pwc-archive/papers-with-abstracts huggingface dataset:
Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGAN
Authors: Dongyeun Lee, Jae Young Lee, Doyeon Kim, Jaehyun Choi, Junmo Kim
Date: 2022-04-29 00:00:00
Tasks: Transfer Learning

Transfer learning of StyleGAN has recently shown great potential to solve diverse tasks, especially in domain translation. Previous methods utilized a source model by swapping or freezing weights during transfer learning, however, they have limitations on visual quality and controlling source features. In other words, they require additional models that are computationally demanding and have restricted control steps that prevent a smooth transition. In this paper, we propose a new approach to overcome these limitations. Instead of swapping or freezing, we introduce a simple feature matching loss to improve generation quality. In addition, to control the degree of source features, we train a target model with the proposed strategy, FixNoise, to preserve the source features only in a disentangled subspace of a target feature space. Owing to the disentangled feature space, our method can smoothly control the degree of the source features in a single model. Extensive experiments demonstrate that the proposed method can generate more consistent and realistic images than previous works.
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling
Authors: KiYoon Yoo, Nojun Kwak
Date: 2022-04-29 00:00:00
Tasks: Federated Learning, Model Poisoning, text-classification, Text Classification, Word Embeddings

Recent advances in federated learning have demonstrated its promising capability to learn on decentralized datasets. However, a considerable amount of work has raised concerns due to the potential risks of adversaries participating in the framework to poison the global model for an adversarial purpose. This paper investigates the feasibility of model poisoning for backdoor attacks through rare word embeddings of NLP models. In text classification, less than 1% of adversary clients suffices to manipulate the model output without any drop in the performance on clean sentences. For a less complex dataset, a mere 0.1% of adversary clients is enough to poison the global model effectively. We also propose a technique specialized in the federated learning scheme called Gradient Ensemble, which enhances the backdoor performance in all our experimental settings.
Local Explanation of Dimensionality Reduction
Authors: Avraam Bardos, Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
Date: 2022-04-29 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction, Explainable artificial intelligence, Interpretable Machine Learning

Dimensionality reduction (DR) is a popular method for preparing and analyzing high-dimensional data. Reduced data representations are less computationally intensive and easier to manage and visualize, while retaining a significant percentage of their original information. Aside from these advantages, these reduced representations can be difficult or impossible to interpret in most circumstances, especially when the DR approach does not provide further information about which features of the original space led to their construction. This problem is addressed by Interpretable Machine Learning, a subfield of Explainable Artificial Intelligence that addresses the opacity of machine learning models. However, current research on Interpretable Machine Learning has been focused on supervised tasks, leaving unsupervised tasks like Dimensionality Reduction unexplored. In this paper, we introduce LXDR, a technique capable of providing local interpretations of the output of DR techniques. Experiment results and two LXDR use case examples are presented to evaluate its usefulness.
Robust Solutions for Multi-Defender Stackelberg Security Games
Authors: Dolev Mutzari, Yonatan Aumann, Sarit Kraus
Date: 2022-04-29 00:00:00

Multi-defender Stackelberg Security Games (MSSG) have recently gained increasing attention in the literature. However, the solutions offered to date are highly sensitive, wherein even small perturbations in the attacker's utility or slight uncertainties thereof can dramatically change the defenders' resulting payoffs and alter the equilibrium. In this paper, we introduce a robust model for MSSGs, which admits solutions that are resistant to small perturbations or uncertainties in the game's parameters. First, we formally define the notion of robustness, as well as the robust MSSG model. Then, for the non-cooperative setting, we prove the existence of a robust approximate equilibrium in any such game, and provide an efficient construction thereof. For the cooperative setting, we show that any such game admits a robust approximate alpha-core, provide an efficient construction thereof, and prove that stronger types of the core may be empty. Interestingly, the robust solutions can substantially increase the defenders' utilities over those of the non-robust ones.
PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning
Authors: Linlin Chao, Xiexiong Lin, Taifeng Wang, Wei Chu
Date: 2022-04-29 00:00:00
Tasks: Entity Typing, Graph Embedding, Knowledge Graph Embedding, Link Prediction, Tensor Decomposition

