pwc-archive/papers-with-abstracts huggingface dataset:
Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals
Authors: Meng Xiao, Ziyue Qiao, Yanjie Fu, Hao Dong, Yi Du, Pengyang Wang, Dong Li, Yuanchun Zhou
Date: 2022-03-07 00:00:00

The peer merit review of research proposals has been the major mechanism to decide grant awards. Nowadays, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign proposals to appropriate reviewers. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposals. Existing systems mainly collect topic labels manually reported by discipline investigators. However, such human-reported labels can be non-accurate and incomplete. What role can AI play in developing a fair and precise proposal review system? In this evidential study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). We first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs to learn representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.
Is your forecaster smarter than an energy engineer: a deep dive into electricity price forecasting
Authors: Maria Margarida Mascarenhas, Hussain Kazmi
Date: 2022-09-22 00:00:00
Tasks: Decision Making

The field of electricity price forecasting has seen significant advances in the last years, including the development of new, more accurate forecast models. These models leverage statistical relationships in previously observed data to predict the future; however, there is a lack of analysis explaining these models, which limits their real world applicability in critical infrastructure. In this paper, using data from the Belgian electricity markets, we explore a state-of-the-art forecasting model to understand if its predictions can be trusted in more general settings than the limited context it is trained in. If the model produces poor predictions in extreme conditions or if its predictions are inconsistent with reality, it cannot be relied upon in real-world where these forecasts are used in downstream decision-making activities. Our results show that, despite being largely accurate enough in general, even state of the art forecasts struggle with remaining consistent with reality.
Personalized Game Difficulty Prediction Using Factorization Machines
Authors: Jeppe Theiss Kristensen, Christian Guckelsberger, Paolo Burelli, Perttu Hämäläinen
Date: 2022-09-06 00:00:00
Tasks: Diversity, Prediction

The accurate and personalized estimation of task difficulty provides many opportunities for optimizing user experience. However, user diversity makes such difficulty estimation hard, in that empirical measurements from some user sample do not necessarily generalize to others. In this paper, we contribute a new approach for personalized difficulty estimation of game levels, borrowing methods from content recommendation. Using factorization machines (FM) on a large dataset from a commercial puzzle game, we are able to predict difficulty as the number of attempts a player requires to pass future game levels, based on observed attempt counts from earlier levels and levels played by others. In addition to performance and scalability, FMs offer the benefit that the learned latent variable model can be used to study the characteristics of both players and game levels that contribute to difficulty. We compare the approach to a simple non-personalized baseline and a personalized prediction using Random Forests. Our results suggest that FMs are a promising tool enabling game designers to both optimize player experience and learn more about their players and the game.
EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning
Authors: Chang Li, Xuejuan Lin, Yu Liu, Rencheng Song, Juan Cheng, Xun Chen
Date: 2022-09-02 00:00:00
Proceeding: IEEE Sensors Journal 2022 9
Tasks: Contrastive Learning, EEG, Electroencephalogram (EEG), Emotion Recognition

Convolutional neural networks (CNNs) have achieved better performance than traditional algorithms in electroencephalogram (EEG)-based emotion recognition tasks in the recent years. However, as the number of convolution layers increases, the number of network parameters increases sharply. Furthermore, emotional labels are not fully utilized by most supervised learning methods. To achieve a simple and effective model with supervised learning, we propose an efficient CNN and contrastive learning (ECNN-C) method for EEG-based emotion recognition. We utilize a novel convolutional block to replace the standard convolution to reduce the computational burden of the model. In addition, we adopt supervised contrastive learning to make full use of emotion labels, which allows us to pull EEG samples with the same emotional state together, push samples of different emotional state apart from each other at the same time. Experimental results prove the effectiveness of the ECNN-C method for EEG-based emotion recognition.
Sketch-to-Car: Synthesizing Stylized Car Images From Sketches
Authors: Shujia Liu
Date: 2021-11-01 00:00:00
Proceeding: journal 2021 11
Tasks: Generative Adversarial Network, Image Generation

We investigate the inception module as a building block of conditional generative adversarial network (GAN) in training a sketch-to-car model. We demonstrate that this approach is effective at synthesizing photos from edge maps. Experimental results show that, the use of inception modules significantly accelerates the training process of conditional GAN compared to those using convolutional layers, while maintaining a similar frechet inception distance (FID) score.
Streaming Video Temporal Action Segmentation In Real Time
Authors: Wujun Wen, Yunheng Li, Zhuben Dong, Lin Feng, Wanxiao Yang, Shenlan Liu
Date: 2022-09-28 00:00:00
Tasks: Action Segmentation, Language Modelling, Segmentation, Temporal Action Segmentation, Video Understanding

