pwc-archive/papers-with-abstracts huggingface dataset:
Reconciling the accuracy-diversity trade-off in recommendations
Authors: Kenny Peng, Manish Raghavan, Emma Pierson, Jon Kleinberg, Nikhil Garg
Date: 2023-07-27 00:00:00
Tasks: Diversity, Recommendation Systems

In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items representing a range of categories). As such, real-world recommender systems often explicitly incorporate diversity separately from accuracy. This approach, however, leaves a basic question unanswered: Why is there a trade-off in the first place? We show how the trade-off can be explained via a user's consumption constraints -- users typically only consume a few of the items they are recommended. In a stylized model we introduce, objectives that account for this constraint induce diverse recommendations, while objectives that do not account for this constraint induce homogeneous recommendations. This suggests that accuracy and diversity appear misaligned because standard accuracy metrics do not consider consumption constraints. Our model yields precise and interpretable characterizations of diversity in different settings, giving practical insights into the design of diverse recommendations.
Multi-animal pose estimation, identification and tracking with DeepLabCut
Authors: Jessy Lauer, Mu Zhou, Shaokai Ye, William Menegas, Steffen Schneider, Tanmay Nath, Mohammed Mostafizur Rahman, Valentina Di Santo, Daniel Soberanes, Guoping Feng, Venkatesh N. Murthy, George Lauder, Catherine Dulac, Mackenzie Weygandt Mathis & Alexander Mathis
Date: 2022-04-12 00:00:00
Proceeding: Nature Methods 2022 4
Tasks: Animal Pose Estimation, Pose Estimation

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking—features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal’s identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.
Attention mechanism-based generative adversarial networks for cloud removal in Landsat images
Authors: Meng Xu, Furong Deng, Sen Jia, Xiuping Jia, Antonio J. Plaza
Date: 2022-03-15 00:00:00
Proceeding: Remote Sensing of Environment 2022 3
Tasks: Cloud Removal

The existence of clouds affects the quality of optical remote sensing images. Cloud removal is an important preprocessing procedure to effectively improve the utilization of optical remote sensing images. Thin clouds partly obscure the land surfaces beneath them, making it possible to correct the cloudy scenes according to the available information. In this research, we introduce the attention mechanism-based generative adversarial networks for cloud removal (AMGAN-CR) method for Landsat images. First, attention maps of the input cloudy images are generated to extract the cloud distributions and features through an attentive recurrent network. Second, clouds are removed by an attentive residual network under the guidance of the attention maps. Finally, the generated feature maps are fed to a reconstruction network to restore the final cloud-free images. The networks are trained by cloudy and cloud-free Landsat image pairs, and the cloudy images are tested to validate the effectiveness of AMGAN-CR. Both simulated and real cloud experimental results show that the proposed method is more outstanding than the other five state-of-the-art traditional and deep learning methods in removing cloud.
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning
Authors: Mahdi Eskandari, Huiling Zhu, Arman Shojaeifard, Jiangzhou Wang
Date: 2022-09-03 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

The paper presents a joint beamforming algorithm using statistical channel state information (S-CSI) for reconfigurable intelligent surfaces (RIS) for multiuser MISO wireless communications. We used S-CSI, which is a long-term average of the cascaded channel as opposed to instantaneous CSI utilized in most existing works. Through this method, the overhead of channel estimation is dramatically reduced. We propose a proximal policy optimization (PPO) algorithm which is a well-known actor-critic based reinforcement learning (RL) algorithm to solve the optimization problem. To test the efficacy of this algorithm, simulation results are presented along with evaluations of key system parameters, including the Rician factor and RIS location, on the achievable sum rate of the users.
Writer adaptation for offline text recognition: An exploration of neural network-based methods
Authors: Tobias van der Werff, Maruf A. Dhali, Lambert Schomaker
Date: 2023-07-11 00:00:00
Tasks: Automatic Speech Recognition, Handwriting Recognition, Handwritten Text Recognition, HTR, Meta-Learning, Sentence, speech-recognition, Speech Recognition

