pwc-archive/papers-with-abstracts huggingface dataset:
Investigation of Factorized Optical Flows as Mid-Level Representations
Authors: Hsuan-Kung Yang, Tsu-Ching Hsiao, Ting-Hsuan Liao, Hsu-Shen Liu, Li-Yuan Tsao, Tzu-Wen Wang, Shan-Ya Yang, Yu-Wen Chen, Huang-Ru Liao, Chun-Yi Lee
Date: 2022-03-09 00:00:00
Tasks: Deep Reinforcement Learning, Optical Flow Estimation, reinforcement-learning, Reinforcement Learning (RL)

In this paper, we introduce a new concept of incorporating factorized flow maps as mid-level representations, for bridging the perception and the control modules in modular learning based robotic frameworks. To investigate the advantages of factorized flow maps and examine their interplay with the other types of mid-level representations, we further develop a configurable framework, along with four different environments that contain both static and dynamic objects, for analyzing the impacts of factorized optical flow maps on the performance of deep reinforcement learning agents. Based on this framework, we report our experimental results on various scenarios, and offer a set of analyses to justify our hypothesis. Finally, we validate flow factorization in real world scenarios.
The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease -- A Machine Learning-Based Fusion Approach
Authors: Hafsa Binte Kibria, Abdul Matin
Date: 2022-03-09 00:00:00
Tasks: Classification, severity prediction

In today's world, a massive amount of data is available in almost every sector. This data has become an asset as we can use this enormous amount of data to find information. Mainly health care industry contains many data consisting of patient and disease-related information. By using the machine learning technique, we can look for hidden data patterns to predict various diseases. Recently CVDs, or cardiovascular disease, have become a leading cause of death around the world. The number of death due to CVDs is frightening. That is why many researchers are trying their best to design a predictive model that can save many lives using the data mining model. In this research, some fusion models have been constructed to diagnose CVDs along with its severity. Machine learning(ML) algorithms like artificial neural network, SVM, logistic regression, decision tree, random forest, and AdaBoost have been applied to the heart disease dataset to predict disease. Randomoversampler was implemented because of the class imbalance in multiclass classification. To improve the performance of classification, a weighted score fusion approach was taken. At first, the models were trained. After training, two algorithms' decision was combined using a weighted sum rule. A total of three fusion models have been developed from the six ML algorithms. The results were promising in the performance parameter. The proposed approach has been experimented with different test training ratios for binary and multiclass classification problems, and for both of them, the fusion models performed well. The highest accuracy for multiclass classification was found as 75%, and it was 95% for binary. The code can be found in : https://github.com/hafsa-kibria/Weighted_score_fusion_model_heart_disease_prediction
KPE: Keypoint Pose Encoding for Transformer-based Image Generation
Authors: Soon Yau Cheong, Armin Mustafa, Andrew Gilbert
Date: 2022-03-09 00:00:00
Tasks: Image Generation

Transformers have recently been shown to generate high quality images from text input. However, the existing method of pose conditioning using skeleton image tokens is computationally inefficient and generate low quality images. Therefore we propose a new method; Keypoint Pose Encoding (KPE); KPE is 10 times more memory efficient and over 73% faster at generating high quality images from text input conditioned on the pose. The pose constraint improves the image quality and reduces errors on body extremities such as arms and legs. The additional benefits include invariance to changes in the target image domain and image resolution, making it easily scalable to higher resolution images. We demonstrate the versatility of KPE by generating photorealistic multiperson images derived from the DeepFashion dataset. We also introduce a evaluation method People Count Error (PCE) that is effective in detecting error in generated human images.
What Matters For Meta-Learning Vision Regression Tasks?
Authors: Ning Gao, Hanna Ziesche, Ngo Anh Vien, Michael Volpp, Gerhard Neumann
Date: 2022-03-09 00:00:00
Proceeding: CVPR 2022 1
Tasks: Contrastive Learning, Data Augmentation, Meta-Learning, Object Discovery, Pose Estimation, regression

Meta-learning is widely used in few-shot classification and function regression due to its ability to quickly adapt to unseen tasks. However, it has not yet been well explored on regression tasks with high dimensional inputs such as images. This paper makes two main contributions that help understand this barely explored area. \emph{First}, we design two new types of cross-category level vision regression tasks, namely object discovery and pose estimation of unprecedented complexity in the meta-learning domain for computer vision. To this end, we (i) exhaustively evaluate common meta-learning techniques on these tasks, and (ii) quantitatively analyze the effect of various deep learning techniques commonly used in recent meta-learning algorithms in order to strengthen the generalization capability: data augmentation, domain randomization, task augmentation and meta-regularization. Finally, we (iii) provide some insights and practical recommendations for training meta-learning algorithms on vision regression tasks. \emph{Second}, we propose the addition of functional contrastive learning (FCL) over the task representations in Conditional Neural Processes (CNPs) and train in an end-to-end fashion. The experimental results show that the results of prior work are misleading as a consequence of a poor choice of the loss function as well as too small meta-training sets. Specifically, we find that CNPs outperform MAML on most tasks without fine-tuning. Furthermore, we observe that naive task augmentation without a tailored design results in underfitting.
Computing unsatisfiable cores for LTLf specifications
Authors: Marco Roveri, Claudio Di Ciccio, Chiara Di Francescomarino, Chiara Ghidini
Date: 2022-03-09 00:00:00
Tasks: Management