Knowledge graph (KG) embedding methods which map entities and relations to unique embeddings in the KG have shown promising results on many reasoning tasks. However, the same embedding dimension for both dense entities and sparse entities will cause either over parameterization (sparse entities) or under fitting (dense entities). Normally, a large dimension is set to get better performance. Meanwhile, the inference time grows log-linearly with the number of entities for all entities are traversed and compared. Both the parameter and inference become challenges when working with huge amounts of entities. Thus, we propose PIE, a \textbf{p}arameter and \textbf{i}nference \textbf{e}fficient solution. Inspired from tensor decomposition methods, we find that decompose entity embedding matrix into low rank matrices can reduce more than half of the parameters while maintaining comparable performance. To accelerate model inference, we propose a self-supervised auxiliary task, which can be seen as fine-grained entity typing. By randomly masking and recovering entities' connected relations, the task learns the co-occurrence of entity and relations. Utilizing the fine grained typing, we can filter unrelated entities during inference and get targets with possibly sub-linear time requirement. Experiments on link prediction benchmarks demonstrate the proposed key capabilities. Moreover, we prove effectiveness of the proposed solution on the Open Graph Benchmark large scale challenge dataset WikiKG90Mv2 and achieve the state of the art performance.
User Experience Design for Automatic Credibility Assessment of News Content About COVID-19
Authors: Konstantin Schulz, Jens Rauenbusch, Jan Fillies, Lisa Rutenburg, Dimitrios Karvelas, Georg Rehm
Date: 2022-04-29 00:00:00

The increasingly rapid spread of information about COVID-19 on the web calls for automatic measures of quality assurance. In that context, we check the credibility of news content using selected linguistic features. We present two empirical studies to evaluate the usability of graphical interfaces that offer such credibility assessment. In a moderated qualitative interview with six participants, we identify rating scale, sub-criteria and algorithm authorship as important predictors of the usability. A subsequent quantitative online survey with 50 participants reveals a conflict between transparency and conciseness in the interface design, as well as a perceived hierarchy of metadata: the authorship of a news text is more important than the authorship of the credibility algorithm used to assess the content quality. Finally, we make suggestions for future research, such as proactively documenting credibility-related metadata for Natural Language Processing and Language Technology services and establishing an explicit hierarchical taxonomy of usability predictors for automatic credibility assessment.
KERMIT - A Transformer-Based Approach for Knowledge Graph Matching
Authors: Sven Hertling, Jan Portisch, Heiko Paulheim
Date: 2022-04-29 00:00:00
Tasks: Graph Matching, Knowledge Graphs, Sentence

One of the strongest signals for automated matching of knowledge graphs and ontologies are textual concept descriptions. With the rise of transformer-based language models, text comparison based on meaning (rather than lexical features) is available to researchers. However, performing pairwise comparisons of all textual descriptions of concepts in two knowledge graphs is expensive and scales quadratically (or even worse if concepts have more than one description). To overcome this problem, we follow a two-step approach: we first generate matching candidates using a pre-trained sentence transformer (so called bi-encoder). In a second step, we use fine-tuned transformer cross-encoders to generate the best candidates. We evaluate our approach on multiple datasets and show that it is feasible and produces competitive results.
Maxmin Participatory Budgeting
Authors: Gogulapati Sreedurga, Mayank Ratan Bhardwaj, Y. Narahari
Date: 2022-04-29 00:00:00
Tasks: Fairness

Participatory Budgeting (PB) is a popular voting method by which a limited budget is divided among a set of projects, based on the preferences of voters over the projects. PB is broadly categorised as divisible PB (if the projects are fractionally implementable) and indivisible PB (if the projects are atomic). Egalitarianism, an important objective in PB, has not received much attention in the context of indivisible PB. This paper addresses this gap through a detailed study of a natural egalitarian rule, Maxmin Participatory Budgeting (MPB), in the context of indivisible PB. Our study is in two parts: (1) computational (2) axiomatic. In the first part, we prove that MPB is computationally hard and give pseudo-polynomial time and polynomial-time algorithms when parameterized by certain well-motivated parameters. We propose an algorithm that achieves for MPB, additive approximation guarantees for restricted spaces of instances and empirically show that our algorithm in fact gives exact optimal solutions on real-world PB datasets. We also establish an upper bound on the approximation ratio achievable for MPB by the family of exhaustive strategy-proof PB algorithms. In the second part, we undertake an axiomatic study of the MPB rule by generalizing known axioms in the literature. Our study leads to the proposal of a new axiom, maximal coverage, which captures fairness aspects. We prove that MPB satisfies maximal coverage.
Vision-Language Pre-Training for Boosting Scene Text Detectors
Authors: Sibo Song, Jianqiang Wan, Zhibo Yang, Jun Tang, Wenqing Cheng, Xiang Bai, Cong Yao
Date: 2022-04-29 00:00:00
Proceeding: CVPR 2022 1
Tasks: Contrastive Learning, Language Modeling, Language Modelling, Masked Language Modeling, Representation Learning, Scene Text Detection, Text Detection

