pwc-archive/papers-with-abstracts huggingface dataset:
Video Sentiment Analysis with Bimodal Information-augmented Multi-Head Attention
Authors: Ting Wu, Junjie Peng, Wenqiang Zhang, Huiran Zhang, Chuanshuai Ma, Yansong Huang
Date: 2021-03-03 00:00:00
Tasks: Sentiment Analysis, Time Series Analysis

Humans express feelings or emotions via different channels. Take language as an example, it entails different sentiments under different visual-acoustic contexts. To precisely understand human intentions as well as reduce the misunderstandings caused by ambiguity and sarcasm, we should consider multimodal signals including textual, visual and acoustic signals. The crucial challenge is to fuse different modalities of features for sentiment analysis. To effectively fuse the information carried by different modalities and better predict the sentiments, we design a novel multi-head attention based fusion network, which is inspired by the observations that the interactions between any two pair-wise modalities are different and they do not equally contribute to the final sentiment prediction. By assigning the acoustic-visual, acoustic-textual and visual-textual features with reasonable attention and exploiting a residual structure, we attend to attain the significant features. We conduct extensive experiments on four public multimodal datasets including one in Chinese and three in English. The results show that our approach outperforms the existing methods and can explain the contributions of bimodal interaction in multiple modalities.
Reinforcement Learning with External Knowledge by using Logical Neural Networks
Authors: Daiki Kimura, Subhajit Chaudhury, Akifumi Wachi, Ryosuke Kohita, Asim Munawar, Michiaki Tatsubori, Alexander Gray
Date: 2021-03-03 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Conventional deep reinforcement learning methods are sample-inefficient and usually require a large number of training trials before convergence. Since such methods operate on an unconstrained action set, they can lead to useless actions. A recent neuro-symbolic framework called the Logical Neural Networks (LNNs) can simultaneously provide key-properties of both neural networks and symbolic logic. The LNNs functions as an end-to-end differentiable network that minimizes a novel contradiction loss to learn interpretable rules. In this paper, we utilize LNNs to define an inference graph using basic logical operations, such as AND and NOT, for faster convergence in reinforcement learning. Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide. Our results empirically demonstrate that our method converges faster compared to a model-free reinforcement learning method that doesn't have such logical constraints.
Correct-by-construction reach-avoid control of partially observable linear stochastic systems
Authors: Thom Badings, Hasan A. Poonawala, Marielle Stoelinga, Nils Jansen
Date: 2021-03-03 00:00:00
Tasks: Continuous Control

We study feedback controller synthesis for reach-avoid control of discrete-time, linear time-invariant (LTI) systems with Gaussian process and measurement noise. The problem is to compute a controller such that, with at least some required probability, the system reaches a desired goal state in finite time while avoiding unsafe states. Due to stochasticity and nonconvexity, this problem does not admit exact algorithmic or closed-form solutions in general. Our key contribution is a correct-by-construction controller synthesis scheme based on a finite-state abstraction of a Gaussian belief over the unmeasured state, obtained using a Kalman filter. We formalize this abstraction as a Markov decision process (MDP). To be robust against numerical imprecision in approximating transition probabilities, we use MDPs with intervals of transition probabilities. By construction, any policy on the abstraction can be refined into a piecewise linear feedback controller for the LTI system. We prove that the closed-loop LTI system under this controller satisfies the reach-avoid problem with at least the required probability. The numerical experiments show that our method is able to solve reach-avoid problems for systems with up to 6D state spaces, and with control input constraints that cannot be handled by methods such as the rapidly-exploring random belief trees (RRBT).
Adversarial Environment Generation for Learning to Navigate the Web
Authors: Izzeddin Gur, Natasha Jaques, Kevin Malta, Manoj Tiwari, Honglak Lee, Aleksandra Faust
Date: 2021-03-02 00:00:00
Tasks: Benchmarking, Decision Making, Navigate, Reinforcement Learning (RL), Sequential Decision Making

