pwc-archive/papers-with-abstracts huggingface dataset:
Hear Me Out: A Study on the Use of the Voice Modality for Crowdsourced Relevance Assessments
Authors: Nirmal Roy, Agathe Balayn, David Maxwell, Claudia Hauff
Date: 2023-04-21 00:00:00

The creation of relevance assessments by human assessors (often nowadays crowdworkers) is a vital step when building IR test collections. Prior works have investigated assessor quality & behaviour, though into the impact of a document's presentation modality on assessor efficiency and effectiveness. Given the rise of voice-based interfaces, we investigate whether it is feasible for assessors to judge the relevance of text documents via a voice-based interface. We ran a user study (n = 49) on a crowdsourcing platform where participants judged the relevance of short and long documents sampled from the TREC Deep Learning corpus-presented to them either in the text or voice modality. We found that: (i) participants are equally accurate in their judgements across both the text and voice modality; (ii) with increased document length it takes participants significantly longer (for documents of length > 120 words it takes almost twice as much time) to make relevance judgements in the voice condition; and (iii) the ability of assessors to ignore stimuli that are not relevant (i.e., inhibition) impacts the assessment quality in the voice modality-assessors with higher inhibition are significantly more accurate than those with lower inhibition. Our results indicate that we can reliably leverage the voice modality as a means to effectively collect relevance labels from crowdworkers.
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction
Authors: Zhen Tian, Ting Bai, Wayne Xin Zhao, Ji-Rong Wen, Zhao Cao
Date: 2023-04-21 00:00:00
Tasks: Click-Through Rate Prediction

Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with massive features in online e-commerce platforms. Most existing methods manually design a maximal order and further filter out the useless interactions from them. Although they reduce the high computational costs caused by the exponential growth of high-order feature combinations, they still suffer from the degradation of model capability due to the suboptimal learning of the restricted feature orders. The solution to maintain the model capability and meanwhile keep it efficient is a technical challenge, which has not been adequately addressed. To address this issue, we propose an adaptive feature interaction learning model, named as EulerNet, in which the feature interactions are learned in a complex vector space by conducting space mapping according to Euler's formula. EulerNet converts the exponential powers of feature interactions into simple linear combinations of the modulus and phase of the complex features, making it possible to adaptively learn the high-order feature interactions in an efficient way. Furthermore, EulerNet incorporates the implicit and explicit feature interactions into a unified architecture, which achieves the mutual enhancement and largely boosts the model capabilities. Such a network can be fully learned from data, with no need of pre-designed form or order for feature interactions. Extensive experiments conducted on three public datasets have demonstrated the effectiveness and efficiency of our approach. Our code is available at: https://github.com/RUCAIBox/EulerNet.
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems
Authors: Patrick John Chia, Giuseppe Attanasio, Jacopo Tagliabue, Federico Bianchi, Ciro Greco, Gabriel de Souza P. Moreira, Davide Eynard, Fahd Husain
Date: 2023-04-20 00:00:00
Tasks: Diversity, Fairness, Model Selection, Recommendation Systems

Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user retention and fairness, often taking a back seat. Moreover, reconciling multiple performance perspectives is by definition indeterminate, presenting a stumbling block to those in the pursuit of rounded evaluation of Recommender Systems. EvalRS 2022 -- a data challenge designed around Multi-Objective Evaluation -- was a first practical endeavour, providing many insights into the requirements and challenges of balancing multiple objectives in evaluation. In this work, we reflect on EvalRS 2022 and expound upon crucial learnings to formulate a first-principles approach toward Multi-Objective model selection, and outline a set of guidelines for carrying out a Multi-Objective Evaluation challenge, with potential applicability to the problem of rounded evaluation of competing models in real-world deployments.
Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation
Authors: Juanjuan Yu, Xiufeng He, Peng Yang, Mahdi Motagh, Jia Xu and Jiacheng Xiong
Date: 2023-04-19 00:00:00
Proceeding: Remote Sensing 2023 4
Tasks: Cultural Vocal Bursts Intensity Prediction

Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management.
However, the dense distribution and various sizes of aquacultures make it challenging to accurately
extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework
that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric
decomposition algorithms were applied to extract 13 polarimetric scattering features. Together
with the nine other polarisation and texture features, a total of 22 polarimetric features were then
extracted, among which four were optimised according to the separability index. Subsequently, to
reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual
aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground
functions, the results of which were then used in the marker-controlled watershed algorithm to obtain
refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers
into three categories (aquaculture, background and dikes) was applied to the marker function. In
addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks
to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a
morphological operation that was conducted for label augmentation. An experimental investigation
performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the
crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and
the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were
introduced, our proposed method performed better than standard UNet++ in aquaculture extraction,
achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The
experimental results indicate that the proposed method can handle the adhesion of both adjacent
objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries.
Extraction of cropland field parcels with high resolution remote sensing using multi-task learning
Authors: Leilei Xu, Peng Yang, Juanjuan Yu, Fei Peng, Jia Xu, Shiran Song &Yongxing Wu
Date: 2023-02-14 00:00:00
Proceeding: European Journal of Remote Sensing 2023 2
Tasks: Edge Detection, Management, Multi-Task Learning

Parcel-level farmland information contains rich spatial distribution and boundary details, which is crucial for digital agriculture and agricultural resource surveys. However, the spatial complexity and heterogeneity of features resulting from high resolution makes it difficult to obtain parcel-level information quickly and accurately. In addition, existing methods do not sufficiently take into account the spatial topological information, particularly for blurred boundaries. Here, we develop a multi-task network model to extract plot-level cropland information. Specifically, the model consists of a cascaded multi-task network with integrated semantic and edge detection, a refinement network with fixed edge local connectivity, and an integrated fusion model. To validate the performance of the model, two typical tests were conducted in Denmark (Europe) and Chongqing (Asia) with high-resolution remote sensing images provided by Sentinel-2 (10 m) and Google Earth (0.53 m) as data sources. The results show that our proposed model outperforms other baseline models and exhibits higher performance. This study is expected to provide important support for the design of new global agricultural information management systems in the future.
An Order-Complexity Model for Aesthetic Quality Assessment of Homophony Music Performance
Authors: Xin Jin, Wu Zhou, Jinyu Wang, Duo Xu, Yiqing Rong, Jialin Sun
Date: 2023-04-23 00:00:00

Although computational aesthetics evaluation has made certain achievements in many fields, its research of music performance remains to be explored. At present, subjective evaluation is still a ultimate method of music aesthetics research, but it will consume a lot of human and material resources. In addition, the music performance generated by AI is still mechanical, monotonous and lacking in beauty. In order to guide the generation task of AI music performance, and to improve the performance effect of human performers, this paper uses Birkhoff's aesthetic measure to propose a method of objective measurement of beauty. The main contributions of this paper are as follows: Firstly, we put forward an objective aesthetic evaluation method to measure the music performance aesthetic; Secondly, we propose 10 basic music features and 4 aesthetic music features. Experiments show that our method performs well on performance assessment.
Attention-based Part Assembly for 3D Volumetric Shape Modeling
Authors: Chengzhi Wu, Junwei Zheng, Julius Pfrommer, Jürgen Beyerer
Date: 2023-04-17 00:00:00
Tasks: 3D Shape Modeling

Modeling a 3D volumetric shape as an assembly of decomposed shape parts is much more challenging, but semantically more valuable than direct reconstruction from a full shape representation. The neural network needs to implicitly learn part relations coherently, which is typically performed by dedicated network layers that can generate transformation matrices for each part. In this paper, we propose a VoxAttention network architecture for attention-based part assembly. We further propose a variant of using channel-wise part attention and show the advantages of this approach. Experimental results show that our method outperforms most state-of-the-art methods for the part relation-aware 3D shape modeling task.
YUKI Algorithm and POD-RBF for Elastostatic and dynamic crack identification
Authors: Brahim Benaissa, Nourredine Aït Hocine, Samir Khatir, Mohamed Kamel Riahi, Seyedali Mirjalili
Date: 2021-09-23 00:00:00
Proceeding: Journal of Computational Science 2021 9