Linear-time temporal logic on finite traces (LTLf) is rapidly becoming a de-facto standard to produce specifications in many application domains (e.g., planning, business process management, run-time monitoring, reactive synthesis). Several studies approached the respective satisfiability problem. In this paper, we investigate the problem of extracting the unsatisfiable core in LTLf specifications. We provide four algorithms for extracting an unsatisfiable core leveraging the adaptation of state-of-the-art approaches to LTLf satisfiability checking. We implement the different approaches within the respective tools and carry out an experimental evaluation on a set of reference benchmarks, restricting to the unsatisfiable ones. The results show the feasibility, effectiveness, and complementarities of the different algorithms and tools.
No Efficient Disjunction or Conjunction of Switch-Lists
Authors: Stefan Mengel
Date: 2022-03-09 00:00:00

It is shown that disjunction of two switch-lists can blow up the representation size exponentially. Since switch-lists can be negated without any increase in size, this shows that conjunction of switch-lists also leads to an exponential blow-up in general.
How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting
Authors: Alessio Monti, Angelo Porrello, Simone Calderara, Pasquale Coscia, Lamberto Ballan, Rita Cucchiara
Date: 2022-03-09 00:00:00
Proceeding: CVPR 2022 1
Tasks: Knowledge Distillation, Trajectory Forecasting, Transfer Learning

Accurate prediction of future human positions is an essential task for modern video-surveillance systems. Current state-of-the-art models usually rely on a "history" of past tracked locations (e.g., 3 to 5 seconds) to predict a plausible sequence of future locations (e.g., up to the next 5 seconds). We feel that this common schema neglects critical traits of realistic applications: as the collection of input trajectories involves machine perception (i.e., detection and tracking), incorrect detection and fragmentation errors may accumulate in crowded scenes, leading to tracking drifts. On this account, the model would be fed with corrupted and noisy input data, thus fatally affecting its prediction performance. In this regard, we focus on delivering accurate predictions when only few input observations are used, thus potentially lowering the risks associated with automatic perception. To this end, we conceive a novel distillation strategy that allows a knowledge transfer from a teacher network to a student one, the latter fed with fewer observations (just two ones). We show that a properly defined teacher supervision allows a student network to perform comparably to state-of-the-art approaches that demand more observations. Besides, extensive experiments on common trajectory forecasting datasets highlight that our student network better generalizes to unseen scenarios.
Explainable Machine Learning for Predicting Homicide Clearance in the United States
Authors: Gian Maria Campedelli
Date: 2022-03-09 00:00:00
Tasks: BIG-bench Machine Learning, Explainable artificial intelligence

Purpose: To explore the potential of Explainable Machine Learning in the prediction and detection of drivers of cleared homicides at the national- and state-levels in the United States. Methods: First, nine algorithmic approaches are compared to assess the best performance in predicting cleared homicides country-wise, using data from the Murder Accountability Project. The most accurate algorithm among all (XGBoost) is then used for predicting clearance outcomes state-wise. Second, SHAP, a framework for Explainable Artificial Intelligence, is employed to capture the most important features in explaining clearance patterns both at the national and state levels. Results: At the national level, XGBoost demonstrates to achieve the best performance overall. Substantial predictive variability is detected state-wise. In terms of explainability, SHAP highlights the relevance of several features in consistently predicting investigation outcomes. These include homicide circumstances, weapons, victims' sex and race, as well as number of involved offenders and victims. Conclusions: Explainable Machine Learning demonstrates to be a helpful framework for predicting homicide clearance. SHAP outcomes suggest a more organic integration of the two theoretical perspectives emerged in the literature. Furthermore, jurisdictional heterogeneity highlights the importance of developing ad hoc state-level strategies to improve police performance in clearing homicides.
SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters
Authors: Albert Mosella-Montoro, Javier Ruiz-Hidalgo
Date: 2022-03-09 00:00:00
Proceeding: CVPR 2022 1
Tasks: Graph Neural Network