Learning to autonomously navigate the web is a difficult sequential decision making task. The state and action spaces are large and combinatorial in nature, and websites are dynamic environments consisting of several pages. One of the bottlenecks of training web navigation agents is providing a learnable curriculum of training environments that can cover the large variety of real-world websites. Therefore, we propose using Adversarial Environment Generation (AEG) to generate challenging web environments in which to train reinforcement learning (RL) agents. We provide a new benchmarking environment, gMiniWoB, which enables an RL adversary to use compositional primitives to learn to generate arbitrarily complex websites. To train the adversary, we propose a new technique for maximizing regret using the difference in the scores obtained by a pair of navigator agents. Our results show that our approach significantly outperforms prior methods for minimax regret AEG. The regret objective trains the adversary to design a curriculum of environments that are "just-the-right-challenge" for the navigator agents; our results show that over time, the adversary learns to generate increasingly complex web navigation tasks. The navigator agents trained with our technique learn to complete challenging, high-dimensional web navigation tasks, such as form filling, booking a flight etc. We show that the navigator agent trained with our proposed Flexible b-PAIRED technique significantly outperforms competitive automatic curriculum generation baselines -- including a state-of-the-art RL web navigation approach -- on a set of challenging unseen test environments, and achieves more than 80% success rate on some tasks.
Minimax Model Learning
Authors: Cameron Voloshin, Nan Jiang, Yisong Yue
Date: 2021-03-02 00:00:00
Tasks: model, Model-based Reinforcement Learning, Off-policy evaluation, reinforcement-learning, Reinforcement Learning (RL)

We present a novel off-policy loss function for learning a transition model in model-based reinforcement learning. Notably, our loss is derived from the off-policy policy evaluation objective with an emphasis on correcting distribution shift. Compared to previous model-based techniques, our approach allows for greater robustness under model misspecification or distribution shift induced by learning/evaluating policies that are distinct from the data-generating policy. We provide a theoretical analysis and show empirical improvements over existing model-based off-policy evaluation methods. We provide further analysis showing our loss can be used for off-policy optimization (OPO) and demonstrate its integration with more recent improvements in OPO.
Two-Stage Framework for Seasonal Time Series Forecasting
Authors: Qingyang Xu, Qingsong Wen, Liang Sun
Date: 2021-03-03 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting, Vocal Bursts Valence Prediction

Seasonal time series Forecasting remains a challenging problem due to the long-term dependency from seasonality. In this paper, we propose a two-stage framework to forecast univariate seasonal time series. The first stage explicitly learns the long-range time series structure in a time window beyond the forecast horizon. By incorporating the learned long-range structure, the second stage can enhance the prediction accuracy in the forecast horizon. In both stages, we integrate the auto-regressive model with neural networks to capture both linear and non-linear characteristics in time series. Our framework achieves state-of-the-art performance on M4 Competition Hourly datasets. In particular, we show that incorporating the intermediate results generated in the first stage to existing forecast models can effectively enhance their prediction performance.
Inference-Based Deterministic Messaging For Multi-Agent Communication
Authors: Varun Bhatt, Michael Buro
Date: 2021-03-03 00:00:00

Communication is essential for coordination among humans and animals. Therefore, with the introduction of intelligent agents into the world, agent-to-agent and agent-to-human communication becomes necessary. In this paper, we first study learning in matrix-based signaling games to empirically show that decentralized methods can converge to a suboptimal policy. We then propose a modification to the messaging policy, in which the sender deterministically chooses the best message that helps the receiver to infer the sender's observation. Using this modification, we see, empirically, that the agents converge to the optimal policy in nearly all the runs. We then apply this method to a partially observable gridworld environment which requires cooperation between two agents and show that, with appropriate approximation methods, the proposed sender modification can enhance existing decentralized training methods for more complex domains as well.
Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network
Authors: Zhen Fu, Bo wang, Xihong Wu, Jing Chen
Date: 2021-03-03 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, regression

The auditory attention decoding (AAD) approach was proposed to determine the identity of the attended talker in a multi-talker scenario by analyzing electroencephalography (EEG) data. Although the linear model-based method has been widely used in AAD, the linear assumption was considered oversimplified and the decoding accuracy remained lower for shorter decoding windows. Recently, nonlinear models based on deep neural networks (DNN) have been proposed to solve this problem. However, these models did not fully utilize both the spatial and temporal features of EEG, and the interpretability of DNN models was rarely investigated. In this paper, we proposed novel convolutional recurrent neural network (CRNN) based regression model and classification model, and compared them with both the linear model and the state-of-the-art DNN models. Results showed that, our proposed CRNN-based classification model outperformed others for shorter decoding windows (around 90% for 2 s and 5 s). Although worse than classification models, the decoding accuracy of the proposed CRNN-based regression model was about 5% greater than other regression models. The interpretability of DNN models was also investigated by visualizing layers' weight.
Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment
Authors: Young-Eun Lee, Seong-Whan Lee
Date: 2021-03-03 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography (EEG) signals are distorted by movement artifacts and electromyography signals when users are moving, which make hard to recognize human intention. In addition, as hardware issues are also challenging, ear-EEG has been developed for practical brain-computer interface and has been widely used. In this paper, we proposed ensemble-based convolutional neural networks in ambulatory environment and analyzed the visual event-related potential responses in scalp- and ear-EEG in terms of statistical analysis and brain-computer interface performance. The brain-computer interface performance deteriorated as 3-14% when walking fast at 1.6 m/s. The proposed methods showed 0.728 in average of the area under the curve. The proposed method shows robust to the ambulatory environment and imbalanced data as well.
LQResNet: A Deep Neural Network Architecture for Learning Dynamic Processes
Authors: Pawan Goyal, Peter Benner
Date: 2021-03-03 00:00:00

