pwc-archive/papers-with-abstracts huggingface dataset:
Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Features
Authors: Stelios Boulitsakis-Logothetis
Date: 2022-02-23 00:00:00
Tasks: Attribute, Binary Classification, Fairness

Fairness-aware machine learning seeks to maximise utility in generating predictions while avoiding unfair discrimination based on sensitive attributes such as race, sex, religion, etc. An important line of work in this field is enforcing fairness during the training step of a classifier. A simple yet effective binary classification algorithm that follows this strategy is two-naive-Bayes (2NB), which enforces statistical parity - requiring that the groups comprising the dataset receive positive labels with the same likelihood. In this paper, we generalise this algorithm into N-naive-Bayes (NNB) to eliminate the simplification of assuming only two sensitive groups in the data and instead apply it to an arbitrary number of groups. We propose an extension of the original algorithm's statistical parity constraint and the post-processing routine that enforces statistical independence of the label and the single sensitive attribute. Then, we investigate its application on data with multiple sensitive features and propose a new constraint and post-processing routine to enforce differential fairness, an extension of established group-fairness constraints focused on intersectionalities. We empirically demonstrate the effectiveness of the NNB algorithm on US Census datasets and compare its accuracy and debiasing performance, as measured by disparate impact and DF-$\epsilon$ score, with similar group-fairness algorithms. Finally, we lay out important considerations users should be aware of before incorporating this algorithm into their application, and direct them to further reading on the pros, cons, and ethical implications of using statistical parity as a fairness criterion.
Deep Learning Reproducibility and Explainable AI (XAI)
Authors: A. -M. Leventi-Peetz, T. Östreich
Date: 2022-02-23 00:00:00
Tasks: Deep Learning, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), image-classification, Image Classification

The nondeterminism of Deep Learning (DL) training algorithms and its influence on the explainability of neural network (NN) models are investigated in this work with the help of image classification examples. To discuss the issue, two convolutional neural networks (CNN) have been trained and their results compared. The comparison serves the exploration of the feasibility of creating deterministic, robust DL models and deterministic explainable artificial intelligence (XAI) in practice. Successes and limitation of all here carried out efforts are described in detail. The source code of the attained deterministic models has been listed in this work. Reproducibility is indexed as a development-phase-component of the Model Governance Framework, proposed by the EU within their excellence in AI approach. Furthermore, reproducibility is a requirement for establishing causality for the interpretation of model results and building of trust towards the overwhelming expansion of AI systems applications. Problems that have to be solved on the way to reproducibility and ways to deal with some of them, are examined in this work.
Reconstruction of observed mechanical motions with Artificial Intelligence tools
Authors: Antal Jakovac, Marcell T. Kurbucz, Peter Posfay
Date: 2022-02-23 00:00:00

The goal of this paper is to determine the laws of observed trajectories assuming that there is a mechanical system in the background and using these laws to continue the observed motion in a plausible way. The laws are represented by neural networks with a limited number of parameters. The training of the networks follows the Extreme Learning Machine idea. We determine laws for different levels of embedding, thus we can represent not only the equation of motion but also the symmetries of different kinds. In the recursive numerical evolution of the system, we require the fulfillment of all the observed laws, within the determined numerical precision. In this way, we can successfully reconstruct both integrable and chaotic motions, as we demonstrate in the example of the gravity pendulum and the double pendulum.
Enabling arbitrary translation objectives with Adaptive Tree Search
Authors: Wang Ling, Wojciech Stokowiec, Domenic Donato, Laurent Sartran, Lei Yu, Austin Matthews, Chris Dyer
Date: 2022-02-23 00:00:00
Proceeding: ICLR 2022 4
Tasks: Decoder, Reranking, Translation

