pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning
Authors: Cristina Luna-Jiménez, David Griol, Zoraida Callejas, Ricardo Kleinlein, Juan M. Montero, Fernando Fernández-Martínez
Date: 2021-11-18 00:00:00
Proceeding: Sensors 2021 11
Tasks: Domain Adaptation, Emotion Recognition, Facial Emotion Recognition, Multimodal Emotion Recognition, Speech Emotion Recognition, Transfer Learning

Emotion Recognition is attracting the attention of the research community due to the multiple areas where it can be applied, such as in healthcare or in road safety systems. In this paper, we propose a multimodal emotion recognition system that relies on speech and facial information. For the speech-based modality, we evaluated several transfer-learning techniques, more specifically, embedding extraction and Fine-Tuning. The best accuracy results were achieved when we fine-tuned the CNN-14 of the PANNs framework, confirming that the training was more robust when it did not start from scratch and the tasks were similar. Regarding the facial emotion recognizers, we propose a framework that consists of a pre-trained Spatial Transformer Network on saliency maps and facial images followed by a bi-LSTM with an attention mechanism. The error analysis reported that the frame-based systems could present some problems when they were used directly to solve a video-based task despite the domain adaptation, which opens a new line of research to discover new ways to correct this mismatch and take advantage of the embedded knowledge of these pre-trained models. Finally, from the combination of these two modalities with a late fusion strategy, we achieved 80.08% accuracy on the RAVDESS dataset on a subject-wise 5-CV evaluation, classifying eight emotions. The results revealed that these modalities carry relevant information to detect users’ emotional state and their combination enables improvement of system performance.
Binary Diffing as a Network Alignment Problem via Belief Propagation
Authors: Elie Mengin, Fabrice Rossi
Date: 2021-12-31 00:00:00

In this paper, we address the problem of finding a correspondence, or matching, between the functions of two programs in binary form, which is one of the most common task in binary diffing. We introduce a new formulation of this problem as a particular instance of a graph edit problem over the call graphs of the programs. In this formulation, the quality of a mapping is evaluated simultaneously with respect to both function content and call graph similarities. We show that this formulation is equivalent to a network alignment problem. We propose a solving strategy for this problem based on max-product belief propagation. Finally, we implement a prototype of our method, called QBinDiff, and propose an extensive evaluation which shows that our approach outperforms state of the art diffing tools.
An Intelligent Self-driving Truck System For Highway Transportation
Authors: Dawei Wang, Lingping Gao, Ziquan Lan, Wei Li, Jiaping Ren, Jiahui Zhang, Peng Zhang, Pei Zhou, Shengao Wang, Jia Pan, Dinesh Manocha, Ruigang Yang
Date: 2021-12-31 00:00:00
Tasks: Autonomous Driving, Decision Making

Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing works mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this paper, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real-world deployment, 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which shows our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS
SimSR: Simple Distance-based State Representation for Deep Reinforcement Learning
Authors: Hongyu Zang, Xin Li, Mingzhong Wang
Date: 2021-12-31 00:00:00
Tasks: Deep Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This work explores how to learn robust and generalizable state representation from image-based observations with deep reinforcement learning methods. Addressing the computational complexity, stringent assumptions and representation collapse challenges in existing work of bisimulation metric, we devise Simple State Representation (SimSR) operator. SimSR enables us to design a stochastic approximation method that can practically learn the mapping functions (encoders) from observations to latent representation space. In addition to the theoretical analysis and comparison with the existing work, we experimented and compared our work with recent state-of-the-art solutions in visual MuJoCo tasks. The results shows that our model generally achieves better performance and has better robustness and good generalization.
What is Event Knowledge Graph: A Survey
Authors: Saiping Guan, Xueqi Cheng, Long Bai, Fujun Zhang, Zixuan Li, Yutao Zeng, Xiaolong Jin, Jiafeng Guo
Date: 2021-12-31 00:00:00
Tasks: Question Answering, Survey, Text Generation

Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are also an essential kind of knowledge in the world, which trigger the spring up of event-centric knowledge representation form like Event KG (EKG). It plays an increasingly important role in many downstream applications, such as search, question-answering, recommendation, financial quantitative investments, and text generation. This paper provides a comprehensive survey of EKG from history, ontology, instance, and application views. Specifically, to characterize EKG thoroughly, we focus on its history, definition, schema induction, acquisition, related representative graphs/systems, and applications. The development processes and trends are studied therein. We further summarize prospective directions to facilitate future research on EKG.
比利时根特大学Zeger教授招聘中国国家留学基金委（CSC）联合培养博士生
Authors: 比利时根特大学
Date: 2022-01-01 00:00:00
Proceeding: Ghent university 2022 1

比利时根特大学Zeger教授招聘中国国家留学基金委（CSC）联合培养博士生

01  合作导师简介
Prof. Zeger Hens，比利时根特大学（Ghent University）终身教授，博士生导师。Zeger教授致力于量子点材料的合成、结构表征、物理化学特性与光电器件研究。在Science, Nature materials, Nature nanotechnology, JACS, Advanced Materials, Nano letters, ACS nano等期刊发表论文数百篇。总引用超过15800次，H因子65。
个人主页：https://www.nano.ugent.be/content/people
导师联系方式：Zeger.Hens@UGent.be 

02 主要研究方向
1.胶体量子点光电器件（光电探测器、LED等）。
2.胶体量子点的设计、合成与物理化学性质研究。

03 招聘要求
1.学生已经或者即将取得硕士学位。
2.具有光电器件、材料物理化学或者材料合成相关领域科研背景。
3.具有扎实的专业基础、丰富的实验经验、独立的科研能力、较强英文写作能力。
4.尊重欧洲的文化与价值观，能够友好地与不同国籍、肤色、文化、信仰的同事平等地合作。

04 福利待遇
1.对符合条件学生申请CSC 奖学金联合培养资助（1350欧元/月），并协助学生申请根特大学奖学金（310欧元/月）。
2.配备全新笔记本电脑（约6500元人民币），办公室每日免费咖啡、巧克力与牛奶。
3.课题组实行8小时工作制、周末双休、法定节假日全休。
4.比利时自行车绿色出行补贴（0.25欧元/公里）、公交出行报销福利等。
4.享有比利时带薪假期、免费医疗、子女免费教育以及生育津贴等福利。

05 课题组文化
1.学生不承担任何发票、财务报销等工作，由实验室全职秘书负责。
2.平等与尊重，导师从不凶或者骂学生。
3.每周日常组会，每月专题组会，一对一讨论，导师给予学生全面指导。
4.每天8小时工作后，周末以及法定节假日，导师不干扰学生，不给学生安排工作。
5.放假前主动提醒、不支持也不反对自主加班、课题组日常drinking、party氛围融洽。
6.课题组主页：https://www.nano.ugent.be/  

请感兴趣并且符合条件的学生将简历、成绩单通过Email（Zeger.Hens@UGent.be）发送到Zeger教授，Zeger教授将立刻组织面试，并协助CSC基金申请。
Learning Agent State Online with Recurrent Generate-and-Test
Authors: Amir Samani, Richard S. Sutton
Date: 2021-12-30 00:00:00

