pwc-archive/papers-with-abstracts huggingface dataset:
Overview of BioNLP’09 Shared Task on Event Extraction
Authors: Jin-Dong Kim, Tomoko Ohta, Sampo Pyysalo, Yoshinobu Kano, Jun’ichi Tsujii
Date: 2009-06-01 00:00:00
Proceeding: Association for Computational Linguistics 2009 6
Tasks: Event Extraction, Specificity

The paper presents the design and implementation of the BioNLP’09 Shared Task, and reports the final results with analysis. The shared task consists of three sub-tasks, each of which addresses bio-molecular event extraction at a different level of specificity. The data was developed based on the GENIA event corpus. The shared task was run over 12 weeks, drawing initial interest from 42 teams. Of these teams, 24 submitted final results. The evaluation results are encouraging, indicating that state-of-the-art performance is approaching a practically applicable level and revealing some remaining challenges.
The Strategy Evolution in Double Auction Based on the Experience-Weighted Attraction Learning Model
Authors: Qian Yu, Yaqin Liu, De Xia, Luis Martínez
Date: 2019-01-31 00:00:00
Proceeding: IEEE Access ( Volume: 7) 2019 1

The double auction is a widely applicable trading mechanism used to converge to competitive equilibrium in different markets from which multiple equilibriums and incomplete information may arise. Therefore, different learning models have been applied to facilitate bidding strategies for buyers and sellers in the market. However, due to the existence of problems in double auction markets such as individual bounded rationality and information incompleteness, it is still necessary to explore a more general learning model to depict the learning mechanism in double auction markets and predict the evolution processes of bidding strategies for both sides. Therefore, this paper aims at introducing the use of the experience-weighted attraction (EWA) model for double auction because it combines reinforcement learning with belief learning that then converts EWA in a suitable and interesting learning model for describing and improving individuals' learning behavior. It can become an effective learning model for bidding strategies in the double auction. In addition to the use of the EWA for strategy evolution in the double auction, the impact of its different bidding strategy performance parameters will be also analyzed and compared with other learning models.
Design of Double Auction Mechanism Based on Social Network
Authors: Junping Xu, Xin He
Date: 2020-01-07 00:00:00
Proceeding: IEEE Access ( Volume 8) 2020 1

In the traditional double auction market, the number of achievable tradable pairs are usually limited by the market size. To solve this problem, we propose new double auction mechanisms based on social networks to realize market expansion. In this paper, we examine the double auction market, which consists of a group of sellers and a group of buyers, where the sellers provide the same type of items, and the buyers respectively purchase one from them. In addition, every buyer can invite other potential buyers to enter the market through social networks. The goal of this paper is to propose mechanisms such that they can encourage buyers already in the market to invite other potential buyers to join the auction through social networks, and achieve an effective allocation of merchandises and increase profits for sellers, which cannot be achieved under the existing double auction mechanism. We found that the extended McAfee auction mechanism cannot motivate existing buyers to invite other potential ones, while the traditional Vickrey-Clark-Groves (VCG) mechanism cannot guarantee to break even. To solve these problems, we propose two mechanisms, which are called information network auction (INA) mechanism and double network auction (DNA) mechanism. Both of these mechanisms can encourage old buyers in the market to invite new potential customers to participate. Moreover, INA focuses on achieving more effective allocation and DNA focuses on ensuring a balance between income and expenditure.
Truthful Transaction Protocol for E-Commerce Networks Based on Double Auction
Authors: Jiachen Sun, Ning Ge, Xu Chen, Wei Feng, Jianhua Lu
Date: 2022-11-08 00:00:00
Proceeding: IEEE Transactions on Network Science and Engineering 2022 11
Tasks: Computational Efficiency

With the prosperous development of e-commerce, customized products have gradually become an important part of online transactions, opening up a new promising market for traditional e-commerce. However, mainstream transaction networks do not provide efficient support to transactions of customized products, thus failing to meet the personalized needs of customers, and pricing strategies are usually dominated by sellers, leading to loss of customers' interests. To overcome these problems, we propose an efficient truthful transaction protocol for e-commerce networks, called ETTP. First, we design a screening algorithm to achieve on-demand selection of transaction participants in hierarchical network domains before transaction initiation. This screening algorithm is customer-oriented and offers personalized commodities by preventing unqualified sellers from participating in the transaction. Furthermore, we develop the ETTP-Auction algorithm based on double auction to improve the transaction efficiency. ETTP-Auction inherently ensures the participation of buyers in pricing and protects their interests. Finally, we prove theoretically that ETTP-Auction satisfies the four economic properties (individual rationality, incentive compatibility, budget balance, and computational efficiency) and verify the performance of the proposed method through simulations. Our ETTP protocol achieves significantly higher system performance, reduces communication overhead, and features better scalability compared with benchmark methods.
Deep unsupervised anomaly detection
Authors: Tangqing Li, Zheng Wang, Siying Liu, Wen-Yan Lin
Date: 2021-01-05 00:00:00
Proceeding: Winter Conference on Applications of Computer Vision 2021 1
Tasks: Anomaly Detection, Clustering, Representation Learning, Unsupervised Anomaly Detection, Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly

This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting. The key idea behind our algorithm is to learn the representation underlying normal data. To this end, we leverage the latest clustering technique suitable for handling high dimensional data. This hypothesis provides a reliable starting point for normal data selection. We train an autoencoder from the normal data subset, and iterate between hypothesizing nor-mal candidate subset based on clustering and representation learning. The reconstruction error from the learned autoencoder serves as a scoring function to assess the normality of the data. Experimental results on several public benchmark datasets show that the proposed method outperforms state-of-the-art unsupervised techniques and is comparable to semi-supervised techniques in most cases
D²Net: A Denoising and Dereverberation Network Based on Two-branch Encoder and Dual-path Transformer
Authors: Liusong Wang, Wenbing Wei, Yadong Chen, and Ying Hu
Date: 2022-11-21 00:00:00
Proceeding: APSIPA ASC 2022 11
Tasks: Denoising, Speech Enhancement

The simultaneous denoising and dereverberation for single-channel mixture speech under the complicated acoustic environment is considered to be a challengeable task. In this paper, we propose a denoising and dereverberation network named as D²Net in which a two-branch encoder (TBE) is designed to extract and selectively fuse features with different granularity. In addition, we design a global-local dual-path transformer (GLDPT) which introduces the local dense synthesizer attention (LDSA) in the dual-path transformer to improve the perception of local information. We evaluated our proposed D²Net and conducted ablation studies on the VoiceBank+DEMAND and WHAMR! datasets. Meanwhile, we chose three types of data in the WHAMR! dataset to verify the ability of the D²Net on the tasks of denoising-only, dereverberation-only, and simultaneous denoising and dereverberation, respectively. Experimental results show that our proposed model outperforms the comparative models, and all achieve better performance on the tasks of simultaneous denoising and dereverberation, dereverberation-only, and denoising-only, while keeping a small number of network parameters.
Towards On-device Domain Adaptation for Noise-Robust Keyword Spotting
Authors: Cristian Cioflan, Lukas Cavigelli, Manuele Rusci, Miguel de Prado, Luca Benini
Date: 2022-06-13 00:00:00
Proceeding: IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) 2022 6
Tasks: Edge-computing, Keyword Spotting

The accuracy of a keyword spotting model deployed on embedded devices often degrades when the system is exposed to real environments with significant noise. In this paper, we explore a methodology for tailoring a model to on-site noises through on-device domain adaptation, while accounting for the edge computing-associated costs. We show that accuracy improvements by up to 18 % can be obtained by specialising on difficult, previously unseen noise types, on embedded devices with a power budget in the Watt range, with a storage requirement of 1.1 GB. We also demonstrate an accuracy improvement of 1.43% on an ultra-low power platform consuming few-10mW, requiring only 1.47 MB of memory for the adaptation stage, at a one-time energy cost of 5.81 J.
RMM: Reinforced Memory Management for Class-Incremental Learning
Authors: Yaoyao Liu, Bernt Schiele, Qianru Sun
Date: 2023-01-14 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: class-incremental learning, Class Incremental Learning, Incremental Learning, Management

Class-Incremental Learning (CIL) [40] trains classifiers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase. The preserved data are exemplars used for replaying. However, existing methods use a static and ad hoc strategy for memory allocation, which is often sub-optimal. In this work, we propose a dynamic memory management strategy that is optimized for the incremental phases and different object classes. We call our method reinforced memory management (RMM), leveraging reinforcement learning. RMM training is not naturally compatible with CIL as the past, and future data are strictly non-accessible during the incremental phases. We solve this by training the policy function of RMM on pseudo CIL tasks, e.g., the tasks built on the data of the 0-th phase, and then applying it to target tasks. RMM propagates two levels of actions: Level-1 determines how to split the memory between old and new classes, and Level-2 allocates memory for each specific class. In essence, it is an optimizable and general method for memory management that can be used in any replaying-based CIL method. For evaluation, we plug RMM into two top-performing baselines (LUCIR+AANets and POD+AANets [30]) and conduct experiments on three benchmarks (CIFAR-100, ImageNet-Subset, and ImageNet-Full). Our results show clear improvements, e.g., boosting POD+AANets by 3.6%, 4.4%, and 1.9% in the 25-Phase settings of the above benchmarks, respectively.
Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation
Authors: Yan Li, Mingzhou Yang, Matthew Eagon, Majid Farhadloo, Yiqun Xie, William F. Northrop, Shashi Shekhar
Date: 2023-01-13 00:00:00
Tasks: Decoder

