pwc-archive/papers-with-abstracts huggingface dataset:
What is Healthy? Generative Counterfactual Diffusion for Lesion Localization
Authors: Pedro Sanchez, Antanas Kascenas, Xiao Liu, Alison Q. O'Neil, Sotirios A. Tsaftaris
Date: 2022-07-25 00:00:00
Tasks: counterfactual, Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Reducing the requirement for densely annotated masks in medical image segmentation is important due to cost constraints. In this paper, we consider the problem of inferring pixel-level predictions of brain lesions by only using image-level labels for training. By leveraging recent advances in generative diffusion probabilistic models (DPM), we synthesize counterfactuals of "How would a patient appear if X pathology was not present?". The difference image between the observed patient state and the healthy counterfactual can be used for inferring the location of pathology. We generate counterfactuals that correspond to the minimal change of the input such that it is transformed to healthy domain. This requires training with healthy and unhealthy data in DPMs. We improve on previous counterfactual DPMs by manipulating the generation process with implicit guidance along with attention conditioning instead of using classifiers. Code is available at https://github.com/vios-s/Diff-SCM.
Equivariance and Invariance Inductive Bias for Learning from Insufficient Data
Authors: Tan Wang, Qianru Sun, Sugiri Pranata, Karlekar Jayashree, Hanwang Zhang
Date: 2022-07-25 00:00:00
Tasks: Inductive Bias

We are interested in learning robust models from insufficient data, without the need for any externally pre-trained checkpoints. First, compared to sufficient data, we show why insufficient data renders the model more easily biased to the limited training environments that are usually different from testing. For example, if all the training swan samples are "white", the model may wrongly use the "white" environment to represent the intrinsic class swan. Then, we justify that equivariance inductive bias can retain the class feature while invariance inductive bias can remove the environmental feature, leaving the class feature that generalizes to any environmental changes in testing. To impose them on learning, for equivariance, we demonstrate that any off-the-shelf contrastive-based self-supervised feature learning method can be deployed; for invariance, we propose a class-wise invariant risk minimization (IRM) that efficiently tackles the challenge of missing environmental annotation in conventional IRM. State-of-the-art experimental results on real-world benchmarks (VIPriors, ImageNet100 and NICO) validate the great potential of equivariance and invariance in data-efficient learning. The code is available at https://github.com/Wangt-CN/EqInv
Lifelong Machine Learning of Functionally Compositional Structures
Authors: Jorge A. Mendez
Date: 2022-07-25 00:00:00
Tasks: BIG-bench Machine Learning, Continual Learning, Lifelong learning, Reinforcement Learning (RL)

A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and reuse them in novel combinations for solving different problems. Learning such compositional structures has been a challenge for artificial systems, due to the underlying combinatorial search. To date, research into compositional learning has largely proceeded separately from work on lifelong or continual learning. This dissertation integrated these two lines of work to present a general-purpose framework for lifelong learning of functionally compositional structures. The framework separates the learning into two stages: learning how to combine existing components to assimilate a novel problem, and learning how to adapt the existing components to accommodate the new problem. This separation explicitly handles the trade-off between stability and flexibility. This dissertation instantiated the framework into various supervised and reinforcement learning (RL) algorithms. Supervised learning evaluations found that 1) compositional models improve lifelong learning of diverse tasks, 2) the multi-stage process permits lifelong learning of compositional knowledge, and 3) the components learned by the framework represent self-contained and reusable functions. Similar RL evaluations demonstrated that 1) algorithms under the framework accelerate the discovery of high-performing policies, and 2) these algorithms retain or improve performance on previously learned tasks. The dissertation extended one lifelong compositional RL algorithm to the nonstationary setting, where the task distribution varies over time, and found that modularity permits individually tracking changes to different elements in the environment. The final contribution of this dissertation was a new benchmark for compositional RL, which exposed that existing methods struggle to discover the compositional properties of the environment.
Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions
Authors: Tom Westermann, Nemanja Hranisavljevic, Alexander Fay
Date: 2022-07-25 00:00:00