Mathematical modeling is an essential step, for example, to analyze the transient behavior of a dynamical process and to perform engineering studies such as optimization and control. With the help of first-principles and expert knowledge, a dynamic model can be built, but for complex dynamic processes, appearing, e.g., in biology, chemical plants, neuroscience, financial markets, this often remains an onerous task. Hence, data-driven modeling of the dynamics process becomes an attractive choice and is supported by the rapid advancement in sensor and measurement technology. A data-driven approach, namely operator inference framework, models a dynamic process, where a particular structure of the nonlinear term is assumed. In this work, we suggest combining the operator inference with certain deep neural network approaches to infer the unknown nonlinear dynamics of the system. The approach uses recent advancements in deep learning and possible prior knowledge of the process if possible. We also briefly discuss several extensions and advantages of the proposed methodology. We demonstrate that the proposed methodology accomplishes the desired tasks for dynamics processes encountered in neural dynamics and the glycolytic oscillator.
Automatically detecting the conflicts between software requirements based on finer semantic analysis
Authors: Weize Guo, Li Zhang, Xiaoli Lian
Date: 2021-03-03 00:00:00

Context: Conflicts between software requirements bring uncertainties to product development. Some great approaches have been proposed to identify these conflicts. However, they usually require the software requirements represented with specific templates and/or depend on other external source which is often uneasy to build for lots of projects in practice. Objective: We aim to propose an approach Finer Semantic Analysis-based Requirements Conflict Detector (FSARC) to automatically detecting the conflicts between the given natural language functional requirements by analyzing their finer semantic compositions. Method: We build a harmonized semantic meta-model of functional requirements with the form of eight-tuple. Then we propose algorithms to automatically analyze the linguistic features of requirements and to annotate the semantic elements for their semantic model construction. And we define seven types of conflicts as long as their heuristic detecting rules on the ground of their text pattern and semantical dependency. Finally, we design and implement the algorithm for conflicts detection. Results: The experiment with four requirement datasets illustrates that the recall of FSARC is nearly 100% and the average precision is 83.88% on conflicts detection. Conclusion: We provide a useful tool for detecting the conflicts between natural language functional requirements to improve the quality of the final requirements set. Besides, our approach is capable of transforming the natural language functional requirements into eight semantic tuples, which is useful not only the detection of the conflicts between requirements but also some other tasks such as constructing the association between requirements and so on.
Evaluating Robustness of Counterfactual Explanations
Authors: André Artelt, Valerie Vaquet, Riza Velioglu, Fabian Hinder, Johannes Brinkrolf, Malte Schilling, Barbara Hammer
Date: 2021-03-03 00:00:00
Tasks: counterfactual, Decision Making, Fairness

Transparency is a fundamental requirement for decision making systems when these should be deployed in the real world. It is usually achieved by providing explanations of the system's behavior. A prominent and intuitive type of explanations are counterfactual explanations. Counterfactual explanations explain a behavior to the user by proposing actions -- as changes to the input -- that would cause a different (specified) behavior of the system. However, such explanation methods can be unstable with respect to small changes to the input -- i.e. even a small change in the input can lead to huge or arbitrary changes in the output and of the explanation. This could be problematic for counterfactual explanations, as two similar individuals might get very different explanations. Even worse, if the recommended actions differ considerably in their complexity, one would consider such unstable (counterfactual) explanations as individually unfair. In this work, we formally and empirically study the robustness of counterfactual explanations in general, as well as under different models and different kinds of perturbations. Furthermore, we propose that plausible counterfactual explanations can be used instead of closest counterfactual explanations to improve the robustness and consequently the individual fairness of counterfactual explanations.
Root cause prediction based on bug reports
Authors: Thomas Hirsch, Birgit Hofer
Date: 2021-03-03 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