We introduce an adaptive tree search algorithm, that can find high-scoring outputs under translation models that make no assumptions about the form or structure of the search objective. This algorithm -- a deterministic variant of Monte Carlo tree search -- enables the exploration of new kinds of models that are unencumbered by constraints imposed to make decoding tractable, such as autoregressivity or conditional independence assumptions. When applied to autoregressive models, our algorithm has different biases than beam search has, which enables a new analysis of the role of decoding bias in autoregressive models. Empirically, we show that our adaptive tree search algorithm finds outputs with substantially better model scores compared to beam search in autoregressive models, and compared to reranking techniques in models whose scores do not decompose additively with respect to the words in the output. We also characterise the correlation of several translation model objectives with respect to BLEU. We find that while some standard models are poorly calibrated and benefit from the beam search bias, other often more robust models (autoregressive models tuned to maximize expected automatic metric scores, the noisy channel model and a newly proposed objective) benefit from increasing amounts of search using our proposed decoder, whereas the beam search bias limits the improvements obtained from such objectives. Thus, we argue that as models improve, the improvements may be masked by over-reliance on beam search or reranking based methods.
DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization
Authors: Xuebo Tian, Junqiao Zhao, Chen Ye
Date: 2022-02-23 00:00:00
Tasks: Autonomous Driving, Object, object-detection, Object Detection, Object Tracking, Pose Estimation, Simultaneous Localization and Mapping, Time Series Analysis

Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM.
Multi-view Intent Disentangle Graph Networks for Bundle Recommendation
Authors: Sen Zhao, Wei Wei, Ding Zou, Xianling Mao
Date: 2022-02-23 00:00:00
Tasks: Diversity

Bundle recommendation aims to recommend the user a bundle of items as a whole. Nevertheless, they usually neglect the diversity of the user's intents on adopting items and fail to disentangle the user's intents in representations. In the real scenario of bundle recommendation, a user's intent may be naturally distributed in the different bundles of that user (Global view), while a bundle may contain multiple intents of a user (Local view). Each view has its advantages for intent disentangling: 1) From the global view, more items are involved to present each intent, which can demonstrate the user's preference under each intent more clearly. 2) From the local view, it can reveal the association among items under each intent since items within the same bundle are highly correlated to each other. To this end, we propose a novel model named Multi-view Intent Disentangle Graph Networks (MIDGN), which is capable of precisely and comprehensively capturing the diversity of the user's intent and items' associations at the finer granularity. Specifically, MIDGN disentangles the user's intents from two different perspectives, respectively: 1) In the global level, MIDGN disentangles the user's intent coupled with inter-bundle items; 2) In the Local level, MIDGN disentangles the user's intent coupled with items within each bundle. Meanwhile, we compare the user's intents disentangled from different views under the contrast learning framework to improve the learned intents. Extensive experiments conducted on two benchmark datasets demonstrate that MIDGN outperforms the state-of-the-art methods by over 10.7% and 26.8%, respectively.
Deepfake Detection for Facial Images with Facemasks
Authors: Donggeun Ko, Sangjun Lee, Jinyong Park, Saebyeol Shin, Donghee Hong, Simon S. Woo
Date: 2022-02-23 00:00:00
Tasks: DeepFake Detection, Face Swapping, Image Generation

Hyper-realistic face image generation and manipulation have givenrise to numerous unethical social issues, e.g., invasion of privacy,threat of security, and malicious political maneuvering, which re-sulted in the development of recent deepfake detection methodswith the rising demands of deepfake forensics. Proposed deepfakedetection methods to date have shown remarkable detection perfor-mance and robustness. However, none of the suggested deepfakedetection methods assessed the performance of deepfakes withthe facemask during the pandemic crisis after the outbreak of theCovid-19. In this paper, we thoroughly evaluate the performance ofstate-of-the-art deepfake detection models on the deepfakes withthe facemask. Also, we propose two approaches to enhance themasked deepfakes detection:face-patchandface-crop. The experi-mental evaluations on both methods are assessed through the base-line deepfake detection models on the various deepfake datasets.Our extensive experiments show that, among the two methods,face-cropperforms better than theface-patch, and could be a trainmethod for deepfake detection models to detect fake faces withfacemask in real world.
Prompt-Learning for Short Text Classification
Authors: Yi Zhu, Xinke Zhou, Jipeng Qiang, Yun Li, Yunhao Yuan, Xindong Wu
Date: 2022-02-23 00:00:00
Tasks: Classification, Prompt Learning, text-classification, Text Classification

