a lack of data, including poor generalization, through data augmentation of electromyographic signals, a specialized multi-modal architecture, and regularization. We show results indicating reasonable qualitative performance on test set utterances, and describe promising avenues for future work in this direction.
Liquid Structural State-Space Models
Authors: Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, Daniela Rus
Date: 2022-09-26 00:00:00
Tasks: Heart rate estimation, Long-range modeling, Speech Recognition, SpO2 estimation, State Space Models, Time Series Analysis

A proper parametrization of state transition matrices of linear state-space models (SSMs) followed by standard nonlinearities enables them to efficiently learn representations from sequential data, establishing the state-of-the-art on a large series of long-range sequence modeling benchmarks. In this paper, we show that we can improve further when the structural SSM such as S4 is given by a linear liquid time-constant (LTC) state-space model. LTC neural networks are causal continuous-time neural networks with an input-dependent state transition module, which makes them learn to adapt to incoming inputs at inference. We show that by using a diagonal plus low-rank decomposition of the state transition matrix introduced in S4, and a few simplifications, the LTC-based structural state-space model, dubbed Liquid-S4, achieves the new state-of-the-art generalization across sequence modeling tasks with long-term dependencies such as image, text, audio, and medical time-series, with an average performance of 87.32% on the Long-Range Arena benchmark. On the full raw Speech Command recognition, dataset Liquid-S4 achieves 96.78% accuracy with a 30% reduction in parameter counts compared to S4. The additional gain in performance is the direct result of the Liquid-S4's kernel structure that takes into account the similarities of the input sequence samples during training and inference.
End-to-End Affordance Learning for Robotic Manipulation
Authors: Yiran Geng, Boshi An, Haoran Geng, Yuanpei Chen, Yaodong Yang, Hao Dong
Date: 2022-09-26 00:00:00
Tasks: Reinforcement Learning (RL)

Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can generalize over objects with different semantic categories, diverse shape geometry and versatile functionality. Recently, the technique of visual affordance has shown great prospects in providing object-centric information priors with effective actionable semantics. As such, an effective policy can be trained to open a door by knowing how to exert force on the handle. However, to learn the affordance, it often requires human-defined action primitives, which limits the range of applicable tasks. In this study, we take advantage of visual affordance by using the contact information generated during the RL training process to predict contact maps of interest. Such contact prediction process then leads to an end-to-end affordance learning framework that can generalize over different types of manipulation tasks. Surprisingly, the effectiveness of such framework holds even under the multi-stage and the multi-agent scenarios. We tested our method on eight types of manipulation tasks. Results showed that our methods outperform baseline algorithms, including visual-based affordance methods and RL methods, by a large margin on the success rate. The demonstration can be found at https://sites.google.com/view/rlafford/.
Simultaneous Acquisition of High Quality RGB Image and Polarization Information using a Sparse Polarization Sensor
Authors: Teppei Kurita, Yuhi Kondo, Legong Sun, Yusuke Moriuchi
Date: 2022-09-27 00:00:00

This paper proposes a novel polarization sensor structure and network architecture to obtain a high-quality RGB image and polarization information. Conventional polarization sensors can simultaneously acquire RGB images and polarization information, but the polarizers on the sensor degrade the quality of the RGB images. There is a trade-off between the quality of the RGB image and polarization information as fewer polarization pixels reduce the degradation of the RGB image but decrease the resolution of polarization information. Therefore, we propose an approach that resolves the trade-off by sparsely arranging polarization pixels on the sensor and compensating for low-resolution polarization information with higher resolution using the RGB image as a guide. Our proposed network architecture consists of an RGB image refinement network and a polarization information compensation network. We confirmed the superiority of our proposed network in compensating the differential component of polarization intensity by comparing its performance with state-of-the-art methods for similar tasks: depth completion. Furthermore, we confirmed that our approach could simultaneously acquire higher quality RGB images and polarization information than conventional polarization sensors, resolving the trade-off between the quality of RGB images and polarization information. The baseline code and newly generated real and synthetic large-scale polarization image datasets are available for further research and development.
Continuous approximation by convolutional neural networks with a sigmoidal function
Authors: Weike Chang
Date: 2022-09-27 00:00:00

In this paper we present a class of convolutional neural networks (CNNs) called non-overlapping CNNs in the study of approximation capabilities of CNNs. We prove that such networks with sigmoidal activation function are capable of approximating arbitrary continuous function defined on compact input sets with any desired degree of accuracy. This result extends existing results where only multilayer feedforward networks are a class of approximators. Evaluations elucidate the accuracy and efficiency of our result and indicate that the proposed non-overlapping CNNs are less sensitive to noise.