This paper proposes a supervised machine learning approach for predicting the root cause of a given bug report. Knowing the root cause of a bug can help developers in the debugging process - either directly or indirectly by choosing proper tool support for the debugging task. We mined 54755 closed bug reports from the issue trackers of 103 GitHub projects and applied a set of heuristics to create a benchmark consisting of 10459 reports. A subset was manually classified into three groups (semantic, memory, and concurrency) based on the bugs' root causes. Since the types of root cause are not equally distributed, a combination of keyword search and random selection was applied. Our data set for the machine learning approach consists of 369 bug reports (122 concurrency, 121 memory, and 126 semantic bugs). The bug reports are used as input to a natural language processing algorithm. We evaluated the performance of several classifiers for predicting the root causes for the given bug reports. Linear Support Vector machines achieved the highest mean precision (0.74) and recall (0.72) scores. The created bug data set and classification are publicly available.
Continuous Speech Separation with Ad Hoc Microphone Arrays
Authors: Dongmei Wang, Takuya Yoshioka, Zhuo Chen, Xiaofei Wang, Tianyan Zhou, Zhong Meng
Date: 2021-03-03 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Separation

Speech separation has been shown effective for multi-talker speech recognition. Under the ad hoc microphone array setup where the array consists of spatially distributed asynchronous microphones, additional challenges must be overcome as the geometry and number of microphones are unknown beforehand. Prior studies show, with a spatial-temporalinterleaving structure, neural networks can efficiently utilize the multi-channel signals of the ad hoc array. In this paper, we further extend this approach to continuous speech separation. Several techniques are introduced to enable speech separation for real continuous recordings. First, we apply a transformer-based network for spatio-temporal modeling of the ad hoc array signals. In addition, two methods are proposed to mitigate a speech duplication problem during single talker segments, which seems more severe in the ad hoc array scenarios. One method is device distortion simulation for reducing the acoustic mismatch between simulated training data and real recordings. The other is speaker counting to detect the single speaker segments and merge the output signal channels. Experimental results for AdHoc-LibiCSS, a new dataset consisting of continuous recordings of concatenated LibriSpeech utterances obtained by multiple different devices, show the proposed separation method can significantly improve the ASR accuracy for overlapped speech with little performance degradation for single talker segments.
Human-AI Interactions in Public Sector Decision-Making: "Automation Bias" and "Selective Adherence" to Algorithmic Advice
Authors: Saar Alon-Barkat, Madalina Busuioc
Date: 2021-03-03 00:00:00
Tasks: Decision Making

Artificial intelligence algorithms are increasingly adopted as decisional aides by public bodies, with the promise of overcoming biases of human decision-makers. At the same time, they may introduce new biases in the human-algorithm interaction. Drawing on psychology and public administration literatures, we investigate two key biases: overreliance on algorithmic advice even in the face of warning signals from other sources (automation bias), and selective adoption of algorithmic advice when this corresponds to stereotypes (selective adherence). We assess these via three experimental studies conducted in the NetherlandsWe discuss the implications of our findings for public sector decision making in the age of automation. Overall, our study speaks to potential negative effects of automation of the administrative state for already vulnerable and disadvantaged citizens.
A Fault Localization and Debugging Support Framework driven by Bug Tracking Data
Authors: Thomas Hirsch
Date: 2021-03-03 00:00:00
Tasks: Fault localization

Fault localization has been determined as a major resource factor in the software development life cycle. Academic fault localization techniques are mostly unknown and unused in professional environments. Although manual debugging approaches can vary significantly depending on bug type (e.g. memory bugs or semantic bugs), these differences are not reflected in most existing fault localization tools. Little research has gone into automated identification of bug types to optimize the fault localization process. Further, existing fault localization techniques leverage on historical data only for augmentation of suspiciousness rankings. This thesis aims to provide a fault localization framework by combining data from various sources to help developers in the fault localization process. To achieve this, a bug classification schema is introduced, benchmarks are created, and a novel fault localization method based on historical data is proposed.
Multi-task Learning by Leveraging the Semantic Information
Authors: Fan Zhou, Brahim Chaib-Draa, Boyu Wang
Date: 2021-03-03 00:00:00
Tasks: Multi-Task Learning

One crucial objective of multi-task learning is to align distributions across tasks so that the information between them can be transferred and shared. However, existing approaches only focused on matching the marginal feature distribution while ignoring the semantic information, which may hinder the learning performance. To address this issue, we propose to leverage the label information in multi-task learning by exploring the semantic conditional relations among tasks. We first theoretically analyze the generalization bound of multi-task learning based on the notion of Jensen-Shannon divergence, which provides new insights into the value of label information in multi-task learning. Our analysis also leads to a concrete algorithm that jointly matches the semantic distribution and controls label distribution divergence. To confirm the effectiveness of the proposed method, we first compare the algorithm with several baselines on some benchmarks and then test the algorithms under label space shift conditions. Empirical results demonstrate that the proposed method could outperform most baselines and achieve state-of-the-art performance, particularly showing the benefits under the label shift conditions.