Recently, vision-language joint representation learning has proven to be highly effective in various scenarios. In this paper, we specifically adapt vision-language joint learning for scene text detection, a task that intrinsically involves cross-modal interaction between the two modalities: vision and language, since text is the written form of language. Concretely, we propose to learn contextualized, joint representations through vision-language pre-training, for the sake of enhancing the performance of scene text detectors. Towards this end, we devise a pre-training architecture with an image encoder, a text encoder and a cross-modal encoder, as well as three pretext tasks: image-text contrastive learning (ITC), masked language modeling (MLM) and word-in-image prediction (WIP). The pre-trained model is able to produce more informative representations with richer semantics, which could readily benefit existing scene text detectors (such as EAST and PSENet) in the down-stream text detection task. Extensive experiments on standard benchmarks demonstrate that the proposed paradigm can significantly improve the performance of various representative text detectors, outperforming previous pre-training approaches. The code and pre-trained models will be publicly released.
H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness
Authors: Xinyi Zhang, Cong Hao, Peipei Zhou, Alex Jones, Jingtong Hu
Date: 2022-04-29 00:00:00
Tasks: Multi-Task Learning

The complex nature of real-world problems calls for heterogeneity in both machine learning (ML) models and hardware systems. The heterogeneity in ML models comes from multi-sensor perceiving and multi-task learning, i.e., multi-modality multi-task (MMMT), resulting in diverse deep neural network (DNN) layers and computation patterns. The heterogeneity in systems comes from diverse processing components, as it becomes the prevailing method to integrate multiple dedicated accelerators into one system. Therefore, a new problem emerges: heterogeneous model to heterogeneous system mapping (H2H). While previous mapping algorithms mostly focus on efficient computations, in this work, we argue that it is indispensable to consider computation and communication simultaneously for better system efficiency. We propose a novel H2H mapping algorithm with both computation and communication awareness; by slightly trading computation for communication, the system overall latency and energy consumption can be largely reduced. The superior performance of our work is evaluated based on MAESTRO modeling, demonstrating 15%-74% latency reduction and 23%-64% energy reduction compared with existing computation-prioritized mapping algorithms.
Repro: An Open-Source Library for Improving the Reproducibility and Usability of Publicly Available Research Code
Authors: Daniel Deutsch, Dan Roth
Date: 2022-04-29 00:00:00

We introduce Repro, an open-source library which aims at improving the reproducibility and usability of research code. The library provides a lightweight Python API for running software released by researchers within Docker containers which contain the exact required runtime configuration and dependencies for the code. Because the environment setup for each package is handled by Docker, users do not have to do any configuration themselves. Once Repro is installed, users can run the code for the 30+ papers currently supported by the library. We hope researchers see the value provided to others by including their research code in Repro and consider adding support for their own research code.
CATNet: Cross-event Attention-based Time-aware Network for Medical Event Prediction
Authors: Sicen Liu, Xiaolong Wang, Yang Xiang, Hui Xu, Hui Wang, Buzhou Tang
Date: 2022-04-29 00:00:00
Tasks: Time Series Analysis

Medical event prediction (MEP) is a fundamental task in the medical domain, which needs to predict medical events, including medications, diagnosis codes, laboratory tests, procedures, outcomes, and so on, according to historical medical records. The task is challenging as medical data is a type of complex time series data with heterogeneous and temporal irregular characteristics. Many machine learning methods that consider the two characteristics have been proposed for medical event prediction. However, most of them consider the two characteristics separately and ignore the correlations among different types of medical events, especially relations between historical medical events and target medical events. In this paper, we propose a novel neural network based on attention mechanism, called cross-event attention-based time-aware network (CATNet), for medical event prediction. It is a time-aware, event-aware and task-adaptive method with the following advantages: 1) modeling heterogeneous information and temporal information in a unified way and considering temporal irregular characteristics locally and globally respectively, 2) taking full advantage of correlations among different types of events via cross-event attention. Experiments on two public datasets (MIMIC-III and eICU) show CATNet can be adaptive with different MEP tasks and outperforms other state-of-the-art methods on various MEP tasks. The source code of CATNet will be released after this manuscript is accepted.
A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI Inequality
Authors: Toby Jia-Jun Li, Yuwen Lu, Jaylexia Clark, Meng Chen, Victor Cox, Meng Jiang, Yang Yang, Tamara Kay, Danielle Wood, Jay Brockman
Date: 2022-04-29 00:00:00
Tasks: Position

The growing inequality in gig work between workers and platforms has become a critical social issue as gig work plays an increasingly prominent role in the future of work. The AI inequality is caused by (1) the technology divide in who has access to AI technologies in gig work; and (2) the data divide in who owns the data in gig work leads to unfair working conditions, growing pay gap, neglect of workers' diverse preferences, and workers' lack of trust in the platforms. In this position paper, we argue that a bottom-up approach that empowers individual workers to access AI-enabled work planning support and share data among a group of workers through a network of end-user-programmable intelligent assistants is a practical way to bridge AI inequality in gig work under the current paradigm of privately owned platforms. This position paper articulates a set of research challenges, potential approaches, and community engagement opportunities, seeking to start a dialogue on this important research topic in the interdisciplinary CHIWORK community.
Designing for Responsible Trust in AI Systems: A Communication Perspective
Authors: Q. Vera Liao, S. Shyam Sundar
Date: 2022-04-29 00:00:00