The analysis of event data from production systems is the basis for many applications associated with Industry 4.0. However, heterogeneous and disjoint data is common in this domain. As a consequence, contextual information of an event might be incomplete or improperly interpreted which results in suboptimal analysis results. This paper proposes an approach to access a production systems' event data based on the event data's context (such as the product type, process type or process parameters). The approach extracts filtered event logs from a database system by combining: 1) a semantic model of a production system's hierarchical structure, 2) a formalized process description and 3) an OPC UA information model. As a proof of concept we demonstrate our approach using a sample server based on OPC UA for Machinery Companion Specifications.
Personality-Driven Social Multimedia Content Recommendation
Authors: Qi Yang, Sergey Nikolenko, Alfred Huang, Aleksandr Farseev
Date: 2022-07-25 00:00:00
Tasks: Marketing, Recommendation Systems

Social media marketing plays a vital role in promoting brand and product values to wide audiences. In order to boost their advertising revenues, global media buying platforms such as Facebook Ads constantly reduce the reach of branded organic posts, pushing brands to spend more on paid media ads. In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale. At the same time, various personality type categorization schemes such as the Myers-Briggs Personality Type indicator make it possible to reveal the dependencies between personality traits and user content preferences on a wider scale by categorizing audience behaviours in a unified and structured manner. This problem is yet to be studied in depth by the research community, while the level of impact of different personality traits on content recommendation accuracy has not been widely utilised and comprehensively evaluated so far. Specifically, in this work we investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system called Personality Content Marketing Recommender Engine, or PersiC. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations, which when deployed are able to improve digital advertising efficiency by over 420% as compared to the original human-guided approach.
Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View
Authors: Han-Dong Lim, Donghwan Lee
Date: 2022-07-25 00:00:00
Tasks: Q-Learning

Q-learning has long been one of the most popular reinforcement learning algorithms, and theoretical analysis of Q-learning has been an active research topic for decades. Although researches on asymptotic convergence analysis of Q-learning have a long tradition, non-asymptotic convergence has only recently come under active study. The main goal of this paper is to investigate new finite-time analysis of asynchronous Q-learning under Markovian observation models via a control system viewpoint. In particular, we introduce a discrete-time time-varying switching system model of Q-learning with diminishing step-sizes for our analysis, which significantly improves recent development of the switching system analysis with constant step-sizes, and leads to \(\mathcal{O}\left( \sqrt{\frac{\log k}{k}} \right)\) convergence rate that is comparable to or better than most of the state of the art results in the literature. In the mean while, a technique using the similarly transformation is newly applied to avoid the difficulty in the analysis posed by diminishing step-sizes. The proposed analysis brings in additional insights, covers different scenarios, and provides new simplified templates for analysis to deepen our understanding on Q-learning via its unique connection to discrete-time switching systems.
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
Authors: Hongzuo Xu, Yijie Wang, Songlei Jian, Qing Liao, Yongjun Wang, Guansong Pang
Date: 2022-07-25 00:00:00
Tasks: Anomaly Detection, One-Class Classification, One-class classifier, Time Series, Time Series Analysis, Time Series Anomaly Detection

Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration. Specifically, our approach adaptively penalizes uncertain predictions to restrain irregular samples in anomaly contamination during optimization, while simultaneously encouraging confident predictions on regular samples to ensure effective normality learning. This largely alleviates the negative impact of anomaly contamination. Our approach also creates native anomaly examples via perturbation to simulate time series abnormal behaviors. Through discriminating these dummy anomalies, our one-class learning is further calibrated to form a more precise normality boundary. Extensive experiments on ten real-world datasets show that our model achieves substantial improvement over sixteen state-of-the-art contenders.
Post-processing Networks: Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning
Authors: Atsumoto Ohashi, Ryuichiro Higashinaka
Date: 2022-07-25 00:00:00
Proceeding: SIGDIAL (ACL) 2022 9
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Task-Oriented Dialogue Systems

Many studies have proposed methods for optimizing the dialogue performance of an entire pipeline task-oriented dialogue system by jointly training modules in the system using reinforcement learning. However, these methods are limited in that they can only be applied to modules implemented using trainable neural-based methods. To solve this problem, we propose a method for optimizing a pipeline system composed of modules implemented with arbitrary methods for dialogue performance. With our method, neural-based components called post-processing networks (PPNs) are installed inside such a system to post-process the output of each module. All PPNs are updated to improve the overall dialogue performance of the system by using reinforcement learning, not necessitating each module to be differentiable. Through dialogue simulation and human evaluation on the MultiWOZ dataset, we show that our method can improve the dialogue performance of pipeline systems consisting of various modules.
How much of UCCA can be predicted from AMR?
Authors: Siyana Pavlova, Maxime Amblard, Bruno Guillaume
Date: 2022-07-25 00:00:00
Proceeding: ISA (LREC) 2022 6
Tasks: Abstract Meaning Representation