Learning continually and online from a continuous stream of data is challenging, especially for a reinforcement learning agent with sequential data. When the environment only provides observations giving partial information about the state of the environment, the agent must learn the agent state based on the data stream of experience. We refer to the state learned directly from the data stream of experience as the agent state. Recurrent neural networks can learn the agent state, but the training methods are computationally expensive and sensitive to the hyper-parameters, making them unideal for online learning. This work introduces methods based on the generate-and-test approach to learn the agent state. A generate-and-test algorithm searches for state features by generating features and testing their usefulness. In this process, features useful for the agent's performance on the task are preserved, and the least useful features get replaced with newly generated features. We study the effectiveness of our methods on two online multi-step prediction problems. The first problem, trace conditioning, focuses on the agent's ability to remember a cue for a prediction multiple steps into the future. In the second problem, trace patterning, the agent needs to learn patterns in the observation signals and remember them for future predictions. We show that our proposed methods can effectively learn the agent state online and produce accurate predictions.
Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning
Authors: Tong Mu, Georgios Theocharous, David Arbour, Emma Brunskill
Date: 2021-12-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Online reinforcement learning (RL) algorithms are often difficult to deploy in complex human-facing applications as they may learn slowly and have poor early performance. To address this, we introduce a practical algorithm for incorporating human insight to speed learning. Our algorithm, Constraint Sampling Reinforcement Learning (CSRL), incorporates prior domain knowledge as constraints/restrictions on the RL policy. It takes in multiple potential policy constraints to maintain robustness to misspecification of individual constraints while leveraging helpful ones to learn quickly. Given a base RL learning algorithm (ex. UCRL, DQN, Rainbow) we propose an upper confidence with elimination scheme that leverages the relationship between the constraints, and their observed performance, to adaptively switch among them. We instantiate our algorithm with DQN-type algorithms and UCRL as base algorithms, and evaluate our algorithm in four environments, including three simulators based on real data: recommendations, educational activity sequencing, and HIV treatment sequencing. In all cases, CSRL learns a good policy faster than baselines.
Chatbot for fitness management using IBM Watson
Authors: Sai Rugved Lola, Rahul Dhadvai, Wei Wang, Ting Zhu
Date: 2021-12-30 00:00:00
Tasks: Chatbot, Cloud Computing, Management, Natural Language Understanding

Chatbots have revolutionized the way humans interact with computer systems and they have substituted the use of service agents, call-center representatives etc. Fitness industry has always been a growing industry although it has not adapted to the latest technologies like AI, ML and cloud computing. In this paper, we propose an idea to develop a chatbot for fitness management using IBM Watson and integrate it with a web application. We proposed using Natural Language Processing (NLP) and Natural Language Understanding (NLU) along with frameworks of IBM Cloud Watson provided for the Chatbot Assistant. This software uses a serverless architecture to combine the services of a professional by offering diet plans, home exercises, interactive counseling sessions, fitness recommendations.
From Behavioral Theories to Econometrics: Inferring Preferences of Human Agents from Data on Repeated Interactions
Authors: Gali Noti
Date: 2021-12-30 00:00:00
Tasks: Econometrics

We consider the problem of estimating preferences of human agents from data of strategic systems where the agents repeatedly interact. Recently, it was demonstrated that a new estimation method called "quantal regret" produces more accurate estimates for human agents than the classic approach that assumes that agents are rational and reach a Nash equilibrium; however, this method has not been compared to methods that take into account behavioral aspects of human play. In this paper we leverage equilibrium concepts from behavioral economics for this purpose and ask how well they perform compared to the quantal regret and Nash equilibrium methods. We develop four estimation methods based on established behavioral equilibrium models to infer the utilities of human agents from observed data of normal-form games. The equilibrium models we study are quantal-response equilibrium, action-sampling equilibrium, payoff-sampling equilibrium, and impulse-balance equilibrium. We show that in some of these concepts the inference is achieved analytically via closed formulas, while in the others the inference is achieved only algorithmically. We use experimental data of 2x2 games to evaluate the estimation success of these behavioral equilibrium methods. The results show that the estimates they produce are more accurate than the estimates of the Nash equilibrium. The comparison with the quantal-regret method shows that the behavioral methods have better hit rates, but the quantal-regret method performs better in terms of the overall mean squared error, and we discuss the differences between the methods.
Aim in Climate Change and City Pollution
Authors: Pablo Torres, Beril Sirmacek, Sergio Hoyas, Ricardo Vinuesa
Date: 2021-12-30 00:00:00
Tasks: BIG-bench Machine Learning