Current literature and public discourse on "trust in AI" are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how people develop trust. Given that AI systems differ in their level of trustworthiness, two open questions come to the fore: how should AI trustworthiness be responsibly communicated to ensure appropriate and equitable trust judgments by different users, and how can we protect users from deceptive attempts to earn their trust? We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH, which describes how trustworthiness is communicated in AI systems through trustworthiness cues and how those cues are processed by people to make trust judgments. Besides AI-generated content, we highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users. By bringing to light the variety of users' cognitive processes to make trust judgments and their potential limitations, we urge technology creators to make conscious decisions in choosing reliable trustworthiness cues for target users and, as an industry, to regulate this space and prevent malicious use. Towards these goals, we define the concepts of warranted trustworthiness cues and expensive trustworthiness cues, and propose a checklist of requirements to help technology creators identify appropriate cues to use. We present a hypothetical use case to illustrate how practitioners can use MATCH to design AI systems responsibly, and discuss future directions for research and industry efforts aimed at promoting responsible trust in AI.
Visualization and Optimization Techniques for High Dimensional Parameter Spaces
Authors: Anjul Tyagi
Date: 2022-04-28 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High dimensional parameter space optimization is crucial in many applications. The parameters affecting this performance can be both numerical and categorical in their type. The existing techniques of black-box optimization and visual analytics are good in dealing with numerical parameters but analyzing categorical variables in context of the numerical variables are not well studied. Hence, we propose a novel approach, to create an auto-tuning framework for storage systems optimization combining both direct optimization techniques and visual analytics research. While the optimization algorithm will be the core of the system, visual analytics will provide a guideline with the help of an external agent (expert) to provide crucial hints to narrow down the large search space for the optimization engine. As part of the initial step towards creating an auto-tuning engine for storage systems optimization, we created an Interactive Configuration Explorer \textit{ICE}, which directly addresses the need of analysts to learn how the dependent numerical variable is affected by the parameter settings given multiple optimization objectives. No information is lost as ICE shows the complete distribution and statistics of the dependent variable in context with each categorical variable. Analysts can interactively filter the variables to optimize for certain goals such as achieving a system with maximum performance, low variance, etc. Our system was developed in tight collaboration with a group of systems performance researchers and its final effectiveness was evaluated with expert interviews, a comparative user study, and two case studies. We also discuss our research plan for creating an efficient auto-tuning framework combining black-box optimization and visual analytics for storage systems performance optimization.
BILP-Q: Quantum Coalition Structure Generation
Authors: Supreeth Mysore Venkatesh, Antonio Macaluso, Matthias Klusch
Date: 2022-04-28 00:00:00
Tasks: Combinatorial Optimization

Quantum AI is an emerging field that uses quantum computing to solve typical complex problems in AI. In this work, we propose BILP-Q, the first-ever general quantum approach for solving the Coalition Structure Generation problem (CSGP), which is notably NP-hard. In particular, we reformulate the CSGP in terms of a Quadratic Binary Combinatorial Optimization (QUBO) problem to leverage existing quantum algorithms (e.g., QAOA) to obtain the best coalition structure. Thus, we perform a comparative analysis in terms of time complexity between the proposed quantum approach and the most popular classical baselines. Furthermore, we consider standard benchmark distributions for coalition values to test the BILP-Q on small-scale experiments using the IBM Qiskit environment. Finally, since QUBO problems can be solved operating with quantum annealing, we run BILP-Q on medium-size problems using a real quantum annealer (D-Wave).
Instilling Type Knowledge in Language Models via Multi-Task QA
Authors: Shuyang Li, Mukund Sridhar, Chandana Satya Prakash, Jin Cao, Wael Hamza, Julian McAuley
Date: 2022-04-28 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Articles, dialog state tracking, Knowledge Graphs, Vocal Bursts Type Prediction

Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge -- their types. Previous methods to learn entity types rely on training classifiers on datasets with coarse, noisy, and incomplete labels. We introduce a method to instill fine-grained type knowledge in language models with text-to-text pre-training on type-centric questions leveraging knowledge base documents and knowledge graphs. We create the WikiWiki dataset: entities and passages from 10M Wikipedia articles linked to the Wikidata knowledge graph with 41K types. Models trained on WikiWiki achieve state-of-the-art performance in zero-shot dialog state tracking benchmarks, accurately infer entity types in Wikipedia articles, and can discover new types deemed useful by human judges.