In this paper, we consider two of the currently popular semantic frameworks: Abstract Meaning Representation (AMR)a more abstract framework, and Universal Conceptual Cognitive Annotation (UCCA)-an anchored framework. We use a corpus-based approach to build two graph rewriting systems, a deterministic and a non-deterministic one, from the former to the latter framework. We present their evaluation and a number of ambiguities that we discovered while building our rules. Finally, we provide a discussion and some future work directions in relation to comparing semantic frameworks of different flavors.
Graph Querying for Semantic Annotations
Authors: Maxime Amblard, Bruno Guillaume, Siyana Pavlova, Guy Perrier
Date: 2022-07-25 00:00:00
Proceeding: ISA (LREC) 2022 6

This paper presents how the online tool GREW-MATCH can be used to make queries and visualise data from existing semantically annotated corpora. A dedicated syntax is available to construct simple to complex queries and execute them against a corpus. Such queries give transverse views of the annotated data, these views can help for checking the consistency of annotations in one corpus or across several corpora. GREW-MATCH can then be seen as an error mining tool: when inconsistencies are detected, it helps finding the sentences which should be fixed. Finally, GREW-MATCH can also be used as a side tool to assist annotation tasks helping to find annotation examples in existing corpora to be compared to the data to be annotated.
A Multi-Party Dialogue Ressource in French
Authors: Maria Boritchev, Maxime Amblard
Date: 2022-07-25 00:00:00
Proceeding: LREC 2022 6

We present Dialogues in Games (DinG), a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues between French-speaking players of the board game Catan. Our objective is to make available a quality resource for French, composed of long dialogues, to facilitate their study in the style of (Asher et al., 2016). In a general dialogue setting, participants share personal information, which makes it impossible to disseminate the resource freely and openly. In DinG, the attention of the participants is focused on the game, which prevents them from talking about themselves. In addition, we are conducting a study on the nature of the questions in dialogue, through annotation (Cruz Blandon et al., 2019), in order to develop more natural automatic dialogue systems.
Black-box Few-shot Knowledge Distillation
Authors: Dang Nguyen, Sunil Gupta, Kien Do, Svetha Venkatesh
Date: 2022-07-25 00:00:00
Tasks: image-classification, Image Classification, Knowledge Distillation

Knowledge distillation (KD) is an efficient approach to transfer the knowledge from a large "teacher" network to a smaller "student" network. Traditional KD methods require lots of labeled training samples and a white-box teacher (parameters are accessible) to train a good student. However, these resources are not always available in real-world applications. The distillation process often happens at an external party side where we do not have access to much data, and the teacher does not disclose its parameters due to security and privacy concerns. To overcome these challenges, we propose a black-box few-shot KD method to train the student with few unlabeled training samples and a black-box teacher. Our main idea is to expand the training set by generating a diverse set of out-of-distribution synthetic images using MixUp and a conditional variational auto-encoder. These synthetic images along with their labels obtained from the teacher are used to train the student. We conduct extensive experiments to show that our method significantly outperforms recent SOTA few/zero-shot KD methods on image classification tasks. The code and models are available at: https://github.com/nphdang/FS-BBT
Efficient Classification with Counterfactual Reasoning and Active Learning
Authors: Azhar Mohammed, Dang Nguyen, Bao Duong, Thin Nguyen
Date: 2022-07-25 00:00:00
Tasks: Active Learning, Classification, counterfactual, Counterfactual Reasoning, Data Augmentation

Data augmentation is one of the most successful techniques to improve the classification accuracy of machine learning models in computer vision. However, applying data augmentation to tabular data is a challenging problem since it is hard to generate synthetic samples with labels. In this paper, we propose an efficient classifier with a novel data augmentation technique for tabular data. Our method called CCRAL combines causal reasoning to learn counterfactual samples for the original training samples and active learning to select useful counterfactual samples based on a region of uncertainty. By doing this, our method can maximize our model's generalization on the unseen testing data. We validate our method analytically, and compare with the standard baselines. Our experimental results highlight that CCRAL achieves significantly better performance than those of the baselines across several real-world tabular datasets in terms of accuracy and AUC. Data and source code are available at: https://github.com/nphdang/CCRAL.
Multi-Scale Asset Distribution Model for Dynamic Environments
Authors: Payam Zahadat, Ada Diaconescu
Date: 2022-07-25 00:00:00
Tasks: model