Handwriting recognition has seen significant success with the use of deep learning. However, a persistent shortcoming of neural networks is that they are not well-equipped to deal with shifting data distributions. In the field of handwritten text recognition (HTR), this shows itself in poor recognition accuracy for writers that are not similar to those seen during training. An ideal HTR model should be adaptive to new writing styles in order to handle the vast amount of possible writing styles. In this paper, we explore how HTR models can be made writer adaptive by using only a handful of examples from a new writer (e.g., 16 examples) for adaptation. Two HTR architectures are used as base models, using a ResNet backbone along with either an LSTM or Transformer sequence decoder. Using these base models, two methods are considered to make them writer adaptive: 1) model-agnostic meta-learning (MAML), an algorithm commonly used for tasks such as few-shot classification, and 2) writer codes, an idea originating from automatic speech recognition. Results show that an HTR-specific version of MAML known as MetaHTR improves performance compared to the baseline with a 1.4 to 2.0 improvement in word error rate (WER). The improvement due to writer adaptation is between 0.2 and 0.7 WER, where a deeper model seems to lend itself better to adaptation using MetaHTR than a shallower model. However, applying MetaHTR to larger HTR models or sentence-level HTR may become prohibitive due to its high computational and memory requirements. Lastly, writer codes based on learned features or Hinge statistical features did not lead to improved recognition performance.
Cross-Domain Few-Shot Semantic Segmentation
Authors: Shuo Lei
Date: 2022-03-24 00:00:00
Proceeding: ECCV 2022 3
Tasks: Cross-Domain Few-Shot, Few-Shot Semantic Segmentation, Meta-Learning, Segmentation, Semantic Segmentation, Transfer Learning

Few-shot semantic segmentation aims at learning to segment
a novel object class with only a few annotated examples. Most existing methods consider a setting where base classes are sampled from the
same domain as the novel classes. However, in many applications, collecting sufficient training data for meta-learning is infeasible or impossible. In this paper, we extend few-shot semantic segmentation to a new
task, called Cross-Domain Few-Shot Semantic Segmentation (CD-FSS),
which aims to generalize the meta-knowledge from domains with sufficient training labels to low-resource domains. Moreover, a new benchmark for the CD-FSS task is established and characterized by a task
difficulty measurement. We evaluate both representative few-shot segmentation methods and transfer learning based methods on the proposed
benchmark and find that current few-shot segmentation methods fail to
address CD-FSS. To tackle the challenging CD-FSS problem, we propose
a novel Pyramid-Anchor-Transformation based few-shot segmentation
network (PATNet), in which domain-specific features are transformed
into domain-agnostic ones for downstream segmentation modules to fast
adapt to unseen domains. Our model outperforms the state-of-the-art
few-shot segmentation method in CD-FSS by 8.49% and 10.61% average accuracies in 1-shot and 5-shot, respectively. Code and datasets are
available at https://github.com/slei109/PATNet
AIA: Attention in Attention Within Collaborate Domains
Authors: Le Zhang, Qi Feng, Yao Lu, Chang Liu, and Guangming Lu
Date: 2022-10-07 00:00:00
Proceeding: Pattern Recognition and Computer Vision 2022 10
Tasks: Deep Attention, Position

Attention mechanisms can effectively improve the performance of the mobile networks with a limited computational complexity cost. However, existing attention methods extract importance from only one domain of the networks, hindering further performance improvement. In this paper, we propose the Attention in Attention (AIA) mechanism integrating One Dimension Frequency Channel Attention (1D FCA) with Joint Coordinate Attention (JCA) to collaboratively adjust the channel and coordinate weights in frequency and spatial domains, respectively. Specifically, 1D FCA using 1D Discrete Cosine Transform (DCT) adaptively extract and enhance the necessary channel information in the frequency domain. The JCA using explicit and implicit coordinate information extract and embed position feature into frequency channel attention. Extensive experiments on different datasets demonstrate that the proposed AIA mechanism can effectively improve the accuracy with only a limited computation complexity cost.
Information-based Preprocessing of PLC Data for Automatic Behavior Modeling
Authors: Brandon K. Sai, Jonas Gram, Thomas Bauernhansl
Date: 2023-07-28 00:00:00
Tasks: Time Series