This paper proposes a new metaheuristic algorithm with a search space reduction capability guided by simple formalism. The search population focuses partially on the inside the local search area while the rest explore globally, looking for better search areas. We call the new algorithm by YUKI Algoritm (YA) and employ it in a crack identification problem. With the aid of a set of measurements taken on the defected structure, we aim at identifying the crack parameters such as length and orientation. To this end, we use the so-called model reduction technique through Proper orthogonal Decomposition (POD) endorsed with Radial Basic Function (RBF), which helps in predicting (numerically) the measurement at new points (out of the set of sensors) via interpolation. This method is widely used in this context and was proven very effective computational-wise. In our study of the performance of YA, we deal with two cases; Firstly, in the case of the Elastostatic study. And secondly, in the case of dynamic analysis. We compare the performance of the suggested algorithm with the performance of well-known optimization methods, such as Teaching Learning Based Optimization (TLBO), Cuckoo Search (CS), and the Gray Wolf Optimizer (GWO). The results show that YA provides accurate and faster results compared to the mentioned algorithms.
Deep Neural Network and YUKI Algorithm for Inner Damage Characterization Based on Elastic Boundary Displacement
Authors: Nasreddine Amoura, Brahim Benaissa, Musaddiq Al Ali, and Samir Khatir
Date: 2023-01-31 00:00:00
Proceeding: International Conference of Steel and Composite for Engineering Structures 2023 1

The efficiency of deep neural networks has been proven in several research fields. In this study, we suggest using this method of inverse crack identification based on the structural response of boundary displacement. This structural response is particularly challenging for surrogate modelling due to the overall similarity in the effect of different cracks. From the inverse problem perspective, this corresponds to a problem of many local minima. To solve this problem we use the newly suggested search technique of the dynamic search space reduction by the YUKI algorithm, build to solve this type of problem. We compare the performance of the suggested approach of the RBF modelling technique in terms of direct problem prediction and inverse problem identification accuracy. Deep Neural Networks are found to have better performance in both problems, although the computation time is significantly higher than RBF.
PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis
Authors: Shu Wei, Nuo Xu
Date: 2023-04-24 00:00:00
Tasks: Document Layout Analysis, Graph Neural Network, Image Segmentation, Semantic Segmentation

Document layout analysis has a wide range of requirements across various domains, languages, and business scenarios. However, most current state-of-the-art algorithms are language-dependent, with architectures that rely on transformer encoders or language-specific text encoders, such as BERT, for feature extraction. These approaches are limited in their ability to handle very long documents due to input sequence length constraints and are closely tied to language-specific tokenizers. Additionally, training a cross-language text encoder can be challenging due to the lack of labeled multilingual document datasets that consider privacy. Furthermore, some layout tasks require a clean separation between different layout components without overlap, which can be difficult for image segmentation-based algorithms to achieve. In this paper, we present Paragraph2Graph, a language-independent graph neural network (GNN)-based model that achieves competitive results on common document layout datasets while being adaptable to business scenarios with strict separation. With only 19.95 million parameters, our model is suitable for industrial applications, particularly in multi-language scenarios.
RESEARCH PAPER Crack identification using model reduction based on proper orthogonal decomposition coupled with radial basis functions
Authors: Brahim Benaissa, Nourredine Aït Hocine, Idir Belaidi, Abderrachid Hamrani, Valeria Pettarin
Date: 2022-02-06 00:00:00
Proceeding: Structural and Multidisciplinary Optimization 2022 2
Tasks: Position

This work deals with the crack identification using model reduction based on the proper orthogonal decomposition method. The proposed inverse problem consists of the estimation of the crack length and its position in a plate using boundary displacements as input data. Genetic algorithm and particle swarm optimization were applied for the minimization of the error function expressed as the difference between the boundary displacements of the actual crack and those of the estimated crack. It was found that the proposed approach is able to accurately estimate crack size and detect its location. The stability of the identification algorithm was tested against measurement uncertainty by introducing a white Gaussian noise in the input data. The approach showed high stability for noise levels lower than 5 %. The efficiency of the approach using small number of sensor points was also demonstrated.
How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning
Authors: Haodong Feng, Yue Wang, Hui Xiang, Zhiyang Jin, Dixia Fan
Date: 2023-04-23 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Self-Learning, State Estimation, valid

