pwc-archive/papers-with-abstracts huggingface dataset:
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation
Authors: Yuansheng Wang, Wangbin Sun, Kun Xu, Zulun Zhu, Liang Chen, Zibin Zheng
Date: 2022-05-02 00:00:00
Tasks: Classification, Contrastive Learning, Data Augmentation, Graph Classification, Node Classification, Self-Supervised Learning

Graph contrastive learning (GCL), as a popular approach to graph self-supervised learning, has recently achieved a non-negligible effect. To achieve superior performance, the majority of existing GCL methods elaborate on graph data augmentation to construct appropriate contrastive pairs. However, existing methods place more emphasis on the complex graph data augmentation which requires extra time overhead, and pay less attention to developing contrastive schemes specific to encoder characteristics. We argue that a better contrastive scheme should be tailored to the characteristics of graph neural networks (e.g., neighborhood aggregation) and propose a simple yet effective method named FastGCL. Specifically, by constructing weighted-aggregated and non-aggregated neighborhood information as positive and negative samples respectively, FastGCL identifies the potential semantic information of data without disturbing the graph topology and node attributes, resulting in faster training and convergence speeds. Extensive experiments have been conducted on node classification and graph classification tasks, showing that FastGCL has competitive classification performance and significant training speedup compared to existing state-of-the-art methods.
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Authors: Zhenwei Tang, Shichao Pei, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Robert Hoehndorf, Xiangliang Zhang
Date: 2022-05-02 00:00:00
Tasks: Data Augmentation, Knowledge Graph Completion, Knowledge Graphs, World Knowledge

Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given KG, i.e., knowledge graph completion (KGC). However, existing KGC methods suffer from two main issues, 1) the false negative issue, i.e., the sampled negative training instances may include potential true facts; and 2) the data sparsity issue, i.e., true facts account for only a tiny part of all possible facts. To this end, we propose positive-unlabeled learning with adversarial data augmentation (PUDA) for KGC. In particular, PUDA tailors positive-unlabeled risk estimator for the KGC task to deal with the false negative issue. Furthermore, to address the data sparsity issue, PUDA achieves a data augmentation strategy by unifying adversarial training and positive-unlabeled learning under the positive-unlabeled minimax game. Extensive experimental results on real-world benchmark datasets demonstrate the effectiveness and compatibility of our proposed method.
A Novel Approach to Fairness in Automated Decision-Making using Affective Normalization
Authors: Jesse Hoey, Gabrielle Chan
Date: 2022-05-02 00:00:00
Tasks: Decision Making, Fairness

Any decision, such as one about who to hire, involves two components. First, a rational component, i.e., they have a good education, they speak clearly. Second, an affective component, based on observables such as visual features of race and gender, and possibly biased by stereotypes. Here we propose a method for measuring the affective, socially biased, component, thus enabling its removal. That is, given a decision-making process, these affective measurements remove the affective bias in the decision, rendering it fair across a set of categories defined by the method itself. We thus propose that this may solve three key problems in intersectional fairness: (1) the definition of categories over which fairness is a consideration; (2) an infinite regress into smaller and smaller groups; and (3) ensuring a fair distribution based on basic human rights or other prior information. The primary idea in this paper is that fairness biases can be measured using affective coherence, and that this can be used to normalize outcome mappings. We aim for this conceptual work to expose a novel method for handling fairness problems that uses emotional coherence as an independent measure of bias that goes beyond statistical parity.
Deep-Attack over the Deep Reinforcement Learning
Authors: Yang Li, Quan Pan, Erik Cambria
Date: 2022-05-02 00:00:00
Tasks: Adversarial Attack, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent adversarial attack developments have made reinforcement learning more vulnerable, and different approaches exist to deploy attacks against it, where the key is how to choose the right timing of the attack. Some work tries to design an attack evaluation function to select critical points that will be attacked if the value is greater than a certain threshold. This approach makes it difficult to find the right place to deploy an attack without considering the long-term impact. In addition, there is a lack of appropriate indicators of assessment during attacks. To make the attacks more intelligent as well as to remedy the existing problems, we propose the reinforcement learning-based attacking framework by considering the effectiveness and stealthy spontaneously, while we also propose a new metric to evaluate the performance of the attack model in these two aspects. Experimental results show the effectiveness of our proposed model and the goodness of our proposed evaluation metric. Furthermore, we validate the transferability of the model, and also its robustness under the adversarial training.
Seeding Diversity into AI Art
Authors: Marvin Zammit, Antonios Liapis, Georgios N. Yannakakis
Date: 2022-05-02 00:00:00
Tasks: Diversity, Evolutionary Algorithms, Image Generation