The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is important for societal applications such as eco-routing, which aims to find paths with the lowest exhaust emissions or energy need. The challenges of this problem are three-fold: (1) the dependence of a vehicle's eco-toll on its physical parameters; (2) the lack of access to data with eco-toll information; and (3) the influence of contextual information (i.e. the connections of adjacent segments in the path) on the eco-toll of road segments. Prior work on eco-toll estimation has mostly relied on pure data-driven approaches and has high estimation errors given the limited training data. To address these limitations, we propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas, namely, (1) a physics-informed decoder that integrates the physical laws of the vehicle engine into the network, (2) an attention-based contextual information encoder, and (3) a physics-informed regularization to reduce overfitting. Experiments on real-world heavy-duty truck data show that the proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods.
USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text Retrieval
Authors: Yan Zhang, Zhong Ji, Di Wang, Yanwei Pang, Xuelong Li
Date: 2023-01-17 00:00:00
Tasks: Contrastive Learning, Image-text Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity, Text Retrieval

As a fundamental and challenging task in bridging language and vision domains, Image-Text Retrieval (ITR) aims at searching for the target instances that are semantically relevant to the given query from the other modality, and its key challenge is to measure the semantic similarity across different modalities. Although significant progress has been achieved, existing approaches typically suffer from two major limitations: (1) It hurts the accuracy of the representation by directly exploiting the bottom-up attention based region-level features where each region is equally treated. (2) It limits the scale of negative sample pairs by employing the mini-batch based end-to-end training mechanism. To address these limitations, we propose a Unified Semantic Enhancement Momentum Contrastive Learning (USER) method for ITR. Specifically, we delicately design two simple but effective Global representation based Semantic Enhancement (GSE) modules. One learns the global representation via the self-attention algorithm, noted as Self-Guided Enhancement (SGE) module. The other module benefits from the pre-trained CLIP module, which provides a novel scheme to exploit and transfer the knowledge from an off-the-shelf model, noted as CLIP-Guided Enhancement (CGE) module. Moreover, we incorporate the training mechanism of MoCo into ITR, in which two dynamic queues are employed to enrich and enlarge the scale of negative sample pairs. Meanwhile, a Unified Training Objective (UTO) is developed to learn from mini-batch based and dynamic queue based samples. Extensive experiments on the benchmark MSCOCO and Flickr30K datasets demonstrate the superiority of both retrieval accuracy and inference efficiency. Our source code will be released at https://github.com/zhangy0822/USER.
Deep Learning based Novel Cascaded Approach for Skin Lesion Analysis
Authors: Shubham Innani, Prasad Dutande, Bhakti Baheti, Ujjwal Baid, Sanjay Talbar
Date: 2023-01-16 00:00:00
Tasks: Classification, Decoder, Deep Learning, Diagnostic, Lesion Segmentation, Segmentation, Skin Lesion Segmentation

Automatic lesion analysis is critical in skin cancer diagnosis and ensures effective treatment. The computer aided diagnosis of such skin cancer in dermoscopic images can significantly reduce the clinicians workload and help improve diagnostic accuracy. Although researchers are working extensively to address this problem, early detection and accurate identification of skin lesions remain challenging. This research focuses on a two step framework for skin lesion segmentation followed by classification for lesion analysis. We explored the effectiveness of deep convolutional neural network based architectures by designing an encoder-decoder architecture for skin lesion segmentation and CNN based classification network. The proposed approaches are evaluated quantitatively in terms of the Accuracy, mean Intersection over Union and Dice Similarity Coefficient. Our cascaded end to end deep learning based approach is the first of its kind, where the classification accuracy of the lesion is significantly improved because of prior segmentation.
Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling
Authors: Ahmed Elnaggar, Hazem Essam, Wafaa Salah-Eldin, Walid Moustafa, Mohamed Elkerdawy, Charlotte Rochereau, Burkhard Rost
Date: 2023-01-16 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling, Protein Function Prediction, Protein Language Model, Protein Structure Prediction