Cyber-physical systems (CPS) offer immense optimization potential for manufacturing processes through the availability of multivariate time series data of actors and sensors. Based on automated analysis software, the deployment of adaptive and responsive measures is possible for time series data. Due to the complex and dynamic nature of modern manufacturing, analysis and modeling often cannot be entirely automated. Even machine- or deep learning approaches often depend on a priori expert knowledge and labelling. In this paper, an information-based data preprocessing approach is proposed. By applying statistical methods including variance and correlation analysis, an approximation of the sampling rate in event-based systems and the utilization of spectral analysis, knowledge about the underlying manufacturing processes can be gained prior to modeling. The paper presents, how statistical analysis enables the pruning of a dataset's least important features and how the sampling rate approximation approach sets the base for further data analysis and modeling. The data's underlying periodicity, originating from the cyclic nature of an automated manufacturing process, will be detected by utilizing the fast Fourier transform. This information-based preprocessing method will then be validated for process time series data of cyber-physical systems' programmable logic controllers (PLC).
Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness
Authors: Linqing Li, Zhifeng Wang
Date: 2023-07-15 00:00:00
Tasks: cognitive diagnosis, Informativeness, Knowledge Graphs

Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise-rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, namely the Knowledge-Graph-Exercise Representativeness and Informativeness Framework, to address these two issues. The framework consists of four intricate components and a novel cognitive diagnosis model called the Neural Attentive cognitive diagnosis model. These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component. The informativeness component evaluates the informational value of each question and identifies the candidate question set that exhibits the highest exercise informativeness. Furthermore, the skill embeddings are employed as input for the knowledge importance component. This component transforms a one-dimensional knowledge graph into a multi-dimensional one through four class relations and calculates skill importance weights based on novelty and popularity. Subsequently, the exercise representativeness component incorporates exercise weight knowledge coverage to select questions from the candidate question set for the tested question set. Lastly, the cognitive diagnosis model leverages exercise representation and skill importance weights to predict student performance on the test set and estimate their knowledge state. To evaluate the effectiveness of our selection strategy, extensive experiments were conducted on two publicly available educational datasets. The experimental results demonstrate that our framework can recommend appropriate exercises to students, leading to improved student performance.
Detecting the Presence of COVID-19 Vaccination Hesitancy from South African Twitter Data Using Machine Learning
Authors: Nicholas Perikli, Srimoy Bhattacharya, Blessing Ogbuokiri, Zahra Movahedi Nia, Benjamin Lieberman, Nidhi Tripathi, Salah-Eddine Dahbi, Finn Stevenson, Nicola Bragazzi, Jude Kong, Bruce Mellado
Date: 2023-07-12 00:00:00
Tasks: Sentiment Analysis