Temporal action segmentation (TAS) is a critical step toward long-term video understanding. Recent studies follow a pattern that builds models based on features instead of raw video picture information. However, we claim those models are trained complicatedly and limit application scenarios. It is hard for them to segment human actions of video in real time because they must work after the full video features are extracted. As the real-time action segmentation task is different from TAS task, we define it as streaming video real-time temporal action segmentation (SVTAS) task. In this paper, we propose a real-time end-to-end multi-modality model for SVTAS task. More specifically, under the circumstances that we cannot get any future information, we segment the current human action of streaming video chunk in real time. Furthermore, the model we propose combines the last steaming video chunk feature extracted by language model with the current image feature extracted by image model to improve the quantity of real-time temporal action segmentation. To the best of our knowledge, it is the first multi-modality real-time temporal action segmentation model. Under the same evaluation criteria as full video temporal action segmentation, our model segments human action in real time with less than 40% of state-of-the-art model computation and achieves 90% of the accuracy of the full video state-of-the-art model.
On The Differences Between Song and Speech Emotion Recognition: Effect of Feature Sets, Feature Types, and Classifiers
Authors: Bagus Tris Atmaja, Masato Akagi
Date: 2020-04-01 00:00:00
Tasks: Emotion Recognition, regression, Speech Emotion Recognition

In this paper, we evaluate the different features sets, feature types, and classifiers on both song and speech emotion recognition. Three feature sets: GeMAPS, pyAudioAnalysis, and LibROSA; two feature types: low-level descriptors and high-level statistical functions; and four classifiers: multilayer perceptron, LSTM, GRU, and convolution neural networks are examined on both song and speech data with the same parameter values. The results show no remarkable difference between song and speech data using the same method. In addition, high-level statistical functions of acoustic features gained higher performance scores than low-level descriptors in this classification task. This result strengthens the previous finding on the regression task which reported the advantage use of high-level features.
Multi-scale Attention Network for Single Image Super-Resolution
Authors: Yan Wang, Yusen Li, Gang Wang, Xiaoguang Liu
Date: 2022-09-28 00:00:00
Tasks: Blocking, Image Super-Resolution, Long-range modeling, Super-Resolution

ConvNets can compete with transformers in high-level tasks by exploiting larger receptive fields. To unleash the potential of ConvNet in super-resolution, we propose a multi-scale attention network (MAN), by coupling classical multi-scale mechanism with emerging large kernel attention. In particular, we proposed multi-scale large kernel attention (MLKA) and gated spatial attention unit (GSAU). Through our MLKA, we modify large kernel attention with multi-scale and gate schemes to obtain the abundant attention map at various granularity levels, thereby aggregating global and local information and avoiding potential blocking artifacts. In GSAU, we integrate gate mechanism and spatial attention to remove the unnecessary linear layer and aggregate informative spatial context. To confirm the effectiveness of our designs, we evaluate MAN with multiple complexities by simply stacking different numbers of MLKA and GSAU. Experimental results illustrate that our MAN can perform on par with SwinIR and achieve varied trade-offs between state-of-the-art performance and computations.
SAVAnet: Surgical Action-Driven Visual Attention Network for Autonomous Endoscope Control
Authors: Huxin Gao, Weichen Fan, Liang Qiu, Xiaoxiao Yang, Zhen Li, Xiuli Zuo, Yanqing Li, Max Q-H Meng, Hongliang Ren
Date: 2022-09-19 00:00:00
Proceeding: IEEE Transactions on Automation Science and Engineering 2022 9