This work presents SkinningNet, an end-to-end Two-Stream Graph Neural Network architecture that computes skinning weights from an input mesh and its associated skeleton, without making any assumptions on shape class and structure of the provided mesh. Whereas previous methods pre-compute handcrafted features that relate the mesh and the skeleton or assume a fixed topology of the skeleton, the proposed method extracts this information in an end-to-end learnable fashion by jointly learning the best relationship between mesh vertices and skeleton joints. The proposed method exploits the benefits of the novel Multi-Aggregator Graph Convolution that combines the results of different aggregators during the summarizing step of the Message-Passing scheme, helping the operation to generalize for unseen topologies. Experimental results demonstrate the effectiveness of the contributions of our novel architecture, with SkinningNet outperforming current state-of-the-art alternatives.
Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC Domain
Authors: Zhe Zheng, Xin-Zheng Lu, Ke-Yin Chen, Yu-Cheng Zhou, Jia-Rui Lin
Date: 2022-03-09 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Retrieval, text-classification, Text Classification, Transfer Learning

As an essential task for the architecture, engineering, and construction (AEC) industry, information retrieval (IR) from unstructured textual data based on natural language processing (NLP) is gaining increasing attention. Although various deep learning (DL) models for IR tasks have been investigated in the AEC domain, it is still unclear how domain corpora and domain-specific pretrained DL models can improve performance in various IR tasks. To this end, this work systematically explores the impacts of domain corpora and various transfer learning techniques on the performance of DL models for IR tasks and proposes a pretrained domain-specific language model for the AEC domain. First, both in-domain and close-domain corpora are developed. Then, two types of pretrained models, including traditional wording embedding models and BERT-based models, are pretrained based on various domain corpora and transfer learning strategies. Finally, several widely used DL models for IR tasks are further trained and tested based on various configurations and pretrained models. The result shows that domain corpora have opposite effects on traditional word embedding models for text classification and named entity recognition tasks but can further improve the performance of BERT-based models in all tasks. Meanwhile, BERT-based models dramatically outperform traditional methods in all IR tasks, with maximum improvements of 5.4% and 10.1% in the F1 score, respectively. This research contributes to the body of knowledge in two ways: 1) demonstrating the advantages of domain corpora and pretrained DL models and 2) opening the first domain-specific dataset and pretrained language model for the AEC domain, to the best of our knowledge. Thus, this work sheds light on the adoption and application of pretrained models in the AEC domain.
Data Representativity for Machine Learning and AI Systems
Authors: Line H. Clemmensen, Rune D. Kjærsgaard
Date: 2022-03-09 00:00:00
Tasks: BIG-bench Machine Learning, Experimental Design, Fairness

Data representativity is crucial when drawing inference from data through machine learning models. Scholars have increased focus on unraveling the bias and fairness in models, also in relation to inherent biases in the input data. However, limited work exists on the representativity of samples (datasets) for appropriate inference in AI systems. This paper reviews definitions and notions of a representative sample and surveys their use in scientific AI literature. We introduce three measurable concepts to help focus the notions and evaluate different data samples. Furthermore, we demonstrate that the contrast between a representative sample in the sense of coverage of the input space, versus a representative sample mimicking the distribution of the target population is of particular relevance when building AI systems. Through empirical demonstrations on US Census data, we evaluate the opposing inherent qualities of these concepts. Finally, we propose a framework of questions for creating and documenting data with data representativity in mind, as an addition to existing dataset documentation templates.
LEMON: LanguagE ModeL for Negative Sampling of Knowledge Graph Embeddings
Authors: Md Rashad Al Hasan Rony, Mirza Mohtashim Alam, Semab Ali, Jens Lehmann, Sahar Vahdati
Date: 2022-03-09 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graph Embeddings, Knowledge Graphs, Language Modeling, Language Modelling, Link Prediction, Question Answering, Representation Learning

Knowledge Graph Embedding models have become an important area of machine learning.Those models provide a latent representation of entities and relations in a knowledge graph which can then be used in downstream machine learning tasks such as link prediction. The learning process of such models can be performed by contrasting positive and negative triples. While all triples of a KG are considered positive, negative triples are usually not readily available. Therefore, the choice of the sampling method to obtain the negative triples play a crucial role in the performance and effectiveness of Knowledge Graph Embedding models. Most of the current methods fetch negative samples from a random distribution of entities in the underlying Knowledge Graph which also often includes meaningless triples. Other known methods use adversarial techniques or generative neural networks which consequently reduce the efficiency of the process. In this paper, we propose an approach for generating informative negative samples considering available complementary knowledge about entities. Particularly, Pre-trained Language Models are used to form neighborhood clusters by utilizing the distances between entities to obtain representations of symbolic entities via their textual information. Our comprehensive evaluations demonstrate the effectiveness of the proposed approach on benchmark Knowledge Graphs with textual information for the link prediction task.
ModulE: Module Embedding for Knowledge Graphs
Authors: Jingxuan Chai, Guangming Shi
Date: 2022-03-09 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs

Knowledge graph embedding (KGE) has been shown to be a powerful tool for predicting missing links of a knowledge graph. However, existing methods mainly focus on modeling relation patterns, while simply embed entities to vector spaces, such as real field, complex field and quaternion space. To model the embedding space from a more rigorous and theoretical perspective, we propose a novel general group theory-based embedding framework for rotation-based models, in which both entities and relations are embedded as group elements. Furthermore, in order to explore more available KGE models, we utilize a more generic group structure, module, a generalization notion of vector space. Specifically, under our framework, we introduce a more generic embedding method, ModulE, which projects entities to a module. Following the method of ModulE, we build three instantiating models: ModulE$_{\mathbb{R},\mathbb{C}}$, ModulE$_{\mathbb{R},\mathbb{H}}$ and ModulE$_{\mathbb{H},\mathbb{H}}$, by adopting different module structures. Experimental results show that ModulE$_{\mathbb{H},\mathbb{H}}$ which embeds entities to a module over non-commutative ring, achieves state-of-the-art performance on multiple benchmark datasets.
Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning
Authors: Zheng Zhang, Xiaohan Wang, Qingrui Zhang, Tianjiang Hu
Date: 2022-03-09 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

It is of great challenge, though promising, to coordinate collective robots for hunting an evader in a decentralized manner purely in light of local observations. In this paper, this challenge is addressed by a novel hybrid cooperative pursuit algorithm that combines reinforcement learning with the artificial potential field method. In the proposed algorithm, decentralized deep reinforcement learning is employed to learn cooperative pursuit policies that are adaptive to dynamic environments. The artificial potential field method is integrated into the learning process as predefined rules to improve the data efficiency and generalization ability. It is shown by numerical simulations that the proposed hybrid design outperforms the pursuit policies either learned from vanilla reinforcement learning or designed by the potential field method. Furthermore, experiments are conducted by transferring the learned pursuit policies into real-world mobile robots. Experimental results demonstrate the feasibility and potential of the proposed algorithm in learning multiple cooperative pursuit strategies.
Gym-saturation: an OpenAI Gym environment for saturation provers
Authors: Boris Shminke
Date: 2022-03-09 00:00:00
Tasks: OpenAI Gym, Reinforcement Learning (RL)

`gym-saturation` is an OpenAI Gym environment for reinforcement learning (RL) agents capable of proving theorems. Currently, only theorems written in a formal language of the Thousands of Problems for Theorem Provers (TPTP) library in clausal normal form (CNF) are supported. `gym-saturation` implements the 'given clause' algorithm (similar to the one used in Vampire and E Prover). Being written in Python, `gym-saturation` was inspired by PyRes. In contrast to the monolithic architecture of a typical Automated Theorem Prover (ATP), `gym-saturation` gives different agents opportunities to select clauses themselves and train from their experience. Combined with a particular agent, `gym-saturation` can work as an ATP. Even with a non trained agent based on heuristics, `gym-saturation` can find refutations for 688 (of 8257) CNF problems from TPTP v7.5.0.
Align-Deform-Subtract: An Interventional Framework for Explaining Object Differences
Authors: Cian Eastwood, Li Nanbo, Christopher K. I. Williams
Date: 2022-03-09 00:00:00
Tasks: counterfactual, Object

Given two object images, how can we explain their differences in terms of the underlying object properties? To address this question, we propose Align-Deform-Subtract (ADS) -- an interventional framework for explaining object differences. By leveraging semantic alignments in image-space as counterfactual interventions on the underlying object properties, ADS iteratively quantifies and removes differences in object properties. The result is a set of "disentangled" error measures which explain object differences in terms of the underlying properties. Experiments on real and synthetic data illustrate the efficacy of the framework.
FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction
Authors: Fang Fang, Shenliao Bao
Date: 2022-03-09 00:00:00
Tasks: Imputation

Modern scientific research and applications very often encounter "fragmentary data" which brings big challenges to imputation and prediction. By leveraging the structure of response patterns, we propose a unified and flexible framework based on Generative Adversarial Nets (GAN) to deal with fragmentary data imputation and label prediction at the same time. Unlike most of the other generative model based imputation methods that either have no theoretical guarantee or only consider Missing Completed At Random (MCAR), the proposed FragmGAN has theoretical guarantees for imputation with data Missing At Random (MAR) while no hint mechanism is needed. FragmGAN trains a predictor with the generator and discriminator simultaneously. This linkage mechanism shows significant advantages for predictive performances in extensive experiments.