This paper argues that generative art driven by conformance to a visual and/or semantic corpus lacks the necessary criteria to be considered creative. Among several issues identified in the literature, we focus on the fact that generative adversarial networks (GANs) that create a single image, in a vacuum, lack a concept of novelty regarding how their product differs from previously created ones. We envision that an algorithm that combines the novelty preservation mechanisms in evolutionary algorithms with the power of GANs can deliberately guide its creative process towards output that is both good and novel. In this paper, we use recent advances in image generation based on semantic prompts using OpenAI's CLIP model, interrupting the GAN's iterative process with short cycles of evolutionary divergent search. The results of evolution are then used to continue the GAN's iterative process; we hypothesise that this intervention will lead to more novel outputs. Testing our hypothesis using novelty search with local competition, a quality-diversity evolutionary algorithm that can increase visual diversity while maintaining quality in the form of adherence to the semantic prompt, we explore how different notions of visual diversity can affect both the process and the product of the algorithm. Results show that even a simplistic measure of visual diversity can help counter a drift towards similar images caused by the GAN. This first experiment opens a new direction for introducing higher intentionality and a more nuanced drive for GANs.
On verifying expectations and observations of intelligent agents
Authors: Sourav Chakraborty, Avijeet Ghosh, Sujata Ghosh, François Schwarzentruber
Date: 2022-05-02 00:00:00

Public observation logic (POL) is a variant of dynamic epistemic logic to reason about agent expectations and agent observations. Agents have certain expectations, regarding the situation at hand, that are actuated by the relevant protocols, and they eliminate possible worlds in which their expectations do not match with their observations. In this work, we investigate the computational complexity of the model checking problem for POL and prove its PSPACE-completeness. We also study various syntactic fragments of POL. We exemplify the applicability of POL model checking in verifying different characteristics and features of an interactive system with respect to the distinct expectations and (matching) observations of the system. Finally, we provide a discussion on the implementation of the model checking algorithms.
Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs
Authors: Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, XiaoLi Li, Ru Li, Jeff Z. Pan
Date: 2022-05-02 00:00:00
Tasks: Knowledge Graphs, Vocal Bursts Type Prediction

Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information. To address this problem, it has been recently introduced a promising approach based on jointly embedding logical queries and KGs into a low-dimensional space to identify answer entities. However, existing proposals ignore critical semantic knowledge inherently available in KGs, such as type information. To leverage type information, we propose a novel TypE-aware Message Passing (TEMP) model, which enhances the entity and relation representations in queries, and simultaneously improves generalization, deductive and inductive reasoning. Remarkably, TEMP is a plug-and-play model that can be easily incorporated into existing embedding-based models to improve their performance. Extensive experiments on three real-world datasets demonstrate TEMP's effectiveness.
Large Neighborhood Search based on Neural Construction Heuristics
Authors: Jonas K. Falkner, Daniela Thyssens, Lars Schmidt-Thieme
Date: 2022-05-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We propose a Large Neighborhood Search (LNS) approach utilizing a learned construction heuristic based on neural networks as repair operator to solve the vehicle routing problem with time windows (VRPTW). Our method uses graph neural networks to encode the problem and auto-regressively decodes a solution and is trained with reinforcement learning on the construction task without requiring any labels for supervision. The neural repair operator is combined with a local search routine, heuristic destruction operators and a selection procedure applied to a small population to arrive at a sophisticated solution approach. The key idea is to use the learned model to re-construct the partially destructed solution and to introduce randomness via the destruction heuristics (or the stochastic policy itself) to effectively explore a large neighborhood.
Data-driven emotional body language generation for social robotics
Authors: Mina Marmpena, Fernando Garcia, Angelica Lim, Nikolas Hemion, Thomas Wennekers
Date: 2022-05-02 00:00:00
Tasks: Attribute, Text Generation

In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration, since humans attribute, and perhaps subconsciously anticipate, such traces to perceive an agent as engaging, trustworthy, and socially present. Robotic emotional body language needs to be believable, nuanced and relevant to the context. We implemented a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions and can generate numerous new ones of similar believability and lifelikeness. The framework uses the Conditional Variational Autoencoder model and a sampling approach based on the geometric properties of the model's latent space to condition the generative process on targeted levels of valence and arousal. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones, and the emotional conditioning was adequately differentiable between most levels except the pairs of neutral-positive valence and low-medium arousal. Furthermore, an exploratory analysis of the results reveals a possible impact of the conditioning on the perceived dominance of the robot, as well as on the participants' attention.
Superredundancy: A tool for Boolean formula minimization complexity analysis
Authors: Paolo Liberatore
Date: 2022-05-02 00:00:00