Very few social media studies have been done on South African user-generated content during the COVID-19 pandemic and even fewer using hand-labelling over automated methods. Vaccination is a major tool in the fight against the pandemic, but vaccine hesitancy jeopardizes any public health effort. In this study, sentiment analysis on South African tweets related to vaccine hesitancy was performed, with the aim of training AI-mediated classification models and assessing their reliability in categorizing UGC. A dataset of 30000 tweets from South Africa were extracted and hand-labelled into one of three sentiment classes: positive, negative, neutral. The machine learning models used were LSTM, bi-LSTM, SVM, BERT-base-cased and the RoBERTa-base models, whereby their hyperparameters were carefully chosen and tuned using the WandB platform. We used two different approaches when we pre-processed our data for comparison: one was semantics-based, while the other was corpus-based. The pre-processing of the tweets in our dataset was performed using both methods, respectively. All models were found to have low F1-scores within a range of 45$\%$-55$\%$, except for BERT and RoBERTa which both achieved significantly better measures with overall F1-scores of 60$\%$ and 61$\%$, respectively. Topic modelling using an LDA was performed on the miss-classified tweets of the RoBERTa model to gain insight on how to further improve model accuracy.
Formation Control for Moving Target Enclosing via Relative Localization
Authors: Xueming Liu, Kunda Liu, Tianjiang Hu, Qingrui Zhang
Date: 2023-07-28 00:00:00

In this paper, we investigate the problem of controlling multiple unmanned aerial vehicles (UAVs) to enclose a moving target in a distributed fashion based on a relative distance and self-displacement measurements. A relative localization technique is developed based on the recursive least square estimation (RLSE) technique with a forgetting factor to estimates both the ``UAV-UAV'' and ``UAV-target'' relative positions. The formation enclosing motion is planned using a coupled oscillator model, which generates desired motion for UAVs to distribute evenly on a circle. The coupled-oscillator-based motion can also facilitate the exponential convergence of relative localization due to its persistent excitation nature. Based on the generation strategy of desired formation pattern and relative localization estimates, a cooperative formation tracking control scheme is proposed, which enables the formation geometric center to asymptotically converge to the moving target. The asymptotic convergence performance is analyzed theoretically for both the relative localization technique and the formation control algorithm. Numerical simulations are provided to show the efficiency of the proposed algorithm. Experiments with three quadrotors tracking one target are conducted to evaluate the proposed target enclosing method in real platforms.
PCNN: A Lightweight Parallel Conformer Neural Network for Efficient Monaural Speech Enhancement
Authors: Xinmeng Xu, Weiping tu, Yuhong Yang
Date: 2023-07-28 00:00:00
Tasks: Speech Enhancement

Convolutional neural networks (CNN) and Transformer have wildly succeeded in multimedia applications. However, more effort needs to be made to harmonize these two architectures effectively to satisfy speech enhancement. This paper aims to unify these two architectures and presents a Parallel Conformer for speech enhancement. In particular, the CNN and the self-attention (SA) in the Transformer are fully exploited for local format patterns and global structure representations. Based on the small receptive field size of CNN and the high computational complexity of SA, we specially designed a multi-branch dilated convolution (MBDC) and a self-channel-time-frequency attention (Self-CTFA) module. MBDC contains three convolutional layers with different dilation rates for the feature from local to non-local processing. Experimental results show that our method performs better than state-of-the-art methods in most evaluation criteria while maintaining the lowest model parameters.
Three-phase grid-forming droop control for unbalanced systems and fault ride through
Authors: Prajwal Bhagwat, Dominic Groß
Date: 2023-07-28 00:00:00

In this work, we investigate grid-forming (GFM) control for dc/ac voltage source converters (VSC) under unbalanced system conditions and unbalanced faults. To fully leverage the degrees of freedom of VSCs, we introduce the concept of generalized three-phase GFM control that combines individual GFM controls for every phase with a phase balancing feedback. The proposed control allows trading off voltage and power unbalance under unbalanced conditions, enables current limiting for each phase during unbalanced faults, and reduces to positive sequence GFM droop control in balanced systems. High-fidelity simulations are used to illustrate the properties of the control.
Robust data-driven learning and control of nonlinear systems. A Sontag's formula approach
Authors: Yeyson A. Becerra-Mora, José Ángel Acosta
Date: 2023-07-28 00:00:00