In the short text, the extremely short length, feature sparsity, and high ambiguity pose huge challenges to classification tasks. Recently, as an effective method for tuning Pre-trained Language Models for specific downstream tasks, prompt-learning has attracted a vast amount of attention and research. The main intuition behind the prompt-learning is to insert the template into the input and convert the text classification tasks into equivalent cloze-style tasks. However, most prompt-learning methods expand label words manually or only consider the class name for knowledge incorporating in cloze-style prediction, which will inevitably incur omissions and bias in short text classification tasks. In this paper, we propose a simple short text classification approach that makes use of prompt-learning based on knowledgeable expansion. Taking the special characteristics of short text into consideration, the method can consider both the short text itself and class name during expanding label words space. Specifically, the top $N$ concepts related to the entity in the short text are retrieved from the open Knowledge Graph like Probase, and we further refine the expanded label words by the distance calculation between selected concepts and class labels. Experimental results show that our approach obtains obvious improvement compared with other fine-tuning, prompt-learning, and knowledgeable prompt-tuning methods, outperforming the state-of-the-art by up to 6 Accuracy points on three well-known datasets.
Commonsense Reasoning for Identifying and Understanding the Implicit Need of Help and Synthesizing Assistive Actions
Authors: Maëlic neau, Paulo Santos, Anne-Gwenn Bosser, Nathan Beu, Cédric Buche
Date: 2022-02-23 00:00:00
Tasks: Graph Generation, Scene Graph Generation, Sentiment Analysis

Human-Robot Interaction (HRI) is an emerging subfield of service robotics. While most existing approaches rely on explicit signals (i.e. voice, gesture) to engage, current literature is lacking solutions to address implicit user needs. In this paper, we present an architecture to (a) detect user implicit need of help and (b) generate a set of assistive actions without prior learning. Task (a) will be performed using state-of-the-art solutions for Scene Graph Generation coupled to the use of commonsense knowledge; whereas, task (b) will be performed using additional commonsense knowledge as well as a sentiment analysis on graph structure. Finally, we propose an evaluation of our solution using established benchmarks (e.g. ActionGenome dataset) along with human experiments. The main motivation of our approach is the embedding of the perception-decision-action loop in a single architecture.
Scalable Query Answering under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach
Authors: Gaston Zanitti, Yamil Soto, Valentin Iovene, Maria Vanina Martinez, Ricardo Rodriguez, Gerardo Simari, Demian Wassermann
Date: 2022-02-23 00:00:00

Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the brain has been studied, there is also available ontological knowledge encoding the current state of the art regarding its different areas, activation patterns, key words associated with studies, etc. Furthermore, there is an inherent uncertainty associated with brain scans arising from the mapping between voxels -- 3D pixels -- and actual points in different individual brains. Unfortunately, there is currently no unifying framework for accessing such collections of rich heterogeneous data under uncertainty, making it necessary for researchers to rely on ad hoc tools. In particular, one major weakness of current tools that attempt to address this kind of task is that only very limited propositional query languages have been developed. In this paper, we present NeuroLang, an ontology language with existential rules, probabilistic uncertainty, and built-in mechanisms to guarantee tractable query answering over very large datasets. After presenting the language and its general query answering architecture, we discuss real-world use cases showing how NeuroLang can be applied to practical scenarios for which current tools are inadequate.
Reinforcement Learning in Practice: Opportunities and Challenges
Authors: Yuxi Li
Date: 2022-02-23 00:00:00
Tasks: Combinatorial Optimization, Meta-Learning, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This article is a gentle discussion about the field of reinforcement learning in practice, about opportunities and challenges, touching a broad range of topics, with perspectives and without technical details. The article is based on both historical and recent research papers, surveys, tutorials, talks, blogs, books, (panel) discussions, and workshops/conferences. Various groups of readers, like researchers, engineers, students, managers, investors, officers, and people wanting to know more about the field, may find the article interesting. In this article, we first give a brief introduction to reinforcement learning (RL), and its relationship with deep learning, machine learning and AI. Then we discuss opportunities of RL, in particular, products and services, games, bandits, recommender systems, robotics, transportation, finance and economics, healthcare, education, combinatorial optimization, computer systems, and science and engineering. Then we discuss challenges, in particular, 1) foundation, 2) representation, 3) reward, 4) exploration, 5) model, simulation, planning, and benchmarks, 6) off-policy/offline learning, 7) learning to learn a.k.a. meta-learning, 8) explainability and interpretability, 9) constraints, 10) software development and deployment, 11) business perspectives, and 12) more challenges. We conclude with a discussion, attempting to answer: "Why has RL not been widely adopted in practice yet?" and "When is RL helpful?".
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints
Authors: Dhruv Shah, Sergey Levine
Date: 2022-02-23 00:00:00
Tasks: 3D Reconstruction, General Knowledge, Navigate