A superredundant clause is a clause that is redundant in the resolution closure of a formula. The converse concept of superirredundancy ensures membership of the clause in all minimal CNF formulae that are equivalent to the given one. This allows for building formulae where some clauses are fixed when minimizing size. An example are proofs of complexity hardness of the problems of minimal formula size. Others are proofs of size when forgetting variables or revising a formula. Most clauses can be made superirredundant by splitting them over a new variable.
FedDKD: Federated Learning with Decentralized Knowledge Distillation
Authors: Xinjia Li, BoYu Chen, Wenlian Lu
Date: 2022-05-02 00:00:00
Tasks: Federated Learning, Knowledge Distillation

The performance of federated learning in neural networks is generally influenced by the heterogeneity of the data distribution. For a well-performing global model, taking a weighted average of the local models, as done by most existing federated learning algorithms, may not guarantee consistency with local models in the space of neural network maps. In this paper, we propose a novel framework of federated learning equipped with the process of decentralized knowledge distillation (FedDKD) (i.e., without data on the server). The FedDKD introduces a module of decentralized knowledge distillation (DKD) to distill the knowledge of the local models to train the global model by approaching the neural network map average based on the metric of divergence defined in the loss function, other than only averaging parameters as done in literature. Numeric experiments on various heterogeneous datasets reveal that FedDKD outperforms the state-of-the-art methods with more efficient communication and training in a few DKD steps, especially on some extremely heterogeneous datasets.
DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data
Authors: Nicolò Oreste Pinciroli Vago, Piero Fraternali
Date: 2022-05-02 00:00:00
Tasks: Astronomy, Image Classification, Multimodal Deep Learning, Time Series, Time Series Analysis

Gravitational lensing is the relativistic effect generated by massive bodies, which bend the space-time surrounding them. It is a deeply investigated topic in astrophysics and allows validating theoretical relativistic results and studying faint astrophysical objects that would not be visible otherwise. In recent years Machine Learning methods have been applied to support the analysis of the gravitational lensing phenomena by detecting lensing effects in data sets consisting of images associated with brightness variation time series. However, the state-of-art approaches either consider only images and neglect time-series data or achieve relatively low accuracy on the most difficult data sets. This paper introduces DeepGraviLens, a novel multi-modal network that classifies spatio-temporal data belonging to one non-lensed system type and three lensed system types. It surpasses the current state of the art accuracy results by $\approx 3\%$ to $\approx 11\%$, depending on the considered data set. Such an improvement will enable the acceleration of the analysis of lensed objects in upcoming astrophysical surveys, which will exploit the petabytes of data collected, e.g., from the Vera C. Rubin Observatory.
A Multi-stage deep architecture for summary generation of soccer videos
Authors: Melissa Sanabria, Frédéric Precioso, Pierre-Alexandre Mattei, Thomas Menguy
Date: 2022-05-02 00:00:00
Tasks: Sports Analytics, Video Summarization

Video content is present in an ever-increasing number of fields, both scientific and commercial. Sports, particularly soccer, is one of the industries that has invested the most in the field of video analytics, due to the massive popularity of the game and the emergence of new markets. Previous state-of-the-art methods on soccer matches video summarization rely on handcrafted heuristics to generate summaries which are poorly generalizable, but these works have yet proven that multiple modalities help detect the best actions of the game. On the other hand, machine learning models with higher generalization potential have entered the field of summarization of general-purpose videos, offering several deep learning approaches. However, most of them exploit content specificities that are not appropriate for sport whole-match videos. Although video content has been for many years the main source for automatizing knowledge extraction in soccer, the data that records all the events happening on the field has become lately very important in sports analytics, since this event data provides richer context information and requires less processing. We propose a method to generate the summary of a soccer match exploiting both the audio and the event metadata. The results show that our method can detect the actions of the match, identify which of these actions should belong to the summary and then propose multiple candidate summaries which are similar enough but with relevant variability to provide different options to the final editor. Furthermore, we show the generalization capability of our work since it can transfer knowledge between datasets from different broadcasting companies, different competitions, acquired in different conditions, and corresponding to summaries of different lengths
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
Authors: HeeSun Bae, Seungjae Shin, Byeonghu Na, JoonHo Jang, Kyungwoo Song, Il-Chul Moon
Date: 2022-05-02 00:00:00
Tasks: Learning with noisy labels, Prediction

Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization of a classifier by making the classifier over-fitted to noisy labels. Existing methods on noisy label have focused on modifying the classifier during the training procedure. It has two potential problems. First, these methods are not applicable to a pre-trained classifier without further access to training. Second, it is not easy to train a classifier and regularize all negative effects from noisy labels, simultaneously. We suggest a new branch of method, Noisy Prediction Calibration (NPC) in learning with noisy labels. Through the introduction and estimation of a new type of transition matrix via generative model, NPC corrects the noisy prediction from the pre-trained classifier to the true label as a post-processing scheme. We prove that NPC theoretically aligns with the transition matrix based methods. Yet, NPC empirically provides more accurate pathway to estimate true label, even without involvement in classifier learning. Also, NPC is applicable to any classifier trained with noisy label methods, if training instances and its predictions are available. Our method, NPC, boosts the classification performances of all baseline models on both synthetic and real-world datasets. The implemented code is available at https://github.com/BaeHeeSun/NPC.
Deep Video Harmonization with Color Mapping Consistency
Authors: Xinyuan Lu, Shengyuan Huang, Li Niu, Wenyan Cong, Liqing Zhang
Date: 2022-05-02 00:00:00
Tasks: Video Harmonization

Video harmonization aims to adjust the foreground of a composite video to make it compatible with the background. So far, video harmonization has only received limited attention and there is no public dataset for video harmonization. In this work, we construct a new video harmonization dataset HYouTube by adjusting the foreground of real videos to create synthetic composite videos. Moreover, we consider the temporal consistency in video harmonization task. Unlike previous works which establish the spatial correspondence, we design a novel framework based on the assumption of color mapping consistency, which leverages the color mapping of neighboring frames to refine the current frame. Extensive experiments on our HYouTube dataset prove the effectiveness of our proposed framework. Our dataset and code are available at https://github.com/bcmi/Video-Harmonization-Dataset-HYouTube.
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)
Authors: Michael Weiss, Paolo Tonella
Date: 2022-05-02 00:00:00
Tasks: Active Learning, Uncertainty Quantification

Test Input Prioritizers (TIP) for Deep Neural Networks (DNN) are an important technique to handle the typically very large test datasets efficiently, saving computation and labeling costs. This is particularly true for large-scale, deployed systems, where inputs observed in production are recorded to serve as potential test or training data for the next versions of the system. Feng et. al. propose DeepGini, a very fast and simple TIP, and show that it outperforms more elaborate techniques such as neuron- and surprise coverage. In a large-scale study (4 case studies, 8 test datasets, 32'200 trained models) we verify their findings. However, we also find that other comparable or even simpler baselines from the field of uncertainty quantification, such as the predicted softmax likelihood or the entropy of the predicted softmax likelihoods perform equally well as DeepGini.
Skeptical binary inferences in multi-label problems with sets of probabilities
Authors: Yonatan Carlos Carranza Alarcón, Sébastien Destercke
Date: 2022-05-02 00:00:00
Tasks: valid

In this paper, we consider the problem of making distributionally robust, skeptical inferences for the multi-label problem, or more generally for Boolean vectors. By distributionally robust, we mean that we consider a set of possible probability distributions, and by skeptical we understand that we consider as valid only those inferences that are true for every distribution within this set. Such inferences will provide partial predictions whenever the considered set is sufficiently big. We study in particular the Hamming loss case, a common loss function in multi-label problems, showing how skeptical inferences can be made in this setting. Our experimental results are organised in three sections; (1) the first one indicates the gain computational obtained from our theoretical results by using synthetical data sets, (2) the second one indicates that our approaches produce relevant cautiousness on those hard-to-predict instances where its precise counterpart fails, and (3) the last one demonstrates experimentally how our approach copes with imperfect information (generated by a downsampling procedure) better than the partial abstention [31] and the rejection rules.
Enhancing Adversarial Training with Feature Separability
Authors: Yaxin Li, Xiaorui Liu, Han Xu, Wentao Wang, Jiliang Tang
Date: 2022-05-02 00:00:00

Deep Neural Network (DNN) are vulnerable to adversarial attacks. As a countermeasure, adversarial training aims to achieve robustness based on the min-max optimization problem and it has shown to be one of the most effective defense strategies. However, in this work, we found that compared with natural training, adversarial training fails to learn better feature representations for either clean or adversarial samples, which can be one reason why adversarial training tends to have severe overfitting issues and less satisfied generalize performance. Specifically, we observe two major shortcomings of the features learned by existing adversarial training methods:(1) low intra-class feature similarity; and (2) conservative inter-classes feature variance. To overcome these shortcomings, we introduce a new concept of adversarial training graph (ATG) with which the proposed adversarial training with feature separability (ATFS) enables to coherently boost the intra-class feature similarity and increase inter-class feature variance. Through comprehensive experiments, we demonstrate that the proposed ATFS framework significantly improves both clean and robust performance.