As opposed to scaling-up protein language models (PLMs), we seek improving performance via protein-specific optimization. Although the proportionality between the language model size and the richness of its learned representations is validated, we prioritize accessibility and pursue a path of data-efficient, cost-reduced, and knowledge-guided optimization. Through over twenty experiments ranging from masking, architecture, and pre-training data, we derive insights from protein-specific experimentation into building a model that interprets the language of life, optimally. We present Ankh, the first general-purpose PLM trained on Google's TPU-v4 surpassing the state-of-the-art performance with fewer parameters (<10% for pre-training, <7% for inference, and <30% for the embedding dimension). We provide a representative range of structure and function benchmarks where Ankh excels. We further provide a protein variant generation analysis on High-N and One-N input data scales where Ankh succeeds in learning protein evolutionary conservation-mutation trends and introducing functional diversity while retaining key structural-functional characteristics. We dedicate our work to promoting accessibility to research innovation via attainable resources.
SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization
Authors: Zheng Chuanyang, Zheyang Li, Kai Zhang, Zhi Yang, Wenming Tan, Jun Xiao, Ye Ren, ShiLiang Pu
Date: 2022-11-01 00:00:00
Proceeding: NIPS 2022 11
Tasks: object-detection, Object Detection

Vision Transformers (ViTs) yield impressive performance across various vision tasks. However, heavy computation and memory footprint make them inaccessible for edge devices. Previous works apply importance criteria determined independently by each individual component to prune ViTs. Considering that heterogeneous components in ViTs play distinct roles, these approaches lead to suboptimal performance. In this paper, we introduce joint importance, which integrates essential structural-aware interactions between components for the first time, to perform collaborative pruning. Based on the theoretical analysis, we construct a Taylor-based approximation to evaluate the joint importance. This guides pruning toward a more balanced reduction across all components. To further reduce the algorithm complexity, we incorporate the interactions into the optimization function under some mild assumptions. Moreover, the proposed method can be seamlessly applied to various tasks including object detection. Extensive experiments demonstrate the effectiveness of our method. Notably, the proposed approach outperforms the existing state-of-the-art approaches on ImageNet, increasing accuracy by 0.7% over the DeiT-Base baseline while saving 50% FLOPs. On COCO, we are the first to show that 70% FLOPs of FasterRCNN with ViT backbone can be removed with only 0.3% mAP drop.
A Structure Self-Aware Model for Discourse Parsing on Multi-Party Dialogues
Authors: An-Hui Wang, Linfeng Song, Hui Jiang, Shaopeng Lai, Junfeng Yao, Min Zhang, Jinsong Su
Date: 2021-08-01 00:00:00
Proceeding: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence 2021 8
Tasks: Dialogue Understanding, Discourse Parsing, Graph Neural Network, Representation Learning, Response Generation

Conversational discourse structures aim to describe how a dialogue is organised, thus they are helpful for dialogue understanding and response generation. This paper focuses on predicting discourse dependency structures for multi-party dialogues. Previous work adopts incremental methods that take the features from the already predicted discourse relations to help generate the next one. Although the inter-correlations among predictions are considered, we find that the error propagation is also very serious and hurts the overall performance. To alleviate error propagation, we propose a Structure Self-Aware (SSA) model, which adopts a novel edge-centric Graph Neural Network (GNN) to update the information between each Elementary Discourse Unit (EDU) pair layer by layer, so that expressive representations can be learned without historical predictions. In addition, we take auxiliary training signals (e.g. structure distillation) for better representation learning. Our model achieves the new state-of-the-art performances on two conversational discourse parsing benchmarks, largely outperforming the previous methods.
VTC-LFC: Vision Transformer Compression with Low-Frequency Components
Authors: Zhenyu Wang, Hao Luo, Pichao Wang, Feng Ding, Fan Wang, Hao Li
Date: 2022-11-01 00:00:00
Proceeding: NIPS 2022 11

Although Vision transformers (ViTs) have recently dominated many vision tasks, deploying ViT models on resource-limited devices remains a challenging problem. To address such a challenge, several methods have been proposed to compress ViTs. Most of them borrow experience in convolutional neural networks (CNNs) and mainly focus on the spatial domain. However, the compression only in the spatial domain suffers from a dramatic performance drop without fine-tuning and is not robust to noise, as the noise in the spatial domain can easily confuse the pruning criteria, leading to some parameters/channels being pruned incorrectly. Inspired by recent findings that self-attention is a low-pass filter and low-frequency signals/components are more informative to ViTs, this paper proposes compressing ViTs with low-frequency components. Two metrics named low-frequency sensitivity (LFS) and low-frequency energy (LFE) are proposed for better channel pruning and token pruning. Additionally, a bottom-up cascade pruning scheme is applied to compress different dimensions jointly. Extensive experiments demonstrate that the proposed method could save 40% ～ 60% of the FLOPs in ViTs, thus significantly increasing the throughput on practical devices with less than 1% performance drop on ImageNet-1K.