An endoscope holder must understand the detailed surgical actions and the surgeons’ visual attention to keep important targets in the field of endoscopic view during operations. From an intensive analysis of the surgeons’ attention mechanism, we included that surgical actions, like cutting, suturing, etc., play an important role in determining the positions and weights of visual attention points during a dynamic surgical scene. To perform this process, this work proposes a Surgical Action-driven Visual Attention network (SAVAnet) and applies the network in autonomous endoscope control. Four scenarios are constructed in the da Vinci V-rep simulator: pick&place and needle exercise in a general laparoscopic training environment, needle driving with and without obstacle removal in an abdominal cavity, to create datasets for network training. The results show that the network has an outstanding performance in surgical action prediction with a high average accuracy of over 91%. Additionally, with surgical action guidance, the attention point prediction has higher accuracy and accords with surgeons’ visual attention. Finally, the acquired attention points are utilized to execute visual servoing in simulation. The results verify that the SAVAnet is feasible for autonomous endoscope control in real-time and lays a theoretical foundation for future sim-to-real execution. Note to Practitioners —This paper was motivated by the problem of endowing an endoscope with surgeons’ visual attention mechanism, which is affected by surgical actions, for autonomous endoscope control. An eye-tracking device has been utilized to detect surgeon’s visual attention in real-time and then control the endoscope to follow what the surgeon is looking at. However, this approach is susceptible to the surgical environment. Besides, many instrument detection and segmentation algorithms are developed for automatic surgical instrument tracking. However, surgeons’ visual attention does not always focus on the instruments during operations. In this work, we propose a novel SAVAnet to determine visual attention based on surgical actions. We prove from many qualitative and quantitative experiments that surgical actions play a significant role in determining visual attention. The designed SAVAnet can predict surgical actions correctly and then effectively guide the choice of visual attention. Finally, the simulation results show that the SAVAnet can endow endoscope with surgeons’ visual attention to perform self-control in real time. In future research, we will train the SAVAnet using real datasets and conduct more physical experiments on real surgical robots.
What Does DALL-E 2 Know About Radiology?
Authors: Lisa C. Adams, Felix Busch, Daniel Truhn, Marcus R. Makowski, Hugo JWL. Aerts, Keno K. Bressem
Date: 2022-09-27 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation, Zero-Shot Text-to-Image Generation

Generative models such as DALL-E 2 could represent a promising future tool for image generation, augmentation, and manipulation for artificial intelligence research in radiology provided that these models have sufficient medical domain knowledge. Here we show that DALL-E 2 has learned relevant representations of X-ray images with promising capabilities in terms of zero-shot text-to-image generation of new images, continuation of an image beyond its original boundaries, or removal of elements, while pathology generation or CT, MRI, and ultrasound images are still limited. The use of generative models for augmenting and generating radiological data thus seems feasible, even if further fine-tuning and adaptation of these models to the respective domain is required beforehand.
Multilingual Search with Subword TF-IDF
Authors: Artit Wangperawong
Date: 2022-09-28 00:00:00
Tasks: Information Retrieval, Retrieval

Multilingual search can be achieved with subword tokenization. The accuracy of traditional TF-IDF approaches depend on manually curated tokenization, stop words and stemming rules, whereas subword TF-IDF (STF-IDF) can offer higher accuracy without such heuristics. Moreover, multilingual support can be incorporated inherently as part of the subword tokenization model training. XQuAD evaluation demonstrates the advantages of STF-IDF: superior information retrieval accuracy of 85.4% for English and over 80% for 10 other languages without any heuristics-based preprocessing. The software to reproduce these results are open-sourced as a part of Text2Text: https://github.com/artitw/text2text
Label Noise Robustness of Conformal Prediction
Authors: Bat-Sheva Einbinder, Shai Feldman, Stephen Bates, Anastasios N. Angelopoulos, Asaf Gendler, Yaniv Romano
Date: 2022-09-28 00:00:00
Tasks: Conformal Prediction, Prediction, regression, Uncertainty Quantification

We study the robustness of conformal prediction, a powerful tool for uncertainty quantification, to label noise. Our analysis tackles both regression and classification problems, characterizing when and how it is possible to construct uncertainty sets that correctly cover the unobserved noiseless ground truth labels. We further extend our theory and formulate the requirements for correctly controlling a general loss function, such as the false negative proportion, with noisy labels. Our theory and experiments suggest that conformal prediction and risk-controlling techniques with noisy labels attain conservative risk over the clean ground truth labels whenever the noise is dispersive and increases variability. In other adversarial cases, we can also correct for noise of bounded size in the conformal prediction algorithm in order to ensure achieving the correct risk of the ground truth labels without score or data regularity.
Proceedings of the AI-HRI Symposium at AAAI-FSS 2022
Authors: Zhao Han, Emmanuel Senft, Muneeb I. Ahmad, Shelly Bagchi, Amir Yazdani, Jason R. Wilson, Boyoung Kim, Ruchen Wen, Justin W. Hart, Daniel Hernández García, Matteo Leonetti, Ross Mead, Reuth Mirsky, Ahalya Prabhakar, Megan L. Zimmerman
Date: 2022-09-28 00:00:00