The sustainability of urban environments is an increasingly relevant problem. Air pollution plays a key role in the degradation of the environment as well as the health of the citizens exposed to it. In this chapter we provide a review of the methods available to model air pollution, focusing on the application of machine-learning methods. In fact, machine-learning methods have proved to importantly increase the accuracy of traditional air-pollution approaches while limiting the development cost of the models. Machine-learning tools have opened new approaches to study air pollution, such as flow-dynamics modelling or remote-sensing methodologies.
Bayesian Algorithms Learn to Stabilize Unknown Continuous-Time Systems
Authors: Mohamad Kazem Shirani Faradonbeh, Mohamad Sadegh Shirani Faradonbeh
Date: 2021-12-30 00:00:00

Linear dynamical systems are canonical models for learning-based control of plants with uncertain dynamics. The setting consists of a stochastic differential equation that captures the state evolution of the plant understudy, while the true dynamics matrices are unknown and need to be learned from the observed data of state trajectory. An important issue is to ensure that the system is stabilized and destabilizing control actions due to model uncertainties are precluded as soon as possible. A reliable stabilization procedure for this purpose that can effectively learn from unstable data to stabilize the system in a finite time is not currently available. In this work, we propose a novel Bayesian learning algorithm that stabilizes unknown continuous-time stochastic linear systems. The presented algorithm is flexible and exposes effective stabilization performance after a remarkably short time period of interacting with the system.
Leveraging in-domain supervision for unsupervised image-to-image translation tasks via multi-stream generators
Authors: Dvir Yerushalmi, Dov Danon, Amit H. Bermano
Date: 2021-12-30 00:00:00
Tasks: Image-to-Image Translation, Segmentation, Semantic Segmentation, Translation, Unsupervised Image-To-Image Translation

Supervision for image-to-image translation (I2I) tasks is hard to come by, but bears significant effect on the resulting quality. In this paper, we observe that for many Unsupervised I2I (UI2I) scenarios, one domain is more familiar than the other, and offers in-domain prior knowledge, such as semantic segmentation. We argue that for complex scenes, figuring out the semantic structure of the domain is hard, especially with no supervision, but is an important part of a successful I2I operation. We hence introduce two techniques to incorporate this invaluable in-domain prior knowledge for the benefit of translation quality: through a novel Multi-Stream generator architecture, and through a semantic segmentation-based regularization loss term. In essence, we propose splitting the input data according to semantic masks, explicitly guiding the network to different behavior for the different regions of the image. In addition, we propose training a semantic segmentation network along with the translation task, and to leverage this output as a loss term that improves robustness. We validate our approach on urban data, demonstrating superior quality in the challenging UI2I tasks of converting day images to night ones. In addition, we also demonstrate how reinforcing the target dataset with our augmented images improves the training of downstream tasks such as the classical detection one.
Causal Attention for Interpretable and Generalizable Graph Classification
Authors: Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He, Tat-Seng Chua
Date: 2021-12-30 00:00:00
Tasks: Classification, Graph Attention, Graph Classification, Prediction

In graph classification, attention and pooling-based graph neural networks (GNNs) prevail to extract the critical features from the input graph and support the prediction. They mostly follow the paradigm of learning to attend, which maximizes the mutual information between the attended graph and the ground-truth label. However, this paradigm makes GNN classifiers recklessly absorb all the statistical correlations between input features and labels in the training data, without distinguishing the causal and noncausal effects of features. Instead of underscoring the causal features, the attended graphs are prone to visit the noncausal features as the shortcut to predictions. Such shortcut features might easily change outside the training distribution, thereby making the GNN classifiers suffer from poor generalization. In this work, we take a causal look at the GNN modeling for graph classification. With our causal assumption, the shortcut feature serves as a confounder between the causal feature and prediction. It tricks the classifier to learn spurious correlations that facilitate the prediction in in-distribution (ID) test evaluation, while causing the performance drop in out-of-distribution (OOD) test data. To endow the classifier with better interpretation and generalization, we propose the Causal Attention Learning (CAL) strategy, which discovers the causal patterns and mitigates the confounding effect of shortcuts. Specifically, we employ attention modules to estimate the causal and shortcut features of the input graph. We then parameterize the backdoor adjustment of causal theory -- combine each causal feature with various shortcut features. It encourages the stable relationships between the causal estimation and prediction, regardless of the changes in shortcut parts and distributions. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of CAL.
ChunkFormer: Learning Long Time Series with Multi-stage Chunked Transformer
Authors: Yue Ju, Alka Isac, Yimin Nie
Date: 2021-12-30 00:00:00
Tasks: Time Series, Time Series Analysis