Robotic navigation has been approached as a problem of 3D reconstruction and planning, as well as an end-to-end learning problem. However, long-range navigation requires both planning and reasoning about local traversability, as well as being able to utilize general knowledge about global geography, in the form of a roadmap, GPS, or other side information providing important cues. In this work, we propose an approach that integrates learning and planning, and can utilize side information such as schematic roadmaps, satellite maps and GPS coordinates as a planning heuristic, without relying on them being accurate. Our method, ViKiNG, incorporates a local traversability model, which looks at the robot's current camera observation and a potential subgoal to infer how easily that subgoal can be reached, as well as a heuristic model, which looks at overhead maps for hints and attempts to evaluate the appropriateness of these subgoals in order to reach the goal. These models are used by a heuristic planner to identify the best waypoint in order to reach the final destination. Our method performs no explicit geometric reconstruction, utilizing only a topological representation of the environment. Despite having never seen trajectories longer than 80 meters in its training dataset, ViKiNG can leverage its image-based learned controller and goal-directed heuristic to navigate to goals up to 3 kilometers away in previously unseen environments, and exhibit complex behaviors such as probing potential paths and backtracking when they are found to be non-viable. ViKiNG is also robust to unreliable maps and GPS, since the low-level controller ultimately makes decisions based on egocentric image observations, using maps only as planning heuristics. For videos of our experiments, please check out our project page https://sites.google.com/view/viking-release.
NetRCA: An Effective Network Fault Cause Localization Algorithm
Authors: Chaoli Zhang, Zhiqiang Zhou, Yingying Zhang, Linxiao Yang, Kai He, Qingsong Wen, Liang Sun
Date: 2022-02-23 00:00:00
Tasks: Time Series, Time Series Analysis

Localizing the root cause of network faults is crucial to network operation and maintenance. However, due to the complicated network architectures and wireless environments, as well as limited labeled data, accurately localizing the true root cause is challenging. In this paper, we propose a novel algorithm named NetRCA to deal with this problem. Firstly, we extract effective derived features from the original raw data by considering temporal, directional, attribution, and interaction characteristics. Secondly, we adopt multivariate time series similarity and label propagation to generate new training data from both labeled and unlabeled data to overcome the lack of labeled samples. Thirdly, we design an ensemble model which combines XGBoost, rule set learning, attribution model, and graph algorithm, to fully utilize all data information and enhance performance. Finally, experiments and analysis are conducted on the real-world dataset from ICASSP 2022 AIOps Challenge to demonstrate the superiority and effectiveness of our approach.
Designing Decision Support Systems for Emergency Response: Challenges and Opportunities
Authors: Geoffrey Pettet, Hunter Baxter, Sayyed Mohsen Vazirizade, Hemant Purohit, Meiyi Ma, Ayan Mukhopadhyay, Abhishek Dubey
Date: 2022-02-23 00:00:00
Tasks: Management

Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities. In addition to responding to frequent incidents each day (about 240 million emergency medical services calls and over 5 million road accidents in the US each year), these systems also support response during natural hazards. Recently, there has been a consistent interest in building decision support and optimization tools that can help emergency responders provide more efficient and effective response. This includes a number of principled subsystems that implement early incident detection, incident likelihood forecasting and strategic resource allocation and dispatch policies. In this paper, we highlight the key challenges and provide an overview of the approach developed by our team in collaboration with our community partners.
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction: A Survey and Virtual Design Element Taxonomy
Authors: Michael Walker, Thao Phung, Tathagata Chakraborti, Tom Williams, Daniel Szafir
Date: 2022-02-23 00:00:00
Tasks: Mixed Reality

Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) has been gaining considerable attention in research in recent years. However, the HRI community lacks a set of shared terminology and framework for characterizing aspects of mixed reality interfaces, presenting serious problems for future research. Therefore, it is important to have a common set of terms and concepts that can be used to precisely describe and organize the diverse array of work being done within the field. In this paper, we present a novel taxonomic framework for different types of VAM-HRI interfaces, composed of four main categories of virtual design elements (VDEs). We present and justify our taxonomy and explain how its elements have been developed over the last 30 years as well as the current directions VAM-HRI is headed in the coming decade.