The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration on AI theory and methods aimed at HRI since 2014. This year, after a review of the achievements of the AI-HRI community over the last decade in 2021, we are focusing on a visionary theme: exploring the future of AI-HRI. Accordingly, we added a Blue Sky Ideas track to foster a forward-thinking discussion on future research at the intersection of AI and HRI. As always, we appreciate all contributions related to any topic on AI/HRI and welcome new researchers who wish to take part in this growing community. With the success of past symposia, AI-HRI impacts a variety of communities and problems, and has pioneered the discussions in recent trends and interests. This year's AI-HRI Fall Symposium aims to bring together researchers and practitioners from around the globe, representing a number of university, government, and industry laboratories. In doing so, we hope to accelerate research in the field, support technology transition and user adoption, and determine future directions for our group and our research.
Audio Retrieval with WavText5K and CLAP Training
Authors: Soham Deshmukh, Benjamin Elizalde, Huaming Wang
Date: 2022-09-28 00:00:00
Tasks: AudioCaps, Audio captioning, Contrastive Learning, Retrieval, Text Retrieval

Audio-Text retrieval takes a natural language query to retrieve relevant audio files in a database. Conversely, Text-Audio retrieval takes an audio file as a query to retrieve relevant natural language descriptions. Most of the literature train retrieval systems with one audio captioning dataset, but evaluating the benefit of training with multiple datasets is underexplored. Moreover, retrieval systems have to learn the alignment between elaborated sentences describing audio content of variable length ranging from a few seconds to several minutes. In this work, we propose a new collection of web audio-text pairs and a new framework for retrieval. First, we provide a new collection of about five thousand web audio-text pairs that we refer to as WavText5K. When used to train our retrieval system, WavText5K improved performance more than other audio captioning datasets. Second, our framework learns to connect language and audio content by using a text encoder, two audio encoders, and a contrastive learning objective. Combining both audio encoders helps to process variable length audio. The two contributions beat state of the art performance for AudioCaps and Clotho on Text-Audio retrieval by a relative 2% and 16%, and Audio-Text retrieval by 6% and 23%.
Physics-aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks
Authors: Yingjie Li, Ruiyang Chen, Weilu Gao, Cunxi Yu
Date: 2022-09-28 00:00:00
Tasks: Quantization

Diffractive optical neural networks (DONNs) have attracted lots of attention as they bring significant advantages in terms of power efficiency, parallelism, and computational speed compared with conventional deep neural networks (DNNs), which have intrinsic limitations when implemented on digital platforms. However, inversely mapping algorithm-trained physical model parameters onto real-world optical devices with discrete values is a non-trivial task as existing optical devices have non-unified discrete levels and non-monotonic properties. This work proposes a novel device-to-system hardware-software codesign framework, which enables efficient physics-aware training of DONNs w.r.t arbitrary experimental measured optical devices across layers. Specifically, Gumbel-Softmax is employed to enable differentiable discrete mapping from real-world device parameters into the forward function of DONNs, where the physical parameters in DONNs can be trained by simply minimizing the loss function of the ML task. The results have demonstrated that our proposed framework offers significant advantages over conventional quantization-based methods, especially with low-precision optical devices. Finally, the proposed algorithm is fully verified with physical experimental optical systems in low-precision settings.
Online Subset Selection using $α$-Core with no Augmented Regret
Authors: Sourav Sahoo, Siddhant Chaudhary, Samrat Mukhopadhyay, Abhishek Sinha
Date: 2022-09-28 00:00:00

We revisit the classic problem of optimal subset selection in the online learning set-up. Assume that the set $[N]$ consists of $N$ distinct elements. On the $t$th round, an adversary chooses a monotone reward function $f_t: 2^{[N]} \to \mathbb{R}_+$ that assigns a non-negative reward to each subset of $[N].$ An online policy selects (perhaps randomly) a subset $S_t \subseteq [N]$ consisting of $k$ elements before the reward function $f_t$ for the $t$th round is revealed to the learner. As a consequence of its choice, the policy receives a reward of $f_t(S_t)$ on the $t$th round. Our goal is to design an online sequential subset selection policy to maximize the expected cumulative reward accumulated over a time horizon. In this connection, we propose an online learning policy called SCore (Subset Selection with Core) that solves the problem for a large class of reward functions. The proposed SCore policy is based on a new polyhedral characterization of the reward functions called $\alpha$-Core - a generalization of Core from the cooperative game theory literature. We establish a learning guarantee for the SCore policy in terms of a new performance metric called $\alpha$-augmented regret. In this new metric, the performance of the online policy is compared with an unrestricted offline benchmark that can select all $N$ elements at every round. We show that a large class of reward functions, including submodular, can be efficiently optimized with the SCore policy. We also extend the proposed policy to the optimistic learning set-up where the learner has access to additional untrusted hints regarding the reward functions. Finally, we conclude the paper with a list of open problems.