The analysis of long sequence data remains challenging in many real-world applications. We propose a novel architecture, ChunkFormer, that improves the existing Transformer framework to handle the challenges while dealing with long time series. Original Transformer-based models adopt an attention mechanism to discover global information along a sequence to leverage the contextual data. Long sequential data traps local information such as seasonality and fluctuations in short data sequences. In addition, the original Transformer consumes more resources by carrying the entire attention matrix during the training course. To overcome these challenges, ChunkFormer splits the long sequences into smaller sequence chunks for the attention calculation, progressively applying different chunk sizes in each stage. In this way, the proposed model gradually learns both local and global information without changing the total length of the input sequences. We have extensively tested the effectiveness of this new architecture on different business domains and have proved the advantage of such a model over the existing Transformer-based models.
Pose Estimation of Specific Rigid Objects
Authors: Tomas Hodan
Date: 2021-12-30 00:00:00
Tasks: 6D Pose Estimation using RGB, Autonomous Driving, Object, Pose Estimation

In this thesis, we address the problem of estimating the 6D pose of rigid objects from a single RGB or RGB-D input image, assuming that 3D models of the objects are available. This problem is of great importance to many application fields such as robotic manipulation, augmented reality, and autonomous driving. First, we propose EPOS, a method for 6D object pose estimation from an RGB image. The key idea is to represent an object by compact surface fragments and predict the probability distribution of corresponding fragments at each pixel of the input image by a neural network. Each pixel is linked with a data-dependent number of fragments, which allows systematic handling of symmetries, and the 6D poses are estimated from the links by a RANSAC-based fitting method. EPOS outperformed all RGB and most RGB-D and D methods on several standard datasets. Second, we present HashMatch, an RGB-D method that slides a window over the input image and searches for a match against templates, which are pre-generated by rendering 3D object models in different orientations. The method applies a cascade of evaluation stages to each window location, which avoids exhaustive matching against all templates. Third, we propose ObjectSynth, an approach to synthesize photorealistic images of 3D object models for training methods based on neural networks. The images yield substantial improvements compared to commonly used images of objects rendered on top of random photographs. Fourth, we introduce T-LESS, the first dataset for 6D object pose estimation that includes 3D models and RGB-D images of industry-relevant objects. Fifth, we define BOP, a benchmark that captures the status quo in the field. BOP comprises eleven datasets in a unified format, an evaluation methodology, an online evaluation system, and public challenges held at international workshops organized at the ICCV and ECCV conferences.
An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation
Authors: Shuyin Xia, Xinyu Bai, Guoyin Wang, Deyu Meng, Xinbo Gao, Zizhong Chen, Elisabeth Giem
Date: 2021-12-29 00:00:00
Tasks: Attribute, feature selection

This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time. Rough set has good interpretability, and is a popular method for feature selections. But low efficiency and low accuracy are its main drawbacks that limits its application ability. In this paper,corresponding to the accuracy, we first find the ineffectiveness of rough set because of overfitting, especially in processing noise attribute, and propose a robust measurement for an attribute, called relative importance.we proposed the concept of "rough concept tree" for knowledge representation and classification. Experimental results on public benchmark data sets show that the proposed framework achieves higher accurcy than seven popular or the state-of-the-art feature selection methods.