In many self-organising systems the ability to extract necessary resources from the external environment is essential to the system's growth and survival. Examples include the extraction of sunlight and nutrients in organic plants, of monetary income in business organisations and of mobile robots in swarm intelligence actions. When operating within competitive, ever-changing environments, such systems must distribute their internal assets wisely so as to improve and adapt their ability to extract available resources. As the system size increases, the asset-distribution process often gets organised around a multi-scale control topology. This topology may be static (fixed) or dynamic (enabling growth and structural adaptation) depending on the system's internal constraints and adaptive mechanisms. In this paper, we expand on a plant-inspired asset-distribution model and introduce a more general multi-scale model applicable across a wider range of natural and artificial system domains. We study the impact that the topology of the multi-scale control process has upon the system's ability to self-adapt asset distribution when resource availability changes within the environment. Results show how different topological characteristics and different competition levels between system branches impact overall system profitability, adaptation delays and disturbances when environmental changes occur. These findings provide a basis for system designers to select the most suitable topology and configuration for their particular application and execution environment.
A Reference Data Model for Process-Related User Interaction Logs
Authors: Luka Abb, Jana-Rebecca Rehse
Date: 2022-07-25 00:00:00

User interaction (UI) logs are high-resolution event logs that record low-level activities performed by a user during the execution of a task in an information system. Each event in a UI log corresponds to a single interaction between the user and the interface, such as clicking a button or entering a string into a text field. UI logs are used for purposes like task mining or robotic process automation (RPA), but each study and tool relies on a different conceptualization and implementation of the elements and attributes that constitute user interactions. This lack of standardization makes it difficult to integrate UI logs from different sources and to combine tools for UI data collection with downstream analytics or automation solutions. To address this, we propose a universally applicable reference data model for process-related UI logs. Based on a review of scientific literature and industry solutions, this model includes the core attributes of UI logs, but remains flexible with regard to the scope, level of abstraction, and case notion. We provide an implementation of the model as an extension to the XES interchange standard for event logs and demonstrate its practical applicability in a real-life RPA scenario.
Designing an AI-Driven Talent Intelligence Solution: Exploring Big Data to extend the TOE Framework
Authors: Ali Faqihi, Shah J Miah
Date: 2022-07-25 00:00:00
Tasks: Management

AI has the potential to improve approaches to talent management enabling dynamic provisions through implementing advanced automation. This study aims to identify the new requirements for developing AI-oriented artifacts to address talent management issues. Focusing on enhancing interactions between professional assessment and planning attributes, the design artifact is an intelligent employment automation solution for career guidance that is largely dependent on a talent intelligent module and an individuals growth needs. A design science method is adopted for conducting the experimental study with structured machine learning techniques which is the primary element of a comprehensive AI solution framework informed through a proposed moderation of the technology-organization-environment theory.
Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective
Authors: Guangjing Huang, Xu Chen, Tao Ouyang, Qian Ma, Lin Chen, Junshan Zhang
Date: 2022-07-25 00:00:00
Tasks: Federated Learning

Federated learning (FL) is a promising distributed framework for collaborative artificial intelligence model training while protecting user privacy. A bootstrapping component that has attracted significant research attention is the design of incentive mechanism to stimulate user collaboration in FL. The majority of works adopt a broker-centric approach to help the central operator to attract participants and further obtain a well-trained model. Few works consider forging participant-centric collaboration among participants to pursue an FL model for their common interests, which induces dramatic differences in incentive mechanism design from the broker-centric FL. To coordinate the selfish and heterogeneous participants, we propose a novel analytic framework for incentivizing effective and efficient collaborations for participant-centric FL. Specifically, we respectively propose two novel game models for contribution-oblivious FL (COFL) and contribution-aware FL (CAFL), where the latter one implements a minimum contribution threshold mechanism. We further analyze the uniqueness and existence for Nash equilibrium of both COFL and CAFL games and design efficient algorithms to achieve equilibrium solutions. Extensive performance evaluations show that there exists free-riding phenomenon in COFL, which can be greatly alleviated through the adoption of CAFL model with the optimized minimum threshold.