Deep reinforcement learning (DRL) for fluidic pinball, three individually rotating cylinders in the uniform flow arranged in an equilaterally triangular configuration, can learn the efficient flow control strategies due to the validity of self-learning and data-driven state estimation for complex fluid dynamic problems. In this work, we present a DRL-based real-time feedback strategy to control the hydrodynamic force on fluidic pinball, i.e., force extremum and tracking, from cylinders' rotation. By adequately designing reward functions and encoding historical observations, and after automatic learning of thousands of iterations, the DRL-based control was shown to make reasonable and valid control decisions in nonparametric control parameter space, which is comparable to and even better than the optimal policy found through lengthy brute-force searching. Subsequently, one of these results was analyzed by a machine learning model that enabled us to shed light on the basis of decision-making and physical mechanisms of the force tracking process. The finding from this work can control hydrodynamic force on the operation of fluidic pinball system and potentially pave the way for exploring efficient active flow control strategies in other complex fluid dynamic problems.
TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential
Authors: Dongjingdin Liu, Pengpeng Chen, Miao Yao, Yijing Lu, Zijie Cai, Yuxin Tian
Date: 2023-04-23 00:00:00
Tasks: Action Recognition, Graph Learning, Skeleton Based Action Recognition, Temporal Action Localization, Time Series

Skeleton-based action recognition has achieved remarkable results in human action recognition with the development of graph convolutional networks (GCNs). However, the recent works tend to construct complex learning mechanisms with redundant training and exist a bottleneck for long time-series. To solve these problems, we propose the Temporal-Spatio Graph ConvNeXt (TSGCNeXt) to explore efficient learning mechanism of long temporal skeleton sequences. Firstly, a new graph learning mechanism with simple structure, Dynamic-Static Separate Multi-graph Convolution (DS-SMG) is proposed to aggregate features of multiple independent topological graphs and avoid the node information being ignored during dynamic convolution. Next, we construct a graph convolution training acceleration mechanism to optimize the back-propagation computing of dynamic graph learning with 55.08\% speed-up. Finally, the TSGCNeXt restructure the overall structure of GCN with three Spatio-temporal learning modules,efficiently modeling long temporal features. In comparison with existing previous methods on large-scale datasets NTU RGB+D 60 and 120, TSGCNeXt outperforms on single-stream networks. In addition, with the ema model introduced into the multi-stream fusion, TSGCNeXt achieves SOTA levels. On the cross-subject and cross-set of the NTU 120, accuracies reach 90.22% and 91.74%.
Distilling from Similar Tasks for Transfer Learning on a Budget
Authors: Kenneth Borup, Cheng Perng Phoo, Bharath Hariharan
Date: 2023-04-24 00:00:00
Proceeding: ICCV 2023 1
Tasks: Transfer Learning

We address the challenge of getting efficient yet accurate recognition systems with limited labels. While recognition models improve with model size and amount of data, many specialized applications of computer vision have severe resource constraints both during training and inference. Transfer learning is an effective solution for training with few labels, however often at the expense of a computationally costly fine-tuning of large base models. We propose to mitigate this unpleasant trade-off between compute and accuracy via semi-supervised cross-domain distillation from a set of diverse source models. Initially, we show how to use task similarity metrics to select a single suitable source model to distill from, and that a good selection process is imperative for good downstream performance of a target model. We dub this approach DistillNearest. Though effective, DistillNearest assumes a single source model matches the target task, which is not always the case. To alleviate this, we propose a weighted multi-source distillation method to distill multiple source models trained on different domains weighted by their relevance for the target task into a single efficient model (named DistillWeighted). Our methods need no access to source data, and merely need features and pseudo-labels of the source models. When the goal is accurate recognition under computational constraints, both DistillNearest and DistillWeighted approaches outperform both transfer learning from strong ImageNet initializations as well as state-of-the-art semi-supervised techniques such as FixMatch. Averaged over 8 diverse target tasks our multi-source method outperforms the baselines by 5.6%-points and 4.5%-points, respectively.
A Survey on Multi-Resident Activity Recognition in Smart Environments
Authors: Farhad MortezaPour Shiri, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed, Mohd Anuaruddin Bin Ahmadon, Shingo Yamaguchi
Date: 2023-04-24 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Survey

Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.