An interlaced method to learn and control nonlinear system dynamics from a set of demonstrations is proposed, under a constrained optimization framework for the unsupervised learning process. The nonlinear system is modelled as a mixture of Gaussians and the Sontag's formula together with its associated Control Lyapunov Function is proposed for learning and control. Lyapunov stability and robustness in noisy data environments are guaranteed, as a result of the inclusion of control in the learning-optimization problem. The performances are validated through a well-known dataset of demonstrations with handwriting complex trajectories, succeeding in all of them and outperforming previous methods under bounded disturbances, possibly coming from inaccuracies, imperfect demonstrations or noisy datasets. As a result, the proposed interlaced solution yields a good performance trade-off between reproductions and robustness. The proposed method can be used to program nonlinear trajectories in robotic systems through human demonstrations.
Control-mode as a Grid Service in Software-defined Power Grids: GFL vs GFM
Authors: Guoxuan Cui, Zhongda Chu, Fei Teng
Date: 2023-07-28 00:00:00
Tasks: Scheduling

In power systems with high penetration of power electronics, grid-forming control is proposed to replace traditional Grid-Following Converter (GFL) in order to improve the overall system strength and resist small-signal instability in weak grids by directly forming the terminal voltage. However, sufficient headroom of both active and reactive power must be made available for Grid-Forming Converter (GFM) to operate, potentially leading to sub-optimal operation in steady states. This presents a new research problem to optimally allocate between GFM and GFL to balance the ability of GFMs to improve the grid strength and the potential economic loss resulting from reserved headroom. An optimization framework under software-defined grids is proposed, for the first time, to dynamically determine the optimal allocation of GFMs and GFLs in power systems at each time step of system scheduling according to system conditions, which ensures both system stability and minimum operational cost. To achieve this, the system scheduling model is expanded to simultaneously consider the constraints related to active and reactive power reserves for GFMs, as well as the system level stability. Case studies conducted on the modified IEEE 30-bus system demonstrate significant economic benefits in that the optimal proportion of GFMs in the power system can be dynamically determined while ensuring power reserve and grid stability constraints.
Leveraging Optical Communication Fiber and AI for Distributed Water Pipe Leak Detection
Authors: Huan Wu, Huan-Feng Duan, Wallace W. L. Lai, Kun Zhu, Xin Cheng, Hao Yin, Bin Zhou, Chun-Cheung Lai, Chao Lu, Xiaoli Ding
Date: 2023-07-28 00:00:00
Tasks: Management

Detecting leaks in water networks is a costly challenge. This article introduces a practical solution: the integration of optical network with water networks for efficient leak detection. Our approach uses a fiber-optic cable to measure vibrations, enabling accurate leak identification and localization by an intelligent algorithm. We also propose a method to access leak severity for prioritized repairs. Our solution detects even small leaks with flow rates as low as 0.027 L/s. It offers a cost-effective way to improve leak detection, enhance water management, and increase operational efficiency.
Weather Sensitive High Spatio-Temporal Resolution Transportation Electric Load Profiles For Multiple Decarbonization Pathways
Authors: Samrat Acharya, Malini Ghosal, Travis Thurber, Casey D. Burleyson, Yang Ou, Allison Campbell, Gokul Iyer, Nathalie Voisin, Jason Fuller
Date: 2023-07-28 00:00:00

Electrification of transport compounded with climate change will transform hourly load profiles and their response to weather. Power system operators and EV charging stakeholders require such high-resolution load profiles for their planning studies. However, such profiles accounting whole transportation sector is lacking. Thus, we present a novel approach to generating hourly electric load profiles that considers charging strategies and evolving sensitivity to temperature. The approach consists of downscaling annual state-scale sectoral load projections from the multi-sectoral Global Change Analysis Model (GCAM) into hourly electric load profiles leveraging high resolution climate and population datasets. Profiles are developed and evaluated at the Balancing Authority scale, with a 5-year increment until 2050 over the Western U.S. Interconnect for multiple decarbonization pathways and climate scenarios. The datasets are readily available for production cost model analysis. Our open source